This document discusses a machine learning classification analysis model to measure community satisfaction with traditional market facilities as a public service. It presents the results of applying fuzzy logic, multiple linear regression, artificial neural networks, and decision trees to analyze survey data from 203 respondents on their satisfaction with market traders, managers, facilities, hygiene, and safety. Fuzzy logic generated 32 patterns of classification rules. The artificial neural network model achieved 99.99% accuracy in determining satisfaction levels. Multiple linear regression showed a significant 81.1% correlation between variables and outcomes. Finally, the decision tree method described the knowledge base and analysis process visually.
Software Agents Role and Predictive Approaches for Online AuctionsIRJET Journal
1. Software agents can play a significant role in online auctions by making predictions and interacting with bidders autonomously with little human intervention.
2. A software bidding agent uses machine learning approaches like collecting auction data and defining features to forecast prices. It also considers characteristics like being autonomous, proactive, reactive, social, and intelligent.
3. Social media data from platforms like Twitter can be used alongside machine learning techniques to predict prices by analyzing sentiments in large, real-time, unstructured data streams generated by users.
A Novel Intelligence-based e-Procurement System to offer Maximum Fairness Ind...IJECEIAES
A perfect auction policy is one of the most strategic elements that contribute to success factor for any e-Procurement system. An auction policy can be only term as an effective if it really offer win-win situation to both the bidder as well as to the merchant. After reviewing existing studies on e-Procurement system, it is found that there isno effective research work focusing on this point and maximum research contribution has limited its scope to certain application or case studis. Hence, the proposed system introduces a novel eProcurement system which is equipped by an itelligence-building process for performing predictive analysis of ongoing auction process. A mathematical modelling is implemented where all teh variables have been formed using practical implementation of auction system and followed by optimization process using regression-based approach. The study outcome shows that proposed system offers better response time and higher predictive accuracy in contrast to existing approaches.
Machine learning based recommender system for e-commerceIAESIJAI
Nowadays, e-commerce is becoming an essential part of business for many reasons, including the simplicity, availability, richness and diversity of products and services, flexibility of payment methods and the convenience of shopping remotely without losing time. These benefits have greatly optimized the lives of users, especially with the technological development of mobile devices and the availability of the Internet anytime and anywhere. Because of their direct impact on the revenue of e-commerce companies, recommender systems are considered a must in this field. Recommender systems detect items that match the customer's needs based on the customer's previous actions and make them appear in an interesting way. Such a customized experience helps to increase customer engagement and purchase rates as the suggested items are tailored to the customer's interests. Therefore, perfecting recommendation systems that allow for more personalized and accurate item recommendations is a major challenge in the e-marketing world. In our study, we succeeded in developing an algorithm to suggest personal recommendations to customers using association rules via the Frequent-Pattern-Growth algorithm. Our technique generated good results with a high average probability of purchasing the next product suggested by the recommendation system.
DDS as a Crowd Management Systems Integration PlatformIRJET Journal
This document summarizes a literature review on crowd management systems and integration platforms. It finds that crowd management involves detecting crowds, collecting and analyzing crowd data, and using the analysis to control crowds. Effective integration is key to optimal system performance but current platforms are insufficient. The document advocates for adopting the Data Distribution Service middleware as the preferred integration platform due to its ability to meet the requirements of distributed, real-time crowd management systems.
Providing Accommodation and Food Services to the Population of the RegionYogeshIJTSRD
Analyzing development processes of each providing accommodation and food services to the population of the region, the sequence of choosing and modeling the main factors which influence their development are represented through simulation schemes in this article. Mukhitdinov Khudoyar Suyunovich | Rakhimov Abdihakim Muhammadiyevich "Providing Accommodation and Food Services to the Population of the Region" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Special Issue | International Research Development and Scientific Excellence in Academic Life , March 2021, URL: https://www.ijtsrd.com/papers/ijtsrd38500.pdf Paper Url: https://www.ijtsrd.com/economics/other/38500/providing-accommodation-and-food-services-to-the-population-of-the-region/mukhitdinov-khudoyar-suyunovich
Selection of the Best Proposal using FAHP: Case of Procurement of IT Master P...IJECEIAES
IT master plan, which allows planning and managing the development of the computer systems, derives its importance in the central role of the computer systems in the functioning of organizations. This article focuses on the use of FAHP method for analysis and evaluation of tenders during the awarding of contracts of IT master plan’s realization. For those purposes, a painstaking work was realized for making an inventory of criteria and sub-criteria involved in the evaluation of tenders and for specifying the degrees of preference for each pair of criteria and sub-criteria. To find a provider for the IT master plan’s realization, organizations are increasingly using tendering as the mode of awarding contracts. This paper is an improvement of a previous published paper in which AHP method was used. The goals of this work are to make available to members of tenders committee a decision support tool for evaluating tenders of IT master plan’s realization and endow the organizations with effective IT master plans in order to increase their information systems’ performance.
In the concept of predicting the town toward smart city using fuzzy Tsukamoto, several parameters need to be processed for the feasibility of a smart city. The process of calculating the predicted value of a city towards smart city based on the rules that are made of the value of membership and fuzzy domain. After the calculation, it will get the predicted value of the appropriateness of all town called the smart city. In the concept of predicting smart city, there are 19 rules are used. If every city has variable 1 is not feasible, the city can be called the city worth heading smart city. If there is a variable 2 found not feasible, then the city is still in need of improvement with so-called cities were not yet eligible to the Smart city, whereas if the variable 3 found not feasible, the city is not eligible to get the smart city.
Selecting User Influence on Twitter Data Using Skyline Query under MapReduce ...TELKOMNIKA JOURNAL
The aim of this research was to select and identify user influence on Twitter data. In identification stage, the method proposed in this study was matrix Twitter approach, sentiment analysis, and characterization of the opinion leader. The importan characteristics included external communication, accessibility, and innovation. Based on these characteristics and information from Twitter data through matrix Twitter and sentiment analysis, a algorithm of skyline query was constructed for the selection stage. Algorithm of skyline query selected user influence by comparing with other users according to values of each characteristic. Thus, user influence was indicated as user that was not influenced by other users in any combination of skyline objects. The use of MapReduce framework model in identification and selection stage, support whole operation where Twitter had big size data and rapid changes. The results in identification and selection of user influence exhibited that MapReduce framework minimized the execution time, whereas in parallel skyline query could reveal user influence on the data.
Software Agents Role and Predictive Approaches for Online AuctionsIRJET Journal
1. Software agents can play a significant role in online auctions by making predictions and interacting with bidders autonomously with little human intervention.
2. A software bidding agent uses machine learning approaches like collecting auction data and defining features to forecast prices. It also considers characteristics like being autonomous, proactive, reactive, social, and intelligent.
3. Social media data from platforms like Twitter can be used alongside machine learning techniques to predict prices by analyzing sentiments in large, real-time, unstructured data streams generated by users.
A Novel Intelligence-based e-Procurement System to offer Maximum Fairness Ind...IJECEIAES
A perfect auction policy is one of the most strategic elements that contribute to success factor for any e-Procurement system. An auction policy can be only term as an effective if it really offer win-win situation to both the bidder as well as to the merchant. After reviewing existing studies on e-Procurement system, it is found that there isno effective research work focusing on this point and maximum research contribution has limited its scope to certain application or case studis. Hence, the proposed system introduces a novel eProcurement system which is equipped by an itelligence-building process for performing predictive analysis of ongoing auction process. A mathematical modelling is implemented where all teh variables have been formed using practical implementation of auction system and followed by optimization process using regression-based approach. The study outcome shows that proposed system offers better response time and higher predictive accuracy in contrast to existing approaches.
Machine learning based recommender system for e-commerceIAESIJAI
Nowadays, e-commerce is becoming an essential part of business for many reasons, including the simplicity, availability, richness and diversity of products and services, flexibility of payment methods and the convenience of shopping remotely without losing time. These benefits have greatly optimized the lives of users, especially with the technological development of mobile devices and the availability of the Internet anytime and anywhere. Because of their direct impact on the revenue of e-commerce companies, recommender systems are considered a must in this field. Recommender systems detect items that match the customer's needs based on the customer's previous actions and make them appear in an interesting way. Such a customized experience helps to increase customer engagement and purchase rates as the suggested items are tailored to the customer's interests. Therefore, perfecting recommendation systems that allow for more personalized and accurate item recommendations is a major challenge in the e-marketing world. In our study, we succeeded in developing an algorithm to suggest personal recommendations to customers using association rules via the Frequent-Pattern-Growth algorithm. Our technique generated good results with a high average probability of purchasing the next product suggested by the recommendation system.
DDS as a Crowd Management Systems Integration PlatformIRJET Journal
This document summarizes a literature review on crowd management systems and integration platforms. It finds that crowd management involves detecting crowds, collecting and analyzing crowd data, and using the analysis to control crowds. Effective integration is key to optimal system performance but current platforms are insufficient. The document advocates for adopting the Data Distribution Service middleware as the preferred integration platform due to its ability to meet the requirements of distributed, real-time crowd management systems.
Providing Accommodation and Food Services to the Population of the RegionYogeshIJTSRD
Analyzing development processes of each providing accommodation and food services to the population of the region, the sequence of choosing and modeling the main factors which influence their development are represented through simulation schemes in this article. Mukhitdinov Khudoyar Suyunovich | Rakhimov Abdihakim Muhammadiyevich "Providing Accommodation and Food Services to the Population of the Region" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Special Issue | International Research Development and Scientific Excellence in Academic Life , March 2021, URL: https://www.ijtsrd.com/papers/ijtsrd38500.pdf Paper Url: https://www.ijtsrd.com/economics/other/38500/providing-accommodation-and-food-services-to-the-population-of-the-region/mukhitdinov-khudoyar-suyunovich
Selection of the Best Proposal using FAHP: Case of Procurement of IT Master P...IJECEIAES
IT master plan, which allows planning and managing the development of the computer systems, derives its importance in the central role of the computer systems in the functioning of organizations. This article focuses on the use of FAHP method for analysis and evaluation of tenders during the awarding of contracts of IT master plan’s realization. For those purposes, a painstaking work was realized for making an inventory of criteria and sub-criteria involved in the evaluation of tenders and for specifying the degrees of preference for each pair of criteria and sub-criteria. To find a provider for the IT master plan’s realization, organizations are increasingly using tendering as the mode of awarding contracts. This paper is an improvement of a previous published paper in which AHP method was used. The goals of this work are to make available to members of tenders committee a decision support tool for evaluating tenders of IT master plan’s realization and endow the organizations with effective IT master plans in order to increase their information systems’ performance.
