Quicksort is a divide and conquer algorithm that works by partitioning an array around a pivot value and recursively sorting the subarrays. It first selects a pivot element and partitions the array by moving all elements less than the pivot before it and greater elements after it. The subarrays are then recursively sorted through this process. When implemented efficiently with an in-place partition, quicksort is one of the fastest sorting algorithms in practice, with average case performance of O(n log n) time but worst case of O(n^2) time.
The document discusses greedy algorithms and their application to optimization problems. It provides examples of problems that can be solved using greedy approaches, such as fractional knapsack and making change. However, it notes that some problems like 0-1 knapsack and shortest paths on multi-stage graphs cannot be solved optimally with greedy algorithms. The document also describes various greedy algorithms for minimum spanning trees, single-source shortest paths, and fractional knapsack problems.
Stack is a data structure that only allows elements to be added and removed from one end, called the top. It has components like a top pointer variable, elements that hold data, and a maximum size. Stacks can be implemented as arrays or linked lists. The main operations on a stack are push, which adds an element to the top, and pop, which removes an element from the top. These operations work similarly in array and linked list implementations, by incrementing or decrementing the top pointer and adding or removing the top element.
This document discusses different types of linked lists, including singly linked lists, circular linked lists, and doubly linked lists. It provides examples of circular linked lists and explains that in a circular linked list, the last node contains the address of the first node so that the list can be traversed repeatedly. The document also discusses common linked list operations like insertion and deletion of nodes. Finally, it covers stacks and queues, defining them as LIFO and FIFO data structures, respectively, and providing examples of their implementation and different types.
Materi yang ada pada slide ini berisi :
Apa itu generic programming
Konsep tipe object
Konsep Tag generic<E>
Collection
Iterator
Arraylist
LinkedList
Stack
PriorityQueue
HashMap
----------------------------------------------------------------------
Keep in touch with me in :
Email : rizkiadamunikom@gmail.com
Quicksort is a divide and conquer algorithm that works by partitioning an array around a pivot value and recursively sorting the subarrays. It first selects a pivot element and partitions the array by moving all elements less than the pivot before it and greater elements after it. The subarrays are then recursively sorted through this process. When implemented efficiently with an in-place partition, quicksort is one of the fastest sorting algorithms in practice, with average case performance of O(n log n) time but worst case of O(n^2) time.
The document discusses greedy algorithms and their application to optimization problems. It provides examples of problems that can be solved using greedy approaches, such as fractional knapsack and making change. However, it notes that some problems like 0-1 knapsack and shortest paths on multi-stage graphs cannot be solved optimally with greedy algorithms. The document also describes various greedy algorithms for minimum spanning trees, single-source shortest paths, and fractional knapsack problems.
Stack is a data structure that only allows elements to be added and removed from one end, called the top. It has components like a top pointer variable, elements that hold data, and a maximum size. Stacks can be implemented as arrays or linked lists. The main operations on a stack are push, which adds an element to the top, and pop, which removes an element from the top. These operations work similarly in array and linked list implementations, by incrementing or decrementing the top pointer and adding or removing the top element.
This document discusses different types of linked lists, including singly linked lists, circular linked lists, and doubly linked lists. It provides examples of circular linked lists and explains that in a circular linked list, the last node contains the address of the first node so that the list can be traversed repeatedly. The document also discusses common linked list operations like insertion and deletion of nodes. Finally, it covers stacks and queues, defining them as LIFO and FIFO data structures, respectively, and providing examples of their implementation and different types.
Materi yang ada pada slide ini berisi :
Apa itu generic programming
Konsep tipe object
Konsep Tag generic<E>
Collection
Iterator
Arraylist
LinkedList
Stack
PriorityQueue
HashMap
----------------------------------------------------------------------
Keep in touch with me in :
Email : rizkiadamunikom@gmail.com
Dokumen tersebut merupakan contoh dokumen XML yang mendeskripsikan resep roti tawar dengan 4 langkah pembuatan, mulai dari mencampur bahan, menguleni adonan, mengembangkan adonan, hingga memanggangnya di oven. Dokumen tersebut menggunakan tag-tag XML seperti <Resep>, <bahan>, <Cara_membuat>, dan <langkah> untuk menyusun struktur dan konten dari resep roti tawar.
The document discusses linear and binary search algorithms. Linear search is a sequential search where each element of a collection is checked sequentially to find a target value. Binary search improves on this by checking the middle element first and narrowing the search space in half each time based on the comparison. It allows searching sorted data more efficiently in logarithmic time as opposed to linear time for sequential search. The document provides pseudocode to implement binary search and an example to search an integer in a sorted array.