In the concept of predicting the town toward smart city using fuzzy Tsukamoto, several parameters need to be processed for the feasibility of a smart city. The process of calculating the predicted value of a city towards smart city based on the rules that are made of the value of membership and fuzzy domain. After the calculation, it will get the predicted value of the appropriateness of all town called the smart city. In the concept of predicting smart city, there are 19 rules are used. If every city has variable 1 is not feasible, the city can be called the city worth heading smart city. If there is a variable 2 found not feasible, then the city is still in need of improvement with so-called cities were not yet eligible to the Smart city, whereas if the variable 3 found not feasible, the city is not eligible to get the smart city.
Selecting User Influence on Twitter Data Using Skyline Query under MapReduce ...TELKOMNIKA JOURNAL
The aim of this research was to select and identify user influence on Twitter data. In identification stage, the method proposed in this study was matrix Twitter approach, sentiment analysis, and characterization of the opinion leader. The importan characteristics included external communication, accessibility, and innovation. Based on these characteristics and information from Twitter data through matrix Twitter and sentiment analysis, a algorithm of skyline query was constructed for the selection stage. Algorithm of skyline query selected user influence by comparing with other users according to values of each characteristic. Thus, user influence was indicated as user that was not influenced by other users in any combination of skyline objects. The use of MapReduce framework model in identification and selection stage, support whole operation where Twitter had big size data and rapid changes. The results in identification and selection of user influence exhibited that MapReduce framework minimized the execution time, whereas in parallel skyline query could reveal user influence on the data.
Incentive mechanism design for citizen reporting application using Stackelber...IJECEIAES
The growing utilization of smartphones equipped with various sensors to collect and analyze information around us highlights a paradigm called mobile crowdsensing. To motivate citizens’ participation in crowdsensing and compensate them for their resources, it is necessary to incentivize the participants for their sensing service. There are several studies that used the Stackelberg game to model the incentive mechanism, however, those studies did not include a budget constraint for limited budget case. Another challenge is to optimize crowdsourcer (government) profit in conducting crowdsensing under the limited budget then allocates the budget to several regional working units that are responsible for the specific city problems. We propose an incentive mechanism for mobile crowdsensing based on several identified incentive parameters using the Stackelberg game model and applied the multi-objective optimization problem (MOOP) to the incentive model in which the participant reputation is taken into account. The evaluation of the proposed incentive model is performed through simulations. The simulation indicated that the result appropriately corresponds to the theoretical properties of the model.
IRJET - Automated Water Meter: Prediction of Bill for Water ConservationIRJET Journal
The document summarizes an approach for automated water meters that can help conserve water resources. It discusses how traditional manual water metering systems are labor intensive and prone to errors. Automated water meters using technologies like IoT and machine learning can help manage water resources more efficiently while reducing human intervention. The document then reviews different techniques proposed in previous research for implementing automated water metering systems, including using electronic interface modules, open source systems, convolutional neural networks for digit recognition, and systems that integrate meter reading, leakage detection, data processing and billing units.
Decision support systems, Supplier selection, Information systems, Boolean al...ijmpict
For organizations operating with number of products/services and number of suppliers, to select the right
supplier meeting all their requirements will be a challenging job. Such organizations need a good
decision support system to evaluate the suppliers effectively. Several decision support systems have been
reported to deal with complex selection process to decide the right supplier. Many mathematical models
have also been developed. This paper presents a new method, named as Bit Decision Making (BDM)
method, which treats such complex system of decision making as a collection and sequence of reasonable
number of meaningful and manageable sub-systems by identifying and processing the relevant decision
criteria in each sub-system. Help of Boolean logic and Boolean algebra is taken to assign binary digit
values to the selection criteria and generate mathematical equations that correlate the inputs to the output
at each stage of decision making. Each sub-system with its own mathematical model has been treated as a
standardized decision sub-system for that phase of making decision in evaluating suppliers. The sequence
and connectivity of the sub-systems along with their outputs finally lead to selection of the best supplier. A
real-world case of evaluation of information technology (IT) tenders has been dealt with for application of
the proposed method. The paper discusses in detail the theory, methodology, application and features of
the new method.
Presentasi INCITEST 2021-Contactless Transaction System in Traditional Market...IrawanAfrianto1
This document describes a contactless transaction system using QR codes and digital payments that was developed to support physical distancing during the COVID-19 pandemic in traditional markets in Indonesia. The system was developed through problem identification, research, system modeling, development, and testing. It allows traders and buyers in traditional markets to complete transactions quickly and remotely through a mobile app using QR codes and e-wallet payments. User testing found high acceptance rates of 86-89% among traders and buyers. The system can help digitalize transactions in traditional markets while reducing the spread of COVID-19, but further research is needed on implementation strategies and security.
Deep learning approach analysis model prediction and classification poverty s...IAESIJAI
The problem of poverty is a scourge for every developing country coupled with the economic crisis that occurred during the coronavirus disease (COVID-19) pandemic. The impact of these problems is felt directly by the people in Indonesia, especially in the Province of West Sumatra. This study aims to predict and classify the level of poverty status by developing an analytical model based on the deep learning (DL) approach. The methods used in this study include the K-means method, artificial neural network (ANN), and support vector machine (SVM). The analytical model will be optimized using the pearson correlation (PC) method to measure the accuracy of the analysis. The variable indicator uses the parameters of population (X1), poverty rate (X2), income (X3), and poverty percentage (X4). The results of the study present prediction and classification output with a validity level of accuracy of 99.8%. Based on these results, it can be concluded that the proposed DL analysis model can present an updated analytical model that is quite effective in carrying out the prediction and classification process. The research findings also contribute to the initial handling of the problem of poverty.
Providing Trade Services to the Population of the RegionYogeshIJTSRD
Analyzing the details of providing trade services to the population of the region of the service sector, the sequence of choosing and modeling the main factors which influence their development are represented through simulation schemes in this article. Mukhitdinov Khudoyar Suyunovich | Makhmatkulov Golibjon Kholmuminovich "Providing Trade Services to the Population of the Region" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Special Issue | International Research Development and Scientific Excellence in Academic Life , March 2021, URL: https://www.ijtsrd.com/papers/ijtsrd38499.pdf Paper Url: https://www.ijtsrd.com/economics/other/38499/providing-trade-services-to-the-population-of-the-region/mukhitdinov-khudoyar-suyunovich
Developing Sales Information System Application using Prototyping ModelEditor IJCATR
This research aimed to develop the system that be able to manage the sales transaction, so the transaction services will be
more quickly and efficiently. The system has developed using prototyping model, which have steps including: 1) communication and
initial data collection, 2) quick design, 3) formation of prototyping, 4) evaluation of prototyping, 5) repairing prototyping, and 6) the
final step is producing devices properly so it can used by user. The prototyping model intended to adjust the system in accordance with
its use later, made in stages so that the problems that arise will be immediately addressed. The results of this research is a software
which have consumer transaction services including the purchasing services, sale, inventory management, and report for management
needed purpose. Based on questionnaires given to 18 respondents obtained information on the evaluation system built, among others:
1) 88% strongly agree and 11% agree, the application can increase effectiveness and efficiently the organizations/enterprises; 2) 33%
strongly agree, 62 agree, and 5% not agree, the application can meet the needs of organization/enterprise
Developing Sales Information System Application using Prototyping ModelEditor IJCATR
This research aimed to develop the system that be able to manage the sales transaction, so the transaction services will be more quickly and efficiently. The system has developed using prototyping model, which have steps including: 1) communication and initial data collection, 2) quick design, 3) formation of prototyping, 4) evaluation of prototyping, 5) repairing prototyping, and 6) the final step is producing devices properly so it can used by user. The prototyping model intended to adjust the system in accordance with its use later, made in stages so that the problems that arise will be immediately addressed. The results of this research is a software which have consumer transaction services including the purchasing services, sale, inventory management, and report for management needed purpose. Based on questionnaires given to 18 respondents obtained information on the evaluation system built, among others: 1) 88% strongly agree and 11% agree, the application can increase effectiveness and efficiently the organizations/enterprises; 2) 33% strongly agree, 62 agree, and 5% not agree, the application can meet the needs of organization/enterprise.
This document discusses using a K-means clustering algorithm to classify municipal management in local governments in Peru based on 58 variables across different areas. The K-means algorithm grouped the municipalities into 4 clusters - Cluster 1 contained 32% of municipalities, Cluster 2 contained 8%, Cluster 3 contained 28%, and Cluster 4 contained 32%. The results provide a classification model that could help improve decision-making and provision of local public services in Peruvian municipalities.
This document discusses a product analyst advisor software that uses natural language processing techniques like sentiment analysis to analyze customer reviews and sentiments about products. It extracts reviews from various websites about a product being researched and processes the data to provide useful insights. The insights help users easily select the best available option. The system architecture involves scraping live data from websites, using deep learning algorithms to analyze reviews for sentiments, and displaying product insights. It uses BERT for sentiment analysis and frameworks like Django and ReactJS. Web scraping is used to extract review data for analysis and providing recommendations to users.
Identity Resolution across Different Social Networks using Similarity Analysisrahulmonikasharma
Today the Social Networking Sites have become very popular and are used by most of the people. This is because the Social Networking sites are playing different roles in different fields and facilitating the needs of its users from time to time. The most common purpose why people join in to these websites is to get connected with people and sharing information. An individual may be signed in on more than one Social Networking Site so identifying the same individual on different Social Networking sites is a task. To accomplish this task the proposed system uses the Similarity Analysis method on the available information details.
A NEW MULTI CRITERIA DECISION MAKING METHOD : APPROACH OF LOGARITHMIC CONCEPT...ijaia
The primary aim of the study is to introduce APLOCO method which is developed for the solution of multicriteria
decision making problems both theoretically and practically. In this context, application subject of
APLACO constitutes evaluation of investment potential of different cities in metropolitan status in Turkey.
The secondary purpose of the study is to identify the independent variables affecting the factories in the
operating phase and to estimate the effect levels of independent variables on the dependent variable in the
organized industrial zones (OIZs), whose mission is to reduce regional development disparities and to
mobilize local production dynamics. For this purpose, the effect levels of independent variables on dependent variables have been determined using the multilayer perceptron (MLP) method, which has a wide use in artificial neural networks (ANNs). The effect levels derived from MLP have been then used as
the weight levels of the decision criteria in APLOCO. The independent variables included in MLP are also
used as the decision criteria in APLOCO. According to the results obtained from APLOCO, Istanbul city is
the best alternative in term of the investment potential and other alternatives are Manisa, Denizli, Izmir, Kocaeli, Bursa, Ankara, Adana, and Antalya, respectively. Although APLOCO is used to solve the ranking problem in order to show application process in the paper, it can be employed easily in the solution of classification and selection problems. On the other hand, the
study also shows a rare example of the nested usage of APLOCO which is one of the methods of operation
research as well as MLP used in determination of weights.
An Extensible Web Mining Framework for Real KnowledgeIJEACS
With the emergence of Web 2.0 applications that bestow rich user experience and convenience without time and geographical restrictions, web usage logs became a goldmine to researchers across the globe. User behavior analysis in different domains based on web logs has its utility for enterprises to have strategic decision making. Business growth of enterprises depends on customer-centric approaches that need to know the knowledge of customer behavior to succeed. The rationale behind this is that customers have alternatives and there is intense competition. Therefore business community needs business intelligence to have expert decisions besides focusing customer relationship management. Many researchers contributed towards this end. However, the need for a comprehensive framework that caters to the needs of businesses to ascertain real needs of web users. This paper presents a framework named eXtensible Web Usage Mining Framework (XWUMF) for discovering actionable knowledge from web log data. The framework employs a hybrid approach that exploits fuzzy clustering methods and methods for user behavior analysis. Moreover the framework is extensible as it can accommodate new algorithms for fuzzy clustering and user behavior analysis. We proposed an algorithm known as Sequential Web Usage Miner (SWUM) for efficient mining of web usage patterns from different data sets. We built a prototype application to validate our framework. Our empirical results revealed that the framework helps in discovering actionable knowledge.
ESTIMATING THE EFFORT OF MOBILE APPLICATION DEVELOPMENTcsandit
The rise of the use of mobile technologies in the world, such as smartphones and tablets,
connected to mobile networks is changing old habits and creating new ways for the society to
access information and interact with computer systems. Thus, traditional information systems
are undergoing a process of adaptation to this new computing context. However, it is important
to note that the characteristics of this new context are different. There are new features and,
thereafter, new possibilities, as well as restrictions that did not exist before. Finally, the systems
developed for this environment have different requirements and characteristics than the
traditional information systems. For this reason, there is the need to reassess the current
knowledge about the processes of planning and building for the development of systems in this
new environment. One area in particular that demands such adaptation is software estimation.
The estimation processes, in general, are based on characteristics of the systems, trying to
quantify the complexity of implementing them. Hence, the main objective of this paper is to
present a proposal for an estimation model for mobile applications, as well as discuss the
applicability of traditional estimation models for the purpose of developing systems in the
context of mobile computing. Hence, the main objective of this paper is to present an effort
estimation model for mobile applications.
E-commerce online review for detecting influencing factors users perceptionjournalBEEI
To date, online shopping using e-commerce services becomes a trend. The emergence of e-commerce truly helps people to shop more effectively and efficiently. However, there are still some problems encountered in e-commerce, especially from the user perspective. This research aims to explore user review data, particularly on factors that influence user perception of e-commerce applications, classify, and identify potential solutions to finding problems in e-commerce applications. Data is grabbed using web scraping techniques and classified using proper machine learning, i.e., support vector machine (SVM). Text associations and fishbone analysis are performed based on the classified user review data. The results of this study show that the user satisfaction problem can be captured. Furthermore, various services that should be provided as a potential solution to experienced customers' problems or application users' perception problems can be generated. A detailed discussion of these findings is available in this article.
Ijcsit12REQUIREMENTS ENGINEERING OF A WEB PORTAL USING ORGANIZATIONAL SEMIOTI...ijcsit
The requirements of software are key elements that contribute to the quality and users satisfaction of the
final system. In this work, Requirements Engineering (RE) of web sites is presented using an organizational
semiotics perspective. They are shown as being part of an organization, with particular practices, rules
and views considering stakeholders several differences and opinions. The main contribution of this paper is
to relate an experience, from elicitation to validation, showing how organizational semiotics artifacts were
exploited in a collaborative and participatory way to RE of a web portal. A case study is described in order
to demonstrate the feasibility of using such artifacts to RE when we think about the system as being part of
a social organization.
Framework for opinion as a service on review data of customer using semantics...IJECEIAES
At opinion mining plays a significant role in representing the original and unbiased perception of the products/services. However, there are various challenges associated with performing an effective opinion mining in the present era of distributed computing system with dynamic behaviour of users. Existing approaches is more laborious towards extracting knowledge from the reviews of user which is further subjected to various rounds of operation with complex procedures. The proposed system addresses the problem by introducing a novel framework called as opinion-as-a-service which is meant for direct utilization of the extracted knowledge in most user friendly manner. The proposed system introduces a set of three sequential algorithm that performs aggregated of incoming stream of opinion data, performing indexing, followed by applying semantics for extracting knowledge. The study outcome shows that proposed system is better than existing system in mining performance.
City i-Tick: The android based mobile application for students’ attendance at...journalBEEI
This paper presents City i-Tick, the android based mobile application for students’ attendance at a university. In this study, we developed mobile application for lecturers to take students’ attendance in City University, Petaling Jaya. Managing students’ attendance during lecture periods has become a difficult challenge. The research objectives for this study are to identify user requirement for City i-Tick, to design and develop City i-Tick, and to demonstrate the prototype of City i-Tick. The study is a narrative participatory design and exploits Design Thinking as the research methodology. City i-Tick was successfully validated by 14 lecturers and System Usability Scale (SUS) was used to determine the findings of the study. We found that City i-Tick is effective for lecturers in taking attendance because it is easy to use, easy to learn, and the users feel confident when using this application.
A Survey on Recommendation System based on Knowledge Graph and Machine LearningIRJET Journal
This document provides an overview of recommendation systems based on knowledge graphs and machine learning. It first defines key concepts like recommendation systems, knowledge graphs, meta paths, and knowledge graph embedding. It then discusses standard recommendation approaches like content-based filtering, collaborative filtering, and hybrid filtering. The document focuses on knowledge graph-based recommendation systems, how they address issues with traditional approaches, and how machine learning can be used alongside knowledge graphs. It reviews several papers on using knowledge graphs for recommendations and proposes a comparative study. The document also outlines a proposed recommendation system and potential future research directions in the domain.
UI/UX integrated holistic monitoring of PAUD using the TCSD methodjournalBEEI
User interface (UI)/user experience (UX) is one part of the stages in the development of the system to produce interactive and attractive web-based application layouts so that it is easy to understand and use by users. In this research, a case study was conducted on early childhood in PAUD Kuntum Mekar. The design of the UI/UX model for holistic integrative PAUD monitoring becomes one of the solutions to help parents and teachers. The method used to design UI/UX is the task centered system design (TCSD) approach starting from the stages; 1) identification scope of use, 2) user centered requirement analysis, 3) design and scenario, and 4) walkthrough evaluate, the method used for system testing is user satisfaction, and heuristic usability. The purpose of this study is the UI/UX design with TCSD can provide valid data needs of each actor based on the assignment and design of the story board of the developed system. The results of this research are UI/UX model design for integrative holistic PAUD monitoring application.
Convolutional neural network with binary moth flame optimization for emotion ...IAESIJAI
Electroencephalograph (EEG) signals have the ability of real-time reflecting brain activities. Utilizing the EEG signal for analyzing human emotional states is a common study. The EEG signals of the emotions aren’t distinctive and it is different from one person to another as every one of them has different emotional responses to same stimuli. Which is why, the signals of the EEG are subject dependent and proven to be effective for the subject dependent detection of the Emotions. For the purpose of achieving enhanced accuracy and high true positive rate, the suggested system proposed a binary moth flame optimization (BMFO) algorithm for the process of feature selection and convolutional neural networks (CNNs) for classifications. In this proposal, optimum features are chosen with the use of accuracy as objective function. Ultimately, optimally chosen features are classified after that with the use of a CNN for the purpose of discriminating different emotion states.
A novel ensemble model for detecting fake newsIAESIJAI
Due the growing proliferation of fake news over the past couple of years, our objective in this paper is to propose an ensemble model for the automatic classification of article news as being either real or fake. For this purpose, we opt for a blending technique that combines three models, namely bidirectional long short-term memory (Bi-LSTM), stochastic gradient descent classifier and ridge classifier. The implementation of the proposed model (i.e. BI-LSR) on real world datasets, has shown outstanding results. In fact, it achieved an accuracy score of 99.16%. Accordingly, this ensemble learning has proven to do perform better than individual conventional machine learning and deep learning models as well as many ensemble learning approaches cited in the literature.
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The growing utilization of smartphones equipped with various sensors to collect and analyze information around us highlights a paradigm called mobile crowdsensing. To motivate citizens’ participation in crowdsensing and compensate them for their resources, it is necessary to incentivize the participants for their sensing service. There are several studies that used the Stackelberg game to model the incentive mechanism, however, those studies did not include a budget constraint for limited budget case. Another challenge is to optimize crowdsourcer (government) profit in conducting crowdsensing under the limited budget then allocates the budget to several regional working units that are responsible for the specific city problems. We propose an incentive mechanism for mobile crowdsensing based on several identified incentive parameters using the Stackelberg game model and applied the multi-objective optimization problem (MOOP) to the incentive model in which the participant reputation is taken into account. The evaluation of the proposed incentive model is performed through simulations. The simulation indicated that the result appropriately corresponds to the theoretical properties of the model.
IRJET - Automated Water Meter: Prediction of Bill for Water ConservationIRJET Journal
The document summarizes an approach for automated water meters that can help conserve water resources. It discusses how traditional manual water metering systems are labor intensive and prone to errors. Automated water meters using technologies like IoT and machine learning can help manage water resources more efficiently while reducing human intervention. The document then reviews different techniques proposed in previous research for implementing automated water metering systems, including using electronic interface modules, open source systems, convolutional neural networks for digit recognition, and systems that integrate meter reading, leakage detection, data processing and billing units.
Decision support systems, Supplier selection, Information systems, Boolean al...ijmpict
For organizations operating with number of products/services and number of suppliers, to select the right
supplier meeting all their requirements will be a challenging job. Such organizations need a good
decision support system to evaluate the suppliers effectively. Several decision support systems have been
reported to deal with complex selection process to decide the right supplier. Many mathematical models
have also been developed. This paper presents a new method, named as Bit Decision Making (BDM)
method, which treats such complex system of decision making as a collection and sequence of reasonable
number of meaningful and manageable sub-systems by identifying and processing the relevant decision
criteria in each sub-system. Help of Boolean logic and Boolean algebra is taken to assign binary digit
values to the selection criteria and generate mathematical equations that correlate the inputs to the output
at each stage of decision making. Each sub-system with its own mathematical model has been treated as a
standardized decision sub-system for that phase of making decision in evaluating suppliers. The sequence
and connectivity of the sub-systems along with their outputs finally lead to selection of the best supplier. A
real-world case of evaluation of information technology (IT) tenders has been dealt with for application of
the proposed method. The paper discusses in detail the theory, methodology, application and features of
the new method.