Quicksort has average time complexity of O(n log n), but worst case of O(n^2). It has O(log n) space complexity for the recursion stack. It works by picking a pivot element, partitioning the array into sub-arrays of smaller size based on element values relative to the pivot, and recursively
Dokumen tersebut membahas proses perencanaan proyek perangkat lunak yang meliputi penentuan ruang lingkup, estimasi sumber daya yang dibutuhkan, dan teknik-teknik estimasi seperti berbasis line of code, function point, proses, dan model empiris seperti COCOMO."
1. Buku ini membahas konsep-konsep utama pemrograman berorientasi objek seperti class, objek, enkapsulasi, pewarisan, dan polimorfisme serta penjelasan mengenai UML.
2. Buku ini terdiri dari 286 halaman dan membahas topik-topik seperti class diagram, hubungan antara OOAD dan OOP, konsep objek dan kelas, simulasi enkapsulasi dan pewarisan, serta penjelasan abstraksi dan generik dalam OOP.
3. Buk
The document discusses and compares linear and binary search algorithms. Linear search sequentially checks each element of an unsorted array to find a target value, while binary search works on a sorted array by repeatedly calculating the midpoint and comparing the target to the value there to narrow the search range. It provides steps for performing a binary search, including sorting the array, calculating the midpoint, and updating the search range based on whether the target is less than, greater than, or equal to the midpoint value.
Algorithms Lecture 3: Analysis of Algorithms IIMohamed Loey
We will discuss the following: Maximum Pairwise Product, Fibonacci, Greatest Common Divisors, Naive algorithm is too slow. The Efficient algorithm is much better. Finding the correct algorithm requires knowing something interesting about the problem
Binary search is a fast search algorithm that works by dividing a sorted collection in half at each step to locate a target value. It compares the middle element to the target and eliminates half of the remaining elements based on whether the middle element is greater than or less than the target. This process continues recursively on smaller sub-arrays until the target is found or the sub-array is empty, with an average time complexity of O(log n). The pseudocode shows initializing lower and upper bounds and calculating the mid-point to compare to the target at each step until the target is found or not present.
The document compares the application of three fuzzy logic methods (Tsukamoto, Sugeno, Mamdani) to determine poor families in Sumedang Regency, Indonesia based on criteria set by the government. The three methods were applied to data from 20 families and produced different results for the number of poor versus non-poor families identified. Specifically, the Tsukamoto method identified 4 poor families, the Sugeno method identified 8 poor families, and the Mamdani method identified 10 poor families. This indicates that each method can produce different outputs even when given the same input data.
Algoritma ini merupakan salah satu metode kecerdasann buatan untuk pencocokan kata / kalimat, dan dapat digunakan dalam pencocokan binary. contoh kasus dapat digunakan untuk pengolahan citra, deteksi, pencarian KMS
This document discusses list comprehensions in Python. It provides examples of using list comprehensions to generate lists based on conditions. It describes generating a list of squares of numbers from 1 to 20 and generating a list of letters from a dictionary whose values are greater than or equal to 3. It then discusses using list comprehensions to solve practice problems involving loading height data from a file, summarizing statistics, looking up heights, finding people above a certain height, and printing a report of heights.
This document discusses linear search and binary search algorithms. Linear search sequentially checks each element of an unsorted array to find a target value, resulting in O(n) time complexity. Binary search works on a sorted array, comparing the target to the middle element and recursively searching half the array, requiring O(log n) time. The document provides pseudocode for both algorithms and compares their performance on different sized inputs. It also discusses properties of greedy algorithms and provides an example of when a greedy solution fails to find the optimal result.
The document discusses sorting algorithms and randomized quicksort. It explains that quicksort is an efficient sorting algorithm that was developed by Tony Hoare in 1960. The quicksort algorithm works by picking a pivot element and reordering the array so that all smaller elements come before the pivot and larger elements come after. It then recursively applies this process to the subarrays. Randomized quicksort improves upon quicksort by choosing the pivot element randomly, making the expected performance of the algorithm good for any input.
Prim's algorithm finds a minimum spanning tree of a graph using a greedy approach. It treats the nodes as a single tree and adds new nodes from the graph one by one, choosing the connection with the lowest weight at each step. This grows the tree until all nodes are included. Kruskal's algorithm also uses greedy approach but treats the graph as separate trees that merge as it adds edges from lowest to highest weight while ensuring no cycles are formed.
Dokumen tersebut merupakan contoh dokumen XML yang mendeskripsikan resep roti tawar dengan 4 langkah pembuatan, mulai dari mencampur bahan, menguleni adonan, mengembangkan adonan, hingga memanggangnya di oven. Dokumen tersebut menggunakan tag-tag XML seperti <Resep>, <bahan>, <Cara_membuat>, dan <langkah> untuk menyusun struktur dan konten dari resep roti tawar.