Presentasi INCITEST 2021-Contactless Transaction System in Traditional Market...IrawanAfrianto1
This document describes a contactless transaction system using QR codes and digital payments that was developed to support physical distancing during the COVID-19 pandemic in traditional markets in Indonesia. The system was developed through problem identification, research, system modeling, development, and testing. It allows traders and buyers in traditional markets to complete transactions quickly and remotely through a mobile app using QR codes and e-wallet payments. User testing found high acceptance rates of 86-89% among traders and buyers. The system can help digitalize transactions in traditional markets while reducing the spread of COVID-19, but further research is needed on implementation strategies and security.
Deep learning approach analysis model prediction and classification poverty s...IAESIJAI
The problem of poverty is a scourge for every developing country coupled with the economic crisis that occurred during the coronavirus disease (COVID-19) pandemic. The impact of these problems is felt directly by the people in Indonesia, especially in the Province of West Sumatra. This study aims to predict and classify the level of poverty status by developing an analytical model based on the deep learning (DL) approach. The methods used in this study include the K-means method, artificial neural network (ANN), and support vector machine (SVM). The analytical model will be optimized using the pearson correlation (PC) method to measure the accuracy of the analysis. The variable indicator uses the parameters of population (X1), poverty rate (X2), income (X3), and poverty percentage (X4). The results of the study present prediction and classification output with a validity level of accuracy of 99.8%. Based on these results, it can be concluded that the proposed DL analysis model can present an updated analytical model that is quite effective in carrying out the prediction and classification process. The research findings also contribute to the initial handling of the problem of poverty.
Providing Trade Services to the Population of the RegionYogeshIJTSRD
Analyzing the details of providing trade services to the population of the region of the service sector, the sequence of choosing and modeling the main factors which influence their development are represented through simulation schemes in this article. Mukhitdinov Khudoyar Suyunovich | Makhmatkulov Golibjon Kholmuminovich "Providing Trade Services to the Population of the Region" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Special Issue | International Research Development and Scientific Excellence in Academic Life , March 2021, URL: https://www.ijtsrd.com/papers/ijtsrd38499.pdf Paper Url: https://www.ijtsrd.com/economics/other/38499/providing-trade-services-to-the-population-of-the-region/mukhitdinov-khudoyar-suyunovich
Developing Sales Information System Application using Prototyping ModelEditor IJCATR
This research aimed to develop the system that be able to manage the sales transaction, so the transaction services will be
more quickly and efficiently. The system has developed using prototyping model, which have steps including: 1) communication and
initial data collection, 2) quick design, 3) formation of prototyping, 4) evaluation of prototyping, 5) repairing prototyping, and 6) the
final step is producing devices properly so it can used by user. The prototyping model intended to adjust the system in accordance with
its use later, made in stages so that the problems that arise will be immediately addressed. The results of this research is a software
which have consumer transaction services including the purchasing services, sale, inventory management, and report for management
needed purpose. Based on questionnaires given to 18 respondents obtained information on the evaluation system built, among others:
1) 88% strongly agree and 11% agree, the application can increase effectiveness and efficiently the organizations/enterprises; 2) 33%
strongly agree, 62 agree, and 5% not agree, the application can meet the needs of organization/enterprise
Developing Sales Information System Application using Prototyping ModelEditor IJCATR
This research aimed to develop the system that be able to manage the sales transaction, so the transaction services will be more quickly and efficiently. The system has developed using prototyping model, which have steps including: 1) communication and initial data collection, 2) quick design, 3) formation of prototyping, 4) evaluation of prototyping, 5) repairing prototyping, and 6) the final step is producing devices properly so it can used by user. The prototyping model intended to adjust the system in accordance with its use later, made in stages so that the problems that arise will be immediately addressed. The results of this research is a software which have consumer transaction services including the purchasing services, sale, inventory management, and report for management needed purpose. Based on questionnaires given to 18 respondents obtained information on the evaluation system built, among others: 1) 88% strongly agree and 11% agree, the application can increase effectiveness and efficiently the organizations/enterprises; 2) 33% strongly agree, 62 agree, and 5% not agree, the application can meet the needs of organization/enterprise.
This document discusses using a K-means clustering algorithm to classify municipal management in local governments in Peru based on 58 variables across different areas. The K-means algorithm grouped the municipalities into 4 clusters - Cluster 1 contained 32% of municipalities, Cluster 2 contained 8%, Cluster 3 contained 28%, and Cluster 4 contained 32%. The results provide a classification model that could help improve decision-making and provision of local public services in Peruvian municipalities.
This document discusses a product analyst advisor software that uses natural language processing techniques like sentiment analysis to analyze customer reviews and sentiments about products. It extracts reviews from various websites about a product being researched and processes the data to provide useful insights. The insights help users easily select the best available option. The system architecture involves scraping live data from websites, using deep learning algorithms to analyze reviews for sentiments, and displaying product insights. It uses BERT for sentiment analysis and frameworks like Django and ReactJS. Web scraping is used to extract review data for analysis and providing recommendations to users.
Identity Resolution across Different Social Networks using Similarity Analysisrahulmonikasharma
Today the Social Networking Sites have become very popular and are used by most of the people. This is because the Social Networking sites are playing different roles in different fields and facilitating the needs of its users from time to time. The most common purpose why people join in to these websites is to get connected with people and sharing information. An individual may be signed in on more than one Social Networking Site so identifying the same individual on different Social Networking sites is a task. To accomplish this task the proposed system uses the Similarity Analysis method on the available information details.
A NEW MULTI CRITERIA DECISION MAKING METHOD : APPROACH OF LOGARITHMIC CONCEPT...ijaia
The primary aim of the study is to introduce APLOCO method which is developed for the solution of multicriteria
decision making problems both theoretically and practically. In this context, application subject of
APLACO constitutes evaluation of investment potential of different cities in metropolitan status in Turkey.
The secondary purpose of the study is to identify the independent variables affecting the factories in the
operating phase and to estimate the effect levels of independent variables on the dependent variable in the
organized industrial zones (OIZs), whose mission is to reduce regional development disparities and to
mobilize local production dynamics. For this purpose, the effect levels of independent variables on dependent variables have been determined using the multilayer perceptron (MLP) method, which has a wide use in artificial neural networks (ANNs). The effect levels derived from MLP have been then used as
the weight levels of the decision criteria in APLOCO. The independent variables included in MLP are also
used as the decision criteria in APLOCO. According to the results obtained from APLOCO, Istanbul city is
the best alternative in term of the investment potential and other alternatives are Manisa, Denizli, Izmir, Kocaeli, Bursa, Ankara, Adana, and Antalya, respectively. Although APLOCO is used to solve the ranking problem in order to show application process in the paper, it can be employed easily in the solution of classification and selection problems. On the other hand, the
study also shows a rare example of the nested usage of APLOCO which is one of the methods of operation
research as well as MLP used in determination of weights.
An Extensible Web Mining Framework for Real KnowledgeIJEACS
With the emergence of Web 2.0 applications that bestow rich user experience and convenience without time and geographical restrictions, web usage logs became a goldmine to researchers across the globe. User behavior analysis in different domains based on web logs has its utility for enterprises to have strategic decision making. Business growth of enterprises depends on customer-centric approaches that need to know the knowledge of customer behavior to succeed. The rationale behind this is that customers have alternatives and there is intense competition. Therefore business community needs business intelligence to have expert decisions besides focusing customer relationship management. Many researchers contributed towards this end. However, the need for a comprehensive framework that caters to the needs of businesses to ascertain real needs of web users. This paper presents a framework named eXtensible Web Usage Mining Framework (XWUMF) for discovering actionable knowledge from web log data. The framework employs a hybrid approach that exploits fuzzy clustering methods and methods for user behavior analysis. Moreover the framework is extensible as it can accommodate new algorithms for fuzzy clustering and user behavior analysis. We proposed an algorithm known as Sequential Web Usage Miner (SWUM) for efficient mining of web usage patterns from different data sets. We built a prototype application to validate our framework. Our empirical results revealed that the framework helps in discovering actionable knowledge.
ESTIMATING THE EFFORT OF MOBILE APPLICATION DEVELOPMENTcsandit
The rise of the use of mobile technologies in the world, such as smartphones and tablets,
connected to mobile networks is changing old habits and creating new ways for the society to
access information and interact with computer systems. Thus, traditional information systems
are undergoing a process of adaptation to this new computing context. However, it is important
to note that the characteristics of this new context are different. There are new features and,
thereafter, new possibilities, as well as restrictions that did not exist before. Finally, the systems
developed for this environment have different requirements and characteristics than the
traditional information systems. For this reason, there is the need to reassess the current
knowledge about the processes of planning and building for the development of systems in this
new environment. One area in particular that demands such adaptation is software estimation.
The estimation processes, in general, are based on characteristics of the systems, trying to
quantify the complexity of implementing them. Hence, the main objective of this paper is to
present a proposal for an estimation model for mobile applications, as well as discuss the
applicability of traditional estimation models for the purpose of developing systems in the
context of mobile computing. Hence, the main objective of this paper is to present an effort
estimation model for mobile applications.
E-commerce online review for detecting influencing factors users perceptionjournalBEEI
To date, online shopping using e-commerce services becomes a trend. The emergence of e-commerce truly helps people to shop more effectively and efficiently. However, there are still some problems encountered in e-commerce, especially from the user perspective. This research aims to explore user review data, particularly on factors that influence user perception of e-commerce applications, classify, and identify potential solutions to finding problems in e-commerce applications. Data is grabbed using web scraping techniques and classified using proper machine learning, i.e., support vector machine (SVM). Text associations and fishbone analysis are performed based on the classified user review data. The results of this study show that the user satisfaction problem can be captured. Furthermore, various services that should be provided as a potential solution to experienced customers' problems or application users' perception problems can be generated. A detailed discussion of these findings is available in this article.