The document discusses linear and binary search algorithms. Linear search is a sequential search where each element of a collection is checked sequentially to find a target value. Binary search improves on this by checking the middle element first and narrowing the search space in half each time based on the comparison. It allows searching sorted data more efficiently in logarithmic time as opposed to linear time for sequential search. The document provides pseudocode to implement binary search and an example to search an integer in a sorted array.
Quicksort has average time complexity of O(n log n), but worst case of O(n^2). It has O(log n) space complexity for the recursion stack. It works by picking a pivot element, partitioning the array into sub-arrays of smaller size based on element values relative to the pivot, and recursively
Dokumen tersebut membahas proses perencanaan proyek perangkat lunak yang meliputi penentuan ruang lingkup, estimasi sumber daya yang dibutuhkan, dan teknik-teknik estimasi seperti berbasis line of code, function point, proses, dan model empiris seperti COCOMO."
1. Buku ini membahas konsep-konsep utama pemrograman berorientasi objek seperti class, objek, enkapsulasi, pewarisan, dan polimorfisme serta penjelasan mengenai UML.
2. Buku ini terdiri dari 286 halaman dan membahas topik-topik seperti class diagram, hubungan antara OOAD dan OOP, konsep objek dan kelas, simulasi enkapsulasi dan pewarisan, serta penjelasan abstraksi dan generik dalam OOP.
3. Buk
The document discusses and compares linear and binary search algorithms. Linear search sequentially checks each element of an unsorted array to find a target value, while binary search works on a sorted array by repeatedly calculating the midpoint and comparing the target to the value there to narrow the search range. It provides steps for performing a binary search, including sorting the array, calculating the midpoint, and updating the search range based on whether the target is less than, greater than, or equal to the midpoint value.
Algorithms Lecture 3: Analysis of Algorithms IIMohamed Loey
We will discuss the following: Maximum Pairwise Product, Fibonacci, Greatest Common Divisors, Naive algorithm is too slow. The Efficient algorithm is much better. Finding the correct algorithm requires knowing something interesting about the problem
Binary search is a fast search algorithm that works by dividing a sorted collection in half at each step to locate a target value. It compares the middle element to the target and eliminates half of the remaining elements based on whether the middle element is greater than or less than the target. This process continues recursively on smaller sub-arrays until the target is found or the sub-array is empty, with an average time complexity of O(log n). The pseudocode shows initializing lower and upper bounds and calculating the mid-point to compare to the target at each step until the target is found or not present.
The document compares the application of three fuzzy logic methods (Tsukamoto, Sugeno, Mamdani) to determine poor families in Sumedang Regency, Indonesia based on criteria set by the government. The three methods were applied to data from 20 families and produced different results for the number of poor versus non-poor families identified. Specifically, the Tsukamoto method identified 4 poor families, the Sugeno method identified 8 poor families, and the Mamdani method identified 10 poor families. This indicates that each method can produce different outputs even when given the same input data.
Algoritma ini merupakan salah satu metode kecerdasann buatan untuk pencocokan kata / kalimat, dan dapat digunakan dalam pencocokan binary. contoh kasus dapat digunakan untuk pengolahan citra, deteksi, pencarian KMS
This document discusses list comprehensions in Python. It provides examples of using list comprehensions to generate lists based on conditions. It describes generating a list of squares of numbers from 1 to 20 and generating a list of letters from a dictionary whose values are greater than or equal to 3. It then discusses using list comprehensions to solve practice problems involving loading height data from a file, summarizing statistics, looking up heights, finding people above a certain height, and printing a report of heights.
This document discusses linear search and binary search algorithms. Linear search sequentially checks each element of an unsorted array to find a target value, resulting in O(n) time complexity. Binary search works on a sorted array, comparing the target to the middle element and recursively searching half the array, requiring O(log n) time. The document provides pseudocode for both algorithms and compares their performance on different sized inputs. It also discusses properties of greedy algorithms and provides an example of when a greedy solution fails to find the optimal result.
The document discusses sorting algorithms and randomized quicksort. It explains that quicksort is an efficient sorting algorithm that was developed by Tony Hoare in 1960. The quicksort algorithm works by picking a pivot element and reordering the array so that all smaller elements come before the pivot and larger elements come after. It then recursively applies this process to the subarrays. Randomized quicksort improves upon quicksort by choosing the pivot element randomly, making the expected performance of the algorithm good for any input.
Prim's algorithm finds a minimum spanning tree of a graph using a greedy approach. It treats the nodes as a single tree and adds new nodes from the graph one by one, choosing the connection with the lowest weight at each step. This grows the tree until all nodes are included. Kruskal's algorithm also uses greedy approach but treats the graph as separate trees that merge as it adds edges from lowest to highest weight while ensuring no cycles are formed.
Dokumen tersebut memberikan penjelasan mengenai konsep dasar data mining klasifikasi, proses klasifikasi menggunakan algoritma Naive Bayes, serta contoh kasus klasifikasi menggunakan atribut usia, pendapatan, pekerjaan, dan punya deposito atau tidak.
The document provides an overview of quantum computing concepts and the IBM Quantum Experience platform. It begins with a short history of quantum computing developments from the 1930s to present. It then explains basic quantum concepts like qubits, superposition, entanglement, and quantum gates. The document outlines requirements for building a quantum computer, including well-defined qubits, initialization, gates, coherence times, and measurement. It describes the IBM Quantum Experience as a platform that provides access to an actual quantum processor via the cloud, along with simulation and tutorial capabilities. Users can design circuits using a graphical Quantum Composer interface and run algorithms on real quantum hardware or simulation.
This document discusses looping structures in algorithms and programming. It defines looping as repeating statements to fulfill a looping condition. The main types of looping structures are for, while, and repeat loops. Examples are given in pseudocode and Pascal to illustrate for loops that count ascending and descending, while loops, and repeat loops. Exercises are provided to practice different types of loops.
This short document provides instructions for reducing unwanted LinkedIn update emails that fill up users' inboxes. It outlines a 3 step process for adjusting LinkedIn settings to only receive emails the user wants by going to the settings page and adjusting notification preferences.
Ryanair has utilized several key strategies to become Europe's largest low-cost carrier. These include maintaining a laser focus on cost containment, operating frequent point-to-point flights on short-haul routes, and maximizing ancillary revenue sources. Ryanair keeps costs low by operating efficiently from secondary airports and maintaining a young, fuel-efficient fleet. The strategy has proven highly sustainable due to Ryanair's continued growth and profitability, though recommendations include increasing flight frequencies and opening new routes to drive further expansion.
Algorithm and Programming (Introduction of dev pascal, data type, value, and ...Adam Mukharil Bachtiar
This file contains explanation about introduction of dev pascal, data type, value, and identifier. This file was used in my Algorithm and Programming Class.
Introduction to Quantum Computing & Quantum Information TheoryRahul Mee
This document provides an introduction to quantum computing and quantum information theory. It discusses how technological limitations of conventional computing motivate the development of quantum computing. The key laws of quantum mechanics that enable quantum computing are introduced, including superposition, entanglement, and the Heisenberg uncertainty principle. The document explains how quantum bits (qubits) can represent more than the two states of classical bits, and how quantum gates operate on qubits. It provides examples of one-qubit gates like the Hadamard gate. The potential for quantum computers to massively scale parallelism through quantum effects like entanglement is also summarized.
Migrating IBM Cloud Orchestrator environment from v2.4.0.2 to v2.5.0.1Paulraj Pappaiah
The document outlines the steps to migrate an IBM Cloud Orchestrator environment from version 2.4.0.2 to 2.5.0.1. The key steps include: 1) checking prerequisites and discovering the topology, 2) migrating images between the systems, 3) exporting OpenStack data from the original system, 4) importing the data into the new IBM Cloud Manager, and 5) verifying the migrated resources on both systems and ensuring resources are no longer available on the original system.
Chapter 4: basic search algorithms data structureMahmoud Alfarra
1) The document discusses two common search algorithms: sequential search and binary search. Sequential search looks at each item in a list sequentially until the target is found. Binary search works on a sorted list and divides the search space in half at each step.
2) It provides pseudocode examples of how each algorithm works step-by-step to find a target value in a list or array.
3) Binary search is more efficient than sequential search when the list is sorted, as it can significantly reduce the number of comparisons needed to find the target. Sequential search is used when the list is unsorted.
The document discusses different types of searching algorithms. It describes sequential search which searches an unsorted list sequentially until the target item is found or the entire list is searched. The average runtime is O(n) as the item could be anywhere in the list. Binary search is described as more efficient for sorted lists, repeatedly dividing the search space in half and comparing the target to the middle element. Indexes are also summarized, including clustered vs unclustered indexes and different approaches to storing data entries.
This document discusses various searching and sorting algorithms. It begins by defining searching as finding an element in a given list. Linear search and binary search are described as two common searching algorithms. Linear search has O(n) time complexity while binary search has O(log n) time complexity but requires a sorted list. The document also discusses different sorting algorithms like bubble sort, insertion sort, and merge sort, and defines key concepts related to sorting like stability, efficiency, and passes.