Ijcsit12REQUIREMENTS ENGINEERING OF A WEB PORTAL USING ORGANIZATIONAL SEMIOTI...ijcsit
The requirements of software are key elements that contribute to the quality and users satisfaction of the
final system. In this work, Requirements Engineering (RE) of web sites is presented using an organizational
semiotics perspective. They are shown as being part of an organization, with particular practices, rules
and views considering stakeholders several differences and opinions. The main contribution of this paper is
to relate an experience, from elicitation to validation, showing how organizational semiotics artifacts were
exploited in a collaborative and participatory way to RE of a web portal. A case study is described in order
to demonstrate the feasibility of using such artifacts to RE when we think about the system as being part of
a social organization.
Framework for opinion as a service on review data of customer using semantics...IJECEIAES
At opinion mining plays a significant role in representing the original and unbiased perception of the products/services. However, there are various challenges associated with performing an effective opinion mining in the present era of distributed computing system with dynamic behaviour of users. Existing approaches is more laborious towards extracting knowledge from the reviews of user which is further subjected to various rounds of operation with complex procedures. The proposed system addresses the problem by introducing a novel framework called as opinion-as-a-service which is meant for direct utilization of the extracted knowledge in most user friendly manner. The proposed system introduces a set of three sequential algorithm that performs aggregated of incoming stream of opinion data, performing indexing, followed by applying semantics for extracting knowledge. The study outcome shows that proposed system is better than existing system in mining performance.
City i-Tick: The android based mobile application for students’ attendance at...journalBEEI
This paper presents City i-Tick, the android based mobile application for students’ attendance at a university. In this study, we developed mobile application for lecturers to take students’ attendance in City University, Petaling Jaya. Managing students’ attendance during lecture periods has become a difficult challenge. The research objectives for this study are to identify user requirement for City i-Tick, to design and develop City i-Tick, and to demonstrate the prototype of City i-Tick. The study is a narrative participatory design and exploits Design Thinking as the research methodology. City i-Tick was successfully validated by 14 lecturers and System Usability Scale (SUS) was used to determine the findings of the study. We found that City i-Tick is effective for lecturers in taking attendance because it is easy to use, easy to learn, and the users feel confident when using this application.
A Survey on Recommendation System based on Knowledge Graph and Machine LearningIRJET Journal
This document provides an overview of recommendation systems based on knowledge graphs and machine learning. It first defines key concepts like recommendation systems, knowledge graphs, meta paths, and knowledge graph embedding. It then discusses standard recommendation approaches like content-based filtering, collaborative filtering, and hybrid filtering. The document focuses on knowledge graph-based recommendation systems, how they address issues with traditional approaches, and how machine learning can be used alongside knowledge graphs. It reviews several papers on using knowledge graphs for recommendations and proposes a comparative study. The document also outlines a proposed recommendation system and potential future research directions in the domain.
UI/UX integrated holistic monitoring of PAUD using the TCSD methodjournalBEEI
User interface (UI)/user experience (UX) is one part of the stages in the development of the system to produce interactive and attractive web-based application layouts so that it is easy to understand and use by users. In this research, a case study was conducted on early childhood in PAUD Kuntum Mekar. The design of the UI/UX model for holistic integrative PAUD monitoring becomes one of the solutions to help parents and teachers. The method used to design UI/UX is the task centered system design (TCSD) approach starting from the stages; 1) identification scope of use, 2) user centered requirement analysis, 3) design and scenario, and 4) walkthrough evaluate, the method used for system testing is user satisfaction, and heuristic usability. The purpose of this study is the UI/UX design with TCSD can provide valid data needs of each actor based on the assignment and design of the story board of the developed system. The results of this research are UI/UX model design for integrative holistic PAUD monitoring application.
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Convolutional neural network with binary moth flame optimization for emotion ...IAESIJAI
Electroencephalograph (EEG) signals have the ability of real-time reflecting brain activities. Utilizing the EEG signal for analyzing human emotional states is a common study. The EEG signals of the emotions aren’t distinctive and it is different from one person to another as every one of them has different emotional responses to same stimuli. Which is why, the signals of the EEG are subject dependent and proven to be effective for the subject dependent detection of the Emotions. For the purpose of achieving enhanced accuracy and high true positive rate, the suggested system proposed a binary moth flame optimization (BMFO) algorithm for the process of feature selection and convolutional neural networks (CNNs) for classifications. In this proposal, optimum features are chosen with the use of accuracy as objective function. Ultimately, optimally chosen features are classified after that with the use of a CNN for the purpose of discriminating different emotion states.
A novel ensemble model for detecting fake newsIAESIJAI
Due the growing proliferation of fake news over the past couple of years, our objective in this paper is to propose an ensemble model for the automatic classification of article news as being either real or fake. For this purpose, we opt for a blending technique that combines three models, namely bidirectional long short-term memory (Bi-LSTM), stochastic gradient descent classifier and ridge classifier. The implementation of the proposed model (i.e. BI-LSR) on real world datasets, has shown outstanding results. In fact, it achieved an accuracy score of 99.16%. Accordingly, this ensemble learning has proven to do perform better than individual conventional machine learning and deep learning models as well as many ensemble learning approaches cited in the literature.
K-centroid convergence clustering identification in one-label per type for di...IAESIJAI
Disease prediction is a high demand field which requires significant support from machine learning (ML) to enhance the result efficiency. The research works on application of K-means clustering supervised classification in disease prediction where each class only has one labeled data. The K-centroid convergence clustering identification (KC3 I) system is based on semi-K-means clustering but only requires single labeled data per class for the training process with the training dataset to update the centroid. The KC3 I model also includes a dictionary box to index all the input centroids before and after the updating process. Each centroid matches with a corresponding label inside this box. After the training process, each time the input features arrive, the trained centroid will put them to its cluster depending on the Euclidean distance, then convert them into the specific class name, which is coherent to that centroid index. Two validation stages were carried out and accomplished the expectation in terms of precision, recall, F1-score, and absolute accuracy. The last part demonstrates the possibility of feature reduction by selecting the most crucial feature with the extra tree classifier method. Total data are fed into the KC3 I system with the most important features and remain the same accuracy.
Plant leaf detection through machine learning based image classification appr...IAESIJAI
Since maize is a staple diet for people, especially vegetarians and vegans, maize leaf disease has a significant influence here on the food industry including maize crop productivity. Therefore, it should be understood that maize quality must be optimal; yet, to do so, maize must be safeguarded from several illnesses. As a result, there is a great demand for such an automated system that can identify the condition early on and take the appropriate action. Early disease identification is crucial, but it also poses a major obstacle. As a result, in this research project, we adopt the fundamental k-nearest neighbor (KNN) model and concentrate on building and developing the enhanced k-nearest neighbor (EKNN) model. EKNN aids in identifying several classes of disease. To gather discriminative, boundary, pattern, and structurally linked information, additional high-quality fine and coarse features are generated. This information is then used in the classification process. The classification algorithm offers high-quality gradient-based features. Additionally, the proposed model is assessed using the Plant-Village dataset, and a comparison with many standard classification models using various metrics is also done.
Backbone search for object detection for applications in intrusion warning sy...IAESIJAI
In this work, we propose a novel backbone search method for object detection for applications in intrusion warning systems. The goal is to find a compact model for use in embedded thermal imaging cameras widely used in intrusion warning systems. The proposed method is based on faster region-based convolutional neural network (Faster R-CNN) because it can detect small objects. Inspired by EfficientNet, the sought-after backbone architecture is obtained by finding the most suitable width scale for the base backbone (ResNet50). The evaluation metrics are mean average precision (mAP), number of parameters, and number of multiply–accumulate operations (MACs). The experimental results showed that the proposed method is effective in building a lightweight neural network for the task of object detection. The obtained model can keep the predefined mAP while minimizing the number of parameters and computational resources. All experiments are executed elaborately on the person detection in intrusion warning systems (PDIWS) dataset.
Deep learning method for lung cancer identification and classificationIAESIJAI
Lung cancer (LC) is calming many lives and is becoming a serious cause of concern. The detection of LC at an early stage assists the chances of recovery. Accuracy of detection of LC at an early stage can be improved with the help of a convolutional neural network (CNN) based deep learning approach. In this paper, we present two methodologies for Lung cancer detection (LCD) applied on Lung image database consortium (LIDC) and image database resource initiative (IDRI) data sets. Classification of these LC images is carried out using support vector machine (SVM), and deep CNN. The CNN is trained with i) multiple batches and ii) single batch for LC image classification as non cancer and cancer image. All these methods are being implemented in MATLAB. The accuracy of classification obtained by SVM is 65%, whereas deep CNN produced detection accuracy of 80% and 100% respectively for multiple and single batch training. The novelty of our experimentation is near 100% classification accuracy obtained by our deep CNN model when tested on 25 Lung computed tomography (CT) test images each of size 512×512 pixels in less than 20 iterations as compared to the research work carried out by other researchers using cropped LC nodule images.
Optically processed Kannada script realization with Siamese neural network modelIAESIJAI
Optical character recognition (OCR) is a technology that allows computers to recognize and extract text from images or scanned documents. It is commonly used to convert printed or handwritten text into machine-readable format. This Study presents an OCR system on Kannada Characters based on siamese neural network (SNN). Here the SNN, a Deep neural network which comprises of two identical convolutional neural network (CNN) compare the script and ranks based on the dissimilarity. When lesser dissimilarity score is identified, prediction is done as character match. In this work the authors use 5 classes of Kannada characters which were initially preprocessed using grey scaling and convert it to pgm format. This is directly input into the Deep convolutional network which is learnt from matching and non-matching image between the CNN with contrastive loss function in Siamese architecture. The Proposed OCR system uses very less time and gives more accurate results as compared to the regular CNN. The model can become a powerful tool for identification, particularly in situations where there is a high degree of variation in writing styles or limited training data is available.
Embedded artificial intelligence system using deep learning and raspberrypi f...IAESIJAI
Melanoma is a kind of skin cancer that originates in melanocytes responsible for producing melanin, it can be a severe and potentially deadly form of cancer because it can metastasize to other regions of the body if not detected and treated early. To facilitate this process, Recently, various computer-assisted low-cost, reliable, and accurate diagnostic systems have been proposed based on artificial intelligence (AI) algorithms, particularly deep learning techniques. This work proposed an innovative and intelligent system that combines the internet of things (IoT) with a Raspberry Pi connected to a camera and a deep learning model based on the deep convolutional neural network (CNN) algorithm for real-time detection and classification of melanoma cancer lesions. The key stages of our model before serializing to the Raspberry Pi: Firstly, the preprocessing part contains data cleaning, data transformation (normalization), and data augmentation to reduce overfitting when training. Then, the deep CNN algorithm is used to extract the features part. Finally, the classification part with applied Sigmoid Activation Function. The experimental results indicate the efficiency of our proposed classification system as we achieved an accuracy rate of 92%, a precision of 91%, a sensitivity of 91%, and an area under the curve- receiver operating characteristics (AUC-ROC) of 0.9133.