The document discusses binary search and its advantages over sequential search. It explains that binary search improves search efficiency by dividing the search space in half at each step, allowing it to find an item in a sorted list in O(log n) time compared to O(n) for sequential search. The key steps of binary search are outlined, including comparing the search key to the middle element, and recursively searching either the left or right half depending on whether the key is smaller or larger than the middle element. Examples are provided to illustrate how binary search works on a sorted array to find a target value.
The document discusses different searching algorithms. It describes sequential search which compares the search key to each element in the list sequentially until a match is found. The best case is 1 comparison, average is N/2 comparisons, and worst case is N comparisons. It also describes binary search which divides the sorted list in half at each step, requiring log(N) comparisons in the average and worst cases. The document also covers indexing which structures data for efficient retrieval based on key values and includes clustered vs unclustered indexes.
The document discusses two searching algorithms: linear search and binary search. Linear search sequentially compares an element to each element in an unsorted array, making it slower than binary search. Binary search works by dividing the search space in half at each step based on comparing the target element to the middle element. It is faster than linear search but requires the array to be sorted first. Key differences are that linear search works on unsorted data while binary search requires sorted data, and binary search has better time complexity of O(log n) while linear search is O(n).
The document discusses two searching algorithms - linear search and binary search. Linear search sequentially compares the target element to each element in the array, while binary search uses a divide and conquer approach to quickly hone in on the target element in a sorted array. Both algorithms are demonstrated with pseudocode and examples.
The document discusses binary search, an efficient algorithm for finding a target value within a sorted collection. It works by repeatedly dividing the search space in half and focusing on only one subdivision, based on whether the target value is less than or greater than the middle element. This reduces the number of iterations needed. The key steps are to calculate the middle element, compare it to the target, and either search the left or right half accordingly. Binary search provides significant performance gains over linear search for large sorted datasets.
This document discusses various sorting and searching algorithms. It begins by listing sorting algorithms like selection sort, insertion sort, bubble sort, merge sort, and radix sort. It then discusses searching algorithms like linear/sequential search and binary search. It provides details on the implementation and time complexity of linear search, binary search, bubble sort, insertion sort, selection sort, and merge sort. Key points covered include the divide and conquer approach of merge sort and how binary search recursively halves the search space.
Searching algorithms are used to find elements within datasets. Sequential search linearly checks each element until a match is found, taking O(n) time on average. Interval search algorithms like binary search target the center of a sorted structure and divide the search space in half at each step, taking O(log n) time on average. Jump search checks fewer elements than linear search by jumping ahead by a fixed number of steps or block size, typically the square root of the list length. Interpolation search may check non-middle indexes based on the searched value, working best for uniformly distributed sorted data.
Algorithm and Data Structure - Linear SearchAndiNurkholis1
This material aims to enable students to:
1) Understanding searching algorithm concept
2) Understanding characteristic of linear search
3) Understanding steps of linear search
4) Knowing of advantage and disadvantage of linear search
The document discusses various searching algorithms. It introduces linear search and binary search. Linear search sequentially checks each element until the target is found or the entire array is searched. Binary search works on a sorted array by repeatedly dividing the search space in half and focusing on only one half. The best case for both is O(1) while the worst case for linear search is O(N) and for binary search is O(LogN).
1. Linear search sequentially checks each element of an array to find a target item. It adds the item to the end of the array and uses a counter to check each element until it finds a match.
2. Binary search works on a sorted array. It checks the middle element first, then searches either the left or right half depending on if the target is smaller or larger than the middle element.
3. The example demonstrates linear search finding the letter 'G' in an array and binary search locating the number 44 through a series of steps that narrow the search space.
This document provides an overview of different searching techniques including sequential search, binary search, tree searching using binary search trees, hashing techniques, and general search trees. It defines key search terminology and summarizes algorithms for common search operations like search, insertion and deletion in various data structures that support efficient searching like binary search trees, B-trees, and hash tables. Examples are provided to illustrate search techniques and how they work.
Binary search provides an efficient O(log n) solution for searching a sorted list. It works by repeatedly dividing the search space in half and focusing on only one subdivision, based on comparing the search key to the middle element. This recursively narrows down possible locations until the key is found or the entire list has been searched. Binary search mimics traversing a binary search tree built from the sorted list, with divide-and-conquer reducing the search space at each step.