Deep learning based biometric authentication using electrocardiogram and irisIAESIJAI
Authentication systems play an important role in wide range of applications. The traditional token certificate and password-based authentication systems are now replaced by biometric authentication systems. Generally, these authentication systems are based on the data obtained from face, iris, electrocardiogram (ECG), fingerprint and palm print. But these types of models are unimodal authentication, which suffer from accuracy and reliability issues. In this regard, multimodal biometric authentication systems have gained huge attention to develop the robust authentication systems. Moreover, the current development in deep learning schemes have proliferated to develop more robust architecture to overcome the issues of tradition machine learning based authentication systems. In this work, we have adopted ECG and iris data and trained the obtained features with the help of hybrid convolutional neural network- long short-term memory (CNN-LSTM) model. In ECG, R peak detection is considered as an important aspect for feature extraction and morphological features are extracted. Similarly, gabor-wavelet, gray level co-occurrence matrix (GLCM), gray level difference matrix (GLDM) and principal component analysis (PCA) based feature extraction methods are applied on iris data. The final feature vector is obtained from MIT-BIH and IIT Delhi Iris dataset which is trained and tested by using CNN-LSTM. The experimental analysis shows that the proposed approach achieves average accuracy, precision, and F1-core as 0.985, 0.962 and 0.975, respectively.
Hybrid channel and spatial attention-UNet for skin lesion segmentationIAESIJAI
Melanoma is a type of skin cancer which has affected many lives globally. The American Cancer Society research has suggested that it a serious type of skin cancer and lead to mortality but it is almost 100% curable if it is detected and treated in its early stages. Currently automated computer vision-based schemes are widely adopted but these systems suffer from poor segmentation accuracy. To overcome these issue, deep learning (DL) has become the promising solution which performs extensive training for pattern learning and provide better classification accuracy. However, skin lesion segmentation is affected due to skin hair, unclear boundaries, pigmentation, and mole. To overcome this issue, we adopt UNet based deep learning scheme and incorporated attention mechanism which considers low level statistics and high-level statistics combined with feedback and skip connection module. This helps to obtain the robust features without neglecting the channel information. Further, we use channel attention, spatial attention modulation to achieve the final segmentation. The proposed DL based scheme is instigated on publically available dataset and experimental investigation shows that the proposed Hybrid Attention UNet approach achieves average performance as 0.9715, 0.9962, 0.9710.
Photoplethysmogram signal reconstruction through integrated compression sensi...IAESIJAI
The transmission of photoplethysmogram (PPG) signals in real-time is extremely challenging and facilitates the use of an internet of things (IoT) environment for healthcare- monitoring. This paper proposes an approach for PPG signal reconstruction through integrated compression sensing and basis function aware shallow learning (CSBSL). Integrated-CSBSL approach for combined compression of PPG signals via multiple channels thereby improving the reconstruction accuracy for the PPG signals essential in healthcare monitoring. An optimal basis function aware shallow learning procedure is employed on PPG signals with prior initialization; this is further fine-tuned by utilizing the knowledge of various other channels, which exploit the further sparsity of the PPG signals. The proposed method for learning combined with PPG signals retains the knowledge of spatial and temporal correlation. The proposed Integrated-CSBSL approach consists of two steps, in the first step the shallow learning based on basis function is carried out through training the PPG signals. The proposed method is evaluated using multichannel PPG signal reconstruction, which potentially benefits clinical applications through PPG monitoring and diagnosis.
Speaker identification under noisy conditions using hybrid convolutional neur...IAESIJAI
Speaker identification is biometrics that classifies or identifies a person from other speakers based on speech characteristics. Recently, deep learning models outperformed conventional machine learning models in speaker identification. Spectrograms of the speech have been used as input in deep learning-based speaker identification using clean speech. However, the performance of speaker identification systems gets degraded under noisy conditions. Cochleograms have shown better results than spectrograms in deep learning-based speaker recognition under noisy and mismatched conditions. Moreover, hybrid convolutional neural network (CNN) and recurrent neural network (RNN) variants have shown better performance than CNN or RNN variants in recent studies. However, there is no attempt conducted to use a hybrid CNN and enhanced RNN variants in speaker identification using cochleogram input to enhance the performance under noisy and mismatched conditions. In this study, a speaker identification using hybrid CNN and the gated recurrent unit (GRU) is proposed for noisy conditions using cochleogram input. VoxCeleb1 audio dataset with real-world noises, white Gaussian noises (WGN) and without additive noises were employed for experiments. The experiment results and the comparison with existing works show that the proposed model performs better than other models in this study and existing works.
Multi-channel microseismic signals classification with convolutional neural n...IAESIJAI
Identifying and classifying microseismic signals is essential to warn of mines’ dangers. Deep learning has replaced traditional methods, but labor-intensive manual identification and varying deep learning outcomes pose challenges. This paper proposes a transfer learning-based convolutional neural network (CNN) method called microseismic signals-convolutional neural network (MS-CNN) to automatically recognize and classify microseismic events and blasts. The model was instructed on a limited sample of data to obtain an optimal weight model for microseismic waveform recognition and classification. A comparative analysis was performed with an existing CNN model and classical image classification models such as AlexNet, GoogLeNet, and ResNet50. The outcomes demonstrate that the MS-CNN model achieved the best recognition and classification effect (99.6% accuracy) in the shortest time (0.31 s to identify 277 images in the test set). Thus, the MS-CNN model can efficiently recognize and classify microseismic events and blasts in practical engineering applications, improving the recognition timeliness of microseismic signals and further enhancing the accuracy of event classification.
Sophisticated face mask dataset: a novel dataset for effective coronavirus di...IAESIJAI
Efficient and accurate coronavirus disease (COVID-19) surveillance necessitates robust identification of individuals wearing face masks. This research introduces the sophisticated face mask dataset (SFMD), a comprehensive compilation of high-quality face mask images enriched with detailed annotations on mask types, fits, and usage patterns. Leveraging cutting-edge deep learning models—EfficientNet-B2, ResNet50, and MobileNet-V2—, we compare SFMD against two established benchmarks: the real-world masked face dataset (RMFD) and the masked face recognition dataset (MFRD). Across all models, SFMD consistently outperforms RMFD and MFRD in key metrics, including accuracy, precision, recall, and F1 score. Additionally, our study demonstrates the dataset's capability to cultivate robust models resilient to intricate scenarios like low-light conditions and facial occlusions due to accessories or facial hair.
Transfer learning for epilepsy detection using spectrogram imagesIAESIJAI
Epilepsy stands out as one of the common neurological diseases. The neural activity of the brain is observed using electroencephalography (EEG). Manual inspection of EEG brain signals is a slow and arduous process, which puts heavy load on neurologists and affects their performance. The aim of this study is to find the best result of classification using the transfer learning model that automatically identify the epileptic and the normal activity, to classify EEG signals by using images of spectrogram which represents the percentage of energy for each coefficient of the continuous wavelet. Dataset includes the EEG signals recorded at monitoring unit of epilepsy used in this study to presents an application of transfer learning by comparing three models Alexnet, visual geometry group (VGG19) and residual neural network ResNet using different combinations with seven different classifiers. This study tested the models and reached a different value of accuracy and other metrics used to judge their performances, and as a result the best combination has been achieved with ResNet combined with support vector machine (SVM) classifier that classified EEG signals with a high success rate using multiple performance metrics such as 97.22% accuracy and 2.78% the value of the error rate.
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Machine learning classification analysis model community satisfaction with traditional market facilities as public service
1. IAES International Journal of Artificial Intelligence (IJ-AI)
Vol. 12, No. 4, December 2023, pp. 1744~1754
ISSN: 2252-8938, DOI: 10.11591/ijai.v12.i4.pp1744-1754 1744
Journal homepage: http://ijai.iaescore.com
Machine learning classification analysis model community
satisfaction with traditional market facilities as public service
Hadi Syahputra1
. Musli Yanto1
. Muhammad Reza Putra1
. Aulia Fitrul Hadi1
. Selvi Zola Fenia2
1
Faculty of Computer Science, Universitas Putra Indonesia YPTK, Padang, Sumatera Barat, Indonesia
2
Faculty of Economics and Business, Universitas Putra Indonesia YPTK, Padang, Sumatera Barat, Indonesia
Article Info ABSTRACT
Article history:
Received Sep 7. 2022
Revised Jan 11. 2023
Accepted Mar 10. 2023
Traditional markets are public service facilities that can be utilized by the
community. The market function is used place where sellers and buyers meet
in conducting transactions. This study aims to build a machine learning
classification analysis model in measuring community satisfaction with
traditional market facilities. The analytical methods used include Fuzzy.
multiple linear regression (MRL), artificial neural network (ANN), and
decision tree (DT). Fuzzy is used to generate a pattern of rules in determining
the level of satisfaction. MRL serves to measure and test the correlation of
rules that have been formed. The ANN method is used to carry out the
classification analysis process based on learning. In the final stage. DT is used
to describe the decision tree of the analysis process. This study presents the
results of machine learning analysis which is very good in determining
satisfaction with an accuracy rate of 99.99%. This result is influenced by fuzzy
logic which can develop a classification rule pattern of 32 patterns. MRL also
shows a significant correlation level of 81.1% based on the indicator variables.
Overall, the machine learning classification analysis model can provide
knowledge to be considered in the management of traditional markets as
public service facilities.
Keywords:
Classification analysis models
Community satisfaction
Machine learning
Public services
Traditional markets
This is an open access article under the CC BY-SA license.
Corresponding Author:
Hadi Syahputra
Department of Computer Systems, Universitas Putra Indonesia YPTK
Padang, Indonesia
Email: hadisyahputra@upiyptk.ac.id
1. INTRODUCTION
A public service is a form of activity carried out by individuals and government organizations in
meeting needs [1]. These public services involve the provision of public service facilities such as goods and
services [2]. The provision of these service facilities basically must meet the standards that have been set by
the applicable rules [3]. With the standards that have been determined. all elements of society can use these
facilities together to meet needs [4]. One form of public service that provides services in accessing buying and
selling transactions is the traditional market [5]. The market concept is a facility built for the sale and purchase
of goods and services [6].
To be able to measure the level of satisfaction with public services in traditional markets, an analytical
process is needed [7]. The analysis process in measuring the level of satisfaction is used as input in management
and evaluation [8]. This satisfaction analysis is also used as a process of identifying the quality assessment of
a service that has an impact on sustainability [9]. The assessments include security, cleanliness, and access to
public services [10]. The market service analysis orientation can provide the achievement of targets for
satisfaction with public services [11]. The need for analysis is carried out to see the gaps that occur such as
service, friendliness, and security in transactions [12]. These results can be used as an assessment of the
2. Int J Artif Intell ISSN: 2252-8938
Machine learning classification analysis model community satisfaction with traditional … (Hadi Syahputra)
1745
sustainability of a market so that people do not move to the modern market that is developing at this time [13].
The form of the analysis process that has been carried out in measuring the level of community satisfaction
using the concept of machine learning [14]. This concept can also be used to extract knowledge from the
community for consideration in decision-making [15]. Machine learning is a concept that is used to carry out
the analysis process by presenting information output [16]. Machine learning can process large amounts of data
to carry out classification and prediction analysis processes [17].
Research in analyzing the level of community satisfaction with traditional markets as public services
shows that traditional markets can have an active role in the economic, environmental, political, and
administrative fields [18]. The same study also carried out the process of analyzing community satisfaction
with traditional market facilities using a dataset of 205 respondents presenting quite significant results [19].
Furthermore, the measurement of the level of community satisfaction with public services in traditional markets
using the analytical hierarchy process (AHP) classification analysis presents a significant value in increasing
transaction activities in traditional markets [20].
To improve the performance of the results of the classification analysis process, it is necessary to
perform a method that can support the machine learning work process. Fuzzy logic can support machine
learning performance in presenting rules in the classification process [21]. The results of the implementation
of fuzzy logic in machine learning provide the maximum level of accuracy [22]. Fuzzy logic is also able to
generate new feature sets and rules that can be used to perform the analysis process [23]. Not only fuzzy logic,
the multiple linear regression (MRL) method is also capable of playing an active role in an analysis pattern
[24]. MRL can maximize the analytical model in making predictions and classifications by measuring the
correlation relationships that occur in the data patterns used [25]. MRL is also able to prove the performance
of an analytical model based on the relationship between indicator variables and outputs in the classification
process [26].
To maximize the classification analysis process, the decision tree (DT) method is used to describe the
shape of the decision tree [27]. DT can present the rules in the form of a decision tree from the hidden
knowledge of data [28]. DT is also a method that can describe a decision tree and is used to analyze the
important variables involved in the analysis process [29]. DT method is a method adopted by machine learning
to carry out the classification process [30].
Thus. this study will present a discussion about carrying out an analytical process to measure
community satisfaction with traditional markets as public facilities. The process is built into a classification
model analysis using machine learning. The analytical model was developed using the fuzzy inference method
in artificial neural network (ANN) learning. The results of the analysis will also measure the correlation using
MRL to improve the performance of the model analysis in presenting the output. The final stage of the analysis
process will also be able to describe the knowledge base formed on the decision tree. Thus, this study presents
an analysis of the optimal novelty model to produce maximum analysis output. Thus, the results of this study
can be used as supporting material for decision making in the process of managing public service facilities.
2. METHOD
The process of classification analysis in this research work was carried out in several stages. The
stages of the process can be presented in a research framework by adopting ML performance consisting of
fuzzy, ANN, MRL, and DT methods. The fuzzy method is used to generate classification pattern rules based
on the parameters used. ANN has analytical performance in the form of network learning. The MRL method
is used to measure the performance of the ANN classification analysis process to see the correlation
relationship. The DT analysis stage is finally able to describe the output results and the pattern in conducting
the analysis. The description of the research framework can be described in Figure 1.
Figure 1 describes the stages of analysis in classifying public satisfaction with public service facilities
in traditional markets. These stages are presented in the analysis of the model developed using the ML concept.
The analysis process is presented by making a pattern of classification rules based on indicator variables using
fuzzy logic. The resulting rule pattern will be analyzed using ANN to present the output. The output results
presented will then be measured by the correlation relationship formed between the variables and the output
analysis. The final stage of the analysis process presents an overview of the classification analysis using the
DT method. Overall, the results of the analysis carried out will provide maximum results in determining
community satisfaction with public service facilities.
2.1. Fuzzy inference engine
Fuzzy Inference is a method used to analyze the uncertainty of data [31]. Fuzzy is also a logic
developed in machine learning that can be used to generate rule patterns in classification [32]. Logical
uncertainty can be used as a control process in conducting analysis [33]. Analysis rules are based on fuzzy sets
and the rules that are formed [34]. The fuzzy logic can be seen in Figure 2 [35].
3. ISSN: 2252-8938
Int J Artif Intell, Vol. 12, No. 4, December 2023: 1744-1754
1746
Figure 1. Research framework
Figure 2. Structure fuzzy inference engine
Figure 2 presents the logic of logic in generating classification patterns. The fuzzy performance begins
with the fuzzification process on the input variables. After the fuzzification process is carried out, it is continued
in the formation of fuzzy rules which will later be tested on the inference engine process to see the performance
of the rules formed. The final stage is the defuzzification process which can be used as a transformation process
for the fuzzy output obtained. The pattern of analysis rules produced by fuzzy will later be faced in solving a
problem [36], [37].
2.2. Artificial neural network (ANN)
ANN is a method based on the performance of the human brain in problem-solving [38]. ANN
presents a systematic process in problem-solving to produce solutions [39], [40]. The ANN can be adopted in
machine learning [41]. ANN can provide optimal results by presenting output with a fairly good level of
accuracy [42]. ANN presents mathematical calculations that are modeled to produce the output [43]. ANN can
work effectively and be presented in the form of an algorithm [44]. The output of ANN can be used as an
alternative solution in decision making [45].
4. Int J Artif Intell ISSN: 2252-8938
Machine learning classification analysis model community satisfaction with traditional … (Hadi Syahputra)
1747
2.3. Multiple linear regression (MRL)
The MRL method is a method that has been used to measure the level of relationship between
variables [46]. MRL has the performance to evaluate a model in conducting analysis [47]. MRL provides
mathematical calculations on the parameters and outputs used in conducting the analysis [48]. The MRL results
can prove the results of the analysis based on the correlation test in each indicator variable [49]. Overall MRL
can see the relationship pattern data significantly [50]. The MRL equation can be expressed as (1) [50].
𝑌𝑖 = 𝛽0 + 𝛽1𝑥𝑖1 + 𝛽2 𝑥𝑖 2 + 𝛽3𝑥𝑖 3. . . . . 𝛽𝑝𝑥𝑖𝑝 (1)
MRL analysis is able to test each indicator variable used. Constant values can form a regression model
to present the output relationship from the analysis process. MRL is also used as a medium to measure an
analysis pattern [51]. The regression analysis model aims to provide parameter levels based on variables (X)
and (Y). This model can be proven by the regression equation presented in (2) [52].
𝑌 = 𝑎 + 𝑏𝑋 (2)
In (2) explains that b is the direction of the coefficient and a is the value that represents the intersection.
the equation also describes the equation of the line seen by the variable. Based on the performance of the results
of the MRL classification analysis can present maximum results.
2.4. Decision tree (DT)
A decision tree is a method that can be used in solving classification problems that present output in
the form of a decision tree [53]. DT is also a technique that adopts machine learning and can be implemented
in the classification process [54]. DT performs a classification process by describing a decision tree consisting
of nodes and nodes in describing information [55]. The DT method can be developed to carry out the validation
process for an analysis model [56]. DT a performance by performing mathematical calculations and adopting
a decision-making system [57]. The equations in the DT method can be expressed as (3) [58].
Entropy(S) = − ∑ PS(ci)logPS(ci)
𝑐
𝑖=1 (3)
In (3) presents the calculation process in the DT method. The entropy value is based on a set S [57].
The resulting entropy value will be the root of the decision tree image [59]. The results of the DT analysis
process are finally able to present a form of analysis based on classification [60].
3. RESULTS AND DISCUSSION
The discussion of the process of analyzing the classification of public satisfaction with public facilities
in traditional markets using the machine learning concept starts from the data analysis stage. Research data is
sourced from market visitor respondents by assessing several predetermined indicators including market trader
(X1), market manager (X2), market facilities (X3), hygiene (X4), and safety (X5). The dataset obtained based on
the results of the respondents' assessment can be presented in Table 1.
Table 1. Respondent assessment dataset
Market trader (X1) Market manager (X2) Market facilities (X3) Hygiene (X4) Safety (X5)
83 68 74 62 80
65 68 80 66 75
75 66 67 69 65
70 73 60 68 80
74 75 70 70 82
80 67 76 70 78
60 74 74 72 73
75 68 75 74 79
68 80 76 73 80
74 78 80 69 80
75 75 74 69 83
82 76 65 68 60
83 70 74 73 68
74 56 75 84 78
65 76 80 80 78
68 68 82 80 75
5. ISSN: 2252-8938
Int J Artif Intell, Vol. 12, No. 4, December 2023: 1744-1754
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Table 1 presents a sample of respondents' assessment results of traditional markets as public facilities.
The results of the respondents' assessments were used as an analytical research dataset of 203 data. After the
research dataset, the next step is to carry out the analysis process using a fuzzy inference engine. The stages
carried out in the fuzzy process include the process of fuzzification, rule formation, inference engine, and
defuzzification. The results of the fuzzy analysis process can present the pattern of classification analysis rules
which can be seen in Table 2.