This document contains a lecture on various searching algorithms, including linear search, binary search, and interpolation search. It provides descriptions of each algorithm, pseudocode, examples, and complexity analyses. Linear search has a complexity of O(n) but is simple to implement. Binary search has a better complexity of O(log n) but requires the list to be sorted. Interpolation search has an even better complexity of O(log log n) when values are evenly distributed.
1. The document discusses searching and hashing algorithms. It describes linear and binary searching techniques. Linear search has O(n) time complexity, while binary search has O(log n) time complexity for sorted arrays.
2. Hashing is described as a technique to allow O(1) access time by mapping keys to table indexes via a hash function. Separate chaining and open addressing are two common techniques for resolving collisions when different keys hash to the same index. Separate chaining uses linked lists at each table entry while open addressing probes for the next open slot.
This document discusses different types of arrays and sorting/searching algorithms in C programming. It defines one-dimensional, two-dimensional, and multi-dimensional arrays. It also explains linear search, binary search, bubble sort, and selection sort algorithms - including their applications, merits, and demerits. Key array types include static arrays declared at compile-time with a fixed size, and dynamic arrays allocated at runtime using functions like malloc().
Searching in data structure refers to the process of finding the required information from a collection of items stored as elements in the computer memory. These sets of items are in different forms, such as an array, linked list, graph, or tree. Searching means locating a particular element in a collection of elements. Search result determines whether that particular element is present in the collection or not. If it is present, we can also find out the position of that element in the given collection. Searching is an important technique in computer science.
This document provides information on various searching and sorting algorithms, including linear search, binary search, bubble sort, selection sort, and insertion sort. It begins with an overview of searching and describes linear and binary search algorithms. For linear search, it provides pseudocode and an example. For binary search, it also provides pseudocode and an example. The document then discusses sorting algorithms like bubble sort, selection sort, and insertion sort, providing descriptions, pseudocode, examples, and analyses of each.
Similar to Algorithm and Programming (Searching) (20)
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Slide ini menjelaskan perihal penggunaan komentar yang baik dan buruk pada suatu kode program. Slide ini merupakan bahan ajar untuk mata kuliah Clean Code dan Design Pattern.
Dokumen tersebut memberikan tips-tips untuk membuat nama variabel, fungsi, kelas, dan paket yang baik dalam pembuatan kode program. Beberapa tips utama adalah menggunakan nama yang jelas maksudnya, hindari penggunaan encoding, gunakan kata benda untuk nama kelas dan verba untuk nama metode, serta tambahkan konteks yang bermakna.
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Slide ini berisi penjelasan tentang Data Mining Klasifikasi. Di dalamnya ada tiga algoritma yang dibahas, yaitu: Naive Bayes, kNN, dan ID3 (Decision Tree).
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Slide ini berisi penjelasan tentang teorema-teorema yang berlaku untuk notasi asimptotik beserta cara perhitungannya untuk kebutuhan waktu suatu algoritma.
Takashi Kobayashi and Hironori Washizaki, "SWEBOK Guide and Future of SE Education," First International Symposium on the Future of Software Engineering (FUSE), June 3-6, 2024, Okinawa, Japan
Flutter is a popular open source, cross-platform framework developed by Google. In this webinar we'll explore Flutter and its architecture, delve into the Flutter Embedder and Flutter’s Dart language, discover how to leverage Flutter for embedded device development, learn about Automotive Grade Linux (AGL) and its consortium and understand the rationale behind AGL's choice of Flutter for next-gen IVI systems. Don’t miss this opportunity to discover whether Flutter is right for your project.
OpenMetadata Community Meeting - 5th June 2024OpenMetadata
The OpenMetadata Community Meeting was held on June 5th, 2024. In this meeting, we discussed about the data quality capabilities that are integrated with the Incident Manager, providing a complete solution to handle your data observability needs. Watch the end-to-end demo of the data quality features.
* How to run your own data quality framework
* What is the performance impact of running data quality frameworks
* How to run the test cases in your own ETL pipelines
* How the Incident Manager is integrated
* Get notified with alerts when test cases fail
Watch the meeting recording here - https://www.youtube.com/watch?v=UbNOje0kf6E
AI Fusion Buddy Review: Brand New, Groundbreaking Gemini-Powered AI AppGoogle
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See My Other Reviews Article:
(1) AI Genie Review: https://sumonreview.com/ai-genie-review
(2) SocioWave Review: https://sumonreview.com/sociowave-review
(3) AI Partner & Profit Review: https://sumonreview.com/ai-partner-profit-review
(4) AI Ebook Suite Review: https://sumonreview.com/ai-ebook-suite-review
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GraphSummit Paris - The art of the possible with Graph TechnologyNeo4j
Sudhir Hasbe, Chief Product Officer, Neo4j
Join us as we explore breakthrough innovations enabled by interconnected data and AI. Discover firsthand how organizations use relationships in data to uncover contextual insights and solve our most pressing challenges – from optimizing supply chains, detecting fraud, and improving customer experiences to accelerating drug discoveries.