Table 2. Results of fuzzy logic rules
Rule Variabel fuzzy Satisfaction results
X1 X2 X3 X4 X5
1 IF Not friendly Not good Not good Not hygiene Not safety THEN Not satisfaction
2 Not friendly Not good Not good Pretty hygiene Not safety Not satisfaction
3 Not friendly Not good Pretty good Pretty hygiene Not safety Not satisfaction
4 Not friendly Pretty good Pretty good Pretty hygiene Not safety Satisfaction
5 Friendly enough Pretty good Pretty good Pretty hygiene Not safety Satisfaction
6 Friendly enough Pretty good Pretty good Pretty hygiene Not safety Satisfaction
7 Friendly enough Pretty good Pretty good Not hygiene Not safety Not satisfaction
8 Friendly enough Pretty good Not good Not hygiene Not safety Not satisfaction
9 Friendly enough Not good Not good Not hygiene Not safety Not satisfaction
10 Friendly enough Pretty good Pretty good Not hygiene Safe enough Satisfaction
11 Friendly enough Pretty good Pretty good Hygiene Safety Satisfaction
12 Friendly enough Pretty good Good Hygiene Safety Very satisfaction
13 Friendly enough Good Good Hygiene Safety Very satisfaction
14 Friendly Good Good Hygiene Safety Very satisfaction
15 Friendly Good Good Hygiene Not safety Very satisfaction
16 Friendly Good Good Not hygiene Not safety Satisfaction
17 Friendly Good Not good Not hygiene Not safety Satisfaction
18 Friendly Not good Not good Not hygiene Not safety Not satisfaction
19 Friendly Not good Not good Not hygiene Safe enough Not satisfaction
20 Friendly Not good Not good Pretty hygiene Safe enough Satisfaction
21 Friendly Not good Pretty good Pretty hygiene Safe enough Satisfaction
22 Friendly enough Not good Pretty good Pretty hygiene Safe enough Satisfaction
23 Friendly enough Pretty good Good Not hygiene Not safety Satisfaction
24 Friendly Pretty good Good Not hygiene Not safety Satisfaction
25 Not friendly Pretty good Good Not hygiene Not safety Satisfaction
26 Not friendly Not good Good Not hygiene Not safety Not satisfaction
27 Not friendly Pretty good Good Not hygiene Safe enough Satisfaction
28 Not friendly Pretty good Good Pretty hygiene Safe enough Satisfaction
29 Not friendly Good Good Pretty hygiene Safe enough Very satisfaction
30 Not friendly Good Good Pretty hygiene Safety Very satisfaction
31 Not friendly Good Good Hygiene Safety Very satisfaction
32 Not friendly Pretty good Good Hygiene Safety Satisfaction
Table 2 is the result of fuzzy logic analysis which presents 32 patterns of classification rules. The
pattern of the rules presents the level of satisfaction including not satisfaction, satisfaction, and very
satisfaction. After the pattern analysis, it is continued with the making of a classification ANN analysis model.
The ANN classification learning model adopts the application of multilayer backpropagation which consists
of 5 input layers, 5 hidden layers (1), 5 hidden layers (2), and 1 output layer. The model will be carried out in
the training and testing phase to find the best analysis model. The results of learning analysis using ANN can
be presented in Table 3.
Based on the training and testing of the ANN classification analysis model. the multilayer
backpropagation algorithm can find the best ANN classification analysis model for measuring people's
satisfaction with the traditional market as a public facility. The best analysis model adopts 5 input layers: 10
hidden layers (1), 10 hidden layers (2), 10 hidden layers (3), and 1 output layer. The best ANN model can be
depicted in Figure 3.
Figure 3 is the result of the description of the best ANN classification analysis model obtained after
network learning from the training and testing process. This model provides an output accuracy rate of 99.99%
and an error rate performance of 0.00096225. These results have presented a fairly optimal output in conducting
the classification analysis process. After the learning process presents the best ANN model output, then the
analysis process is carried out with ANN by presenting the outputs which can be seen in Figure 4.
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After the ANN analysis process is presented in Figure 4. the work will continue by measuring the
level of regression and correlation from the previous analysis rules. This measurement is carried out to see the
parameter indicators that affect the results of the analysis obtained. The results of MRL analysis can be
presented in Tables 4 to 6.
Table 4. Results of analysis regression
Model Summary
Model R R
square
Adjusted R
square
Std. error of
the estimate
Change statistics
R square change F Change df1 df2 Sig. F change
1 0.882a
0.779 0.221 0.74976 0.779 0.448 5 26 0.811
a. Predictors: (Constant). Safety. Market_Manager. Market_Trader. Market_Facilities. Hygiene
Table 5. Results of analysis of variance
ANOVAa
Model Sum of squares df Mean square F Sig.
1 Regression 0.189 5 0.221 0.448 0.811b
Residual 14.616 26 0.221
Result 15.875 31
a. Dependent variable: Satisfaction
b. Predictors: (Constant). Safety. Market_Manager. Market_Trader. Market_Facilities. Hygiene
Table 6. Results of analysis coofecien correlation
Coefficient correlationsa
Model Safety Market_Manager Market_Trader Market_Facilities Hygiene
1 Correlations Safety 1.000 0.665 0.725 0.717 0.840
Market_Manager 0.665 1.000 0.686 0.680 0.773
Market_Trader 0.725 0.686 1.000 0.865 0.815
Market_Facilities 0.717 0.680 0.865 1.000 0.725
Hygiene 0.840 0.773 0.815 0.725 1.000
Covariances Safety 0.009 0.000 0.002 0.002 0.005
Market_Manager 0.000 0.006 0.002 0.001 0.001
Market_Trader 0.002 0.002 0.008 0.004 0.001
Market_Facilities 0.002 0.001 0.004 0.010 0.003
Hygiene -0.005 0.001 0.001 -0.003 0.010
a. Dependent Variable: Satisfaction
Based on the MRL analysis test, Table 4 presents the regression analysis of the classification analysis
pattern with a significant value of 81.1%. These results have been able to prove that the pattern of rules formed
based on the fuzzy process provides a fairly optimal output in classifying. Table 5 also presents the results of
the analysis of variance (ANOVA) with a Sum of Squares value of 0.189. These results are an affirmation that
the regression relationship formed between the variables with the output provides a mutually significant level
of relationship. Table 6 presents the results of measuring the coefficients and correlations of each indicator
variable. These results present that the correlation relationship is formed from each variable that occurs above
0.60. This value is the basis that the correlation formed between the patterns of classification analysis rules has
a very good relationship to measure people's satisfaction with traditional markets as public facilities.
After the MRL analysis process proves the regression relationship and the correlation of the
classification pattern, it is continued by conducting a classification analysis using the DT. The DT analysis
process can present the output in the form of a decision tree to describe knowledge in the classification process.
The results of the DT analysis process can be presented in Figure 5.
Figure 5 is the result of the DT analysis in presenting the output in the form of a decision tree. These
results can describe the knowledge-based pattern of classification rules to be taken into consideration in
decision-making. Overall, the classification analysis process produces an output that is optimal enough to
measure people's satisfaction with traditional markets as public facilities.
Based on the discussion that has been done, the analysis process using machine learning produces
maximum output. These results are obtained based on the classification rule pattern that has been developed
based on fuzzy logic and tested using the MRL method. The output of the analysis using ANN can present a
very good level of accuracy of 99.99%. With the results of the analysis presented, the update of this study
presents an optimal analysis model based on the output of the analysis process that has been carried out. Based
on this update, this research can provide an effective and efficient analytical model in the process of planning
and managing traditional markets as public service facilities.
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1751
Figure 5. Results of DT analysis process
4. CONCLUSION
The machine learning classification analysis model in measuring people's satisfaction with traditional
market facilities provides maximum output. These results are based on the pattern of classification rules formed
from the fuzzy process as well as the measurement of regression and correlation using the MRL method. Based
on the results of MRL measurements, shows that the results of the regression and correlation relationships are
quite significant so that the performance of ANN in the network learning process provides the maximum level
of accuracy. Not only that, but the performance role of the DT method also presents results that are quite good
in describing information and knowledge-based in the form of a decision tree. Overall, this research can present
a structured and systematic analytical model with the updates presented to develop a pattern of classification
analysis rules. Based on the results of this study, the classification analysis model can be used as a basis for
decision-making for the government for the traditional market management process.
ACKNOWLEDGEMENTS
This work was supported by Universitas Putra Indonesia with an independent research grant funded
by the Padang Computer College Foundation (YPTK) No. 012/PM/UPI-YPTK/III/2023. This research was
also supported by the Institute for Research and Community Service (LPPM) at Universitas Putra Indonesia
YPTK Padang.
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BIOGRAPHIES OF AUTHORS
Hadi Syahputra a graduate of Putra Indonesia University YPTK. In 2013 he
obtained a Master's Degree in Computer with a concentration in Information Technology.
His career as a lecturer at Putra Indonesia University YPTK began in 2013 until now. 2015
until now he has been trusted as a sports coach at Putra Indonesia University YPTK. Working
in the field of networking and Micro Controllers with several published articles. Monitoring
DNS Query with Pi-Hole Firewall Using Raspberry B+ Integrated with Mikrotik Router RB
931-2nd is an article that has been published in an international journal in 2021. He can be
contacted at email: hadisaputra@upiyptk.ac.id.
Musli Yanto Born in Jakarta on July 7. 1989. A lecturer at the Putra Indonesia
University YPTK Padang. The educational history of the Informatics Engineering
undergraduate program was completed in 2012 and the Informatics Engineering Masters
program in 2014. His areas of expertise include Artificial Intelligence (AI). Expert Systems
(ES). Data Mining (DM). Decision Support Systems (DSS). He could be contacted via email:
musli_yanto@upiyptk.ac.id.
Muhammad Reza Putra graduated from YPTK. Putra Indonesia University. In
2013 he obtained a Master's in Computer with a concentration in Information Technology.
His career as a lecturer at YPTK Putra Indonesia University began in 2013 until now. From
2020 until now he has been entrusted with being the Head of the Secretariat at Putra Indonesia
YPTK University. Working in the field of deep learning. and machine learning with several
published articles. Prediction of Drug Needs to be Based on Deep Learning Approach and
The Classification Model is an article published in an international journal in 2023. He can
be contacted via email: muhammad_reza@upiyptk.ac.id.
Aulia Fitrul Hadi Educational History Bachelor of Computer Th. 2013 and
Master of Computer Th. 2015. Field of Expertise Programming Languages. Operating
Systems. Computer Networks. and Network and Database Security The theme of the focus
of research that has been carried out in Decision Support Systems. Expert Systems.
Augmented Reality. Management Information Systems. Expert Systems. Artificial
intelligence. and Multimedia. He can be contacted at email: fitrulhadi@upiyptk.ac.id.
Selvi Zola Fenia Selvi Zola Fenia. was born in the city of Padang. West Sumatra.
Alumni of Putra Indonesia University YPTK Padang. Completed his undergraduate
education in Psychology in 2010 and continued his career as a Counseling Guidance Teacher
at SMPN 24 Padang until 2011. In 2011 he taught at the Apikes Iris Medical Record Study
program and was appointed Deputy 1 (Curriculum Sector) from 2014 to 2018. The 2013
Master's Degree study program obtained a Master of Management degree from UPI YPTK
Padang. In 2018 he was chosen as SPMI Facilitator at Apikes Iris Padang. Since 2018 until
now he has taught at the Faculty of Economics and Business. Management Studies Program
at UPI YPTK Padang. She could be contacted via email: selvizolafenia.szf@gmail.com.