Zoom is a comprehensive platform designed to connect individuals and teams efficiently. With its user-friendly interface and powerful features, Zoom has become a go-to solution for virtual communication and collaboration. It offers a range of tools, including virtual meetings, team chat, VoIP phone systems, online whiteboards, and AI companions, to streamline workflows and enhance productivity.
E-commerce Development Services- Hornet DynamicsHornet Dynamics
For any business hoping to succeed in the digital age, having a strong online presence is crucial. We offer Ecommerce Development Services that are customized according to your business requirements and client preferences, enabling you to create a dynamic, safe, and user-friendly online store.
A Study of Variable-Role-based Feature Enrichment in Neural Models of CodeAftab Hussain
Understanding variable roles in code has been found to be helpful by students
in learning programming -- could variable roles help deep neural models in
performing coding tasks? We do an exploratory study.
- These are slides of the talk given at InteNSE'23: The 1st International Workshop on Interpretability and Robustness in Neural Software Engineering, co-located with the 45th International Conference on Software Engineering, ICSE 2023, Melbourne Australia
Using Query Store in Azure PostgreSQL to Understand Query PerformanceGrant Fritchey
Microsoft has added an excellent new extension in PostgreSQL on their Azure Platform. This session, presented at Posette 2024, covers what Query Store is and the types of information you can get out of it.
UI5con 2024 - Keynote: Latest News about UI5 and it’s EcosystemPeter Muessig
Learn about the latest innovations in and around OpenUI5/SAPUI5: UI5 Tooling, UI5 linter, UI5 Web Components, Web Components Integration, UI5 2.x, UI5 GenAI.
Recording:
https://www.youtube.com/live/MSdGLG2zLy8?si=INxBHTqkwHhxV5Ta&t=0
Do you want Software for your Business? Visit Deuglo
Deuglo has top Software Developers in India. They are experts in software development and help design and create custom Software solutions.
Deuglo follows seven steps methods for delivering their services to their customers. They called it the Software development life cycle process (SDLC).
Requirement — Collecting the Requirements is the first Phase in the SSLC process.
Feasibility Study — after completing the requirement process they move to the design phase.
Design — in this phase, they start designing the software.
Coding — when designing is completed, the developers start coding for the software.
Testing — in this phase when the coding of the software is done the testing team will start testing.
Installation — after completion of testing, the application opens to the live server and launches!
Maintenance — after completing the software development, customers start using the software.
Hand Rolled Applicative User ValidationCode KataPhilip Schwarz
Could you use a simple piece of Scala validation code (granted, a very simplistic one too!) that you can rewrite, now and again, to refresh your basic understanding of Applicative operators <*>, <*, *>?
The goal is not to write perfect code showcasing validation, but rather, to provide a small, rough-and ready exercise to reinforce your muscle-memory.
Despite its grandiose-sounding title, this deck consists of just three slides showing the Scala 3 code to be rewritten whenever the details of the operators begin to fade away.
The code is my rough and ready translation of a Haskell user-validation program found in a book called Finding Success (and Failure) in Haskell - Fall in love with applicative functors.
E-Invoicing Implementation: A Step-by-Step Guide for Saudi Arabian CompaniesQuickdice ERP
Explore the seamless transition to e-invoicing with this comprehensive guide tailored for Saudi Arabian businesses. Navigate the process effortlessly with step-by-step instructions designed to streamline implementation and enhance efficiency.
WhatsApp offers simple, reliable, and private messaging and calling services for free worldwide. With end-to-end encryption, your personal messages and calls are secure, ensuring only you and the recipient can access them. Enjoy voice and video calls to stay connected with loved ones or colleagues. Express yourself using stickers, GIFs, or by sharing moments on Status. WhatsApp Business enables global customer outreach, facilitating sales growth and relationship building through showcasing products and services. Stay connected effortlessly with group chats for planning outings with friends or staying updated on family conversations.
UI5con 2024 - Boost Your Development Experience with UI5 Tooling ExtensionsPeter Muessig
The UI5 tooling is the development and build tooling of UI5. It is built in a modular and extensible way so that it can be easily extended by your needs. This session will showcase various tooling extensions which can boost your development experience by far so that you can really work offline, transpile your code in your project to use even newer versions of EcmaScript (than 2022 which is supported right now by the UI5 tooling), consume any npm package of your choice in your project, using different kind of proxies, and even stitching UI5 projects during development together to mimic your target environment.
8 Best Automated Android App Testing Tool and Framework in 2024.pdfkalichargn70th171
Regarding mobile operating systems, two major players dominate our thoughts: Android and iPhone. With Android leading the market, software development companies are focused on delivering apps compatible with this OS. Ensuring an app's functionality across various Android devices, OS versions, and hardware specifications is critical, making Android app testing essential.
Graspan: A Big Data System for Big Code AnalysisAftab Hussain
We built a disk-based parallel graph system, Graspan, that uses a novel edge-pair centric computation model to compute dynamic transitive closures on very large program graphs.
We implement context-sensitive pointer/alias and dataflow analyses on Graspan. An evaluation of these analyses on large codebases such as Linux shows that their Graspan implementations scale to millions of lines of code and are much simpler than their original implementations.
These analyses were used to augment the existing checkers; these augmented checkers found 132 new NULL pointer bugs and 1308 unnecessary NULL tests in Linux 4.4.0-rc5, PostgreSQL 8.3.9, and Apache httpd 2.2.18.
- Accepted in ASPLOS ‘17, Xi’an, China.
- Featured in the tutorial, Systemized Program Analyses: A Big Data Perspective on Static Analysis Scalability, ASPLOS ‘17.
- Invited for presentation at SoCal PLS ‘16.
- Invited for poster presentation at PLDI SRC ‘16.
7. WhatisSequentialSearch
• Trace group of data one by one.
• Start the process from the first data.
• If the data was found in group then stop
the searching but if not, search until the
last data in grup.
9. Ilustration of Seq. Search Without Sentinel
Given an array to be processed:
Number
Data that want to be sought : 9
• Number[1] = 9?
• Number[2] = 9?
• Number[3] = 9?
Result: 9 is found in number[3]
5 1 9 4 2
[1] [2] [3] [4] [5]
i i + 1
i i + 1
i (STOP SEARCH)
10. Sequential Search Without Sentinel
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Procedure SeqSearchTanpaSentinel (Input nama_array:tipe_array)
{I.S. : elemen array [1..maks_array] sudah terdefinisi}
{F.S. : menampilkan hasil pencarian (ditemukan/tidak)}
Kamus:
i : integer
data_cari : tipedata
Algoritma:
input(data_cari)
i 1
while(nama_array [i] ≠ data_cari) and (i < maks_array) do
i i + 1
endwhile
if (nama_array[i] = data_cari)
then
output(data_cari,’ ditemukan pada indeks ke-’,i)
else
output(data_cari,’ tidak ditemukan’)
endif
EndProcedure
11. SequentialSearchUseSentinel
• Place the data that want to be sought in
sentinel.
• Sentinel is additional index that was placed in
max array + 1.
• If the data is found in sentinel that means the
result is data is not found and vice versa.
12. Ilustration of Seq. Search Use Sentinel
Data that was sought: 9
Number
Result: Data was found in Number[3]
Data that was sought: 10
Number
Result: Data was not found
5 1 9 4 2 9
[1] [2] [3] [4] [5] [6]
sentinel
5 1 9 4 2 10
[1] [2] [3] [4] [5] [6]
sentinel
13. Sequential Search Use Sentinel
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Procedure SeqSearchSentinel (Input nama_array:tipe_array)
{I.S. : elemen array [1..maks_array] sudah terdefinisi}
{F.S. : menampilkan hasil pencarian (ditemukan/tidak)}
Kamus:
i : integer
data_cari : tipedata
Algoritma:
input(data_cari)
i 1
nama_array(maks_array + 1) data_cari
while (nama_array [i] ≠ data_cari) do
i i + 1
endwhile
if (i < maks_array+1)
then
output(data_cari,’ ditemukan pada indeks ke-’,i)
else
output(data_cari,’ tidak ditemukan’)
endif
EndProcedure
15. Ilustration of Seq. Search Use Boolean
Given an array to be processed:
Number
Data that want to be sought : 9
• Number[1] = 9?
• Number[2] = 9?
• Number[3] = 9?
Result: 9 is found in number[3]
5 1 9 4 2
[1] [2] [3] [4] [5]
FOUND FALSE
FOUND FALSE
FOUND TRUE (STOP SEARCH)
16. Sequential Search Use Sentinel
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Procedure SeqSearchBoolean (Input nama_array:tipe_array)
{I.S. : elemen array [1..maks_array] sudah terdefinisi}
{F.S. : menampilkan data yg dicari ditemukan atau tidak ditemukan}
Kamus:
i : integer
ketemu : boolean
data_cari : tipedata
Algoritma:
input(data_cari)
i 1
ketemu false
while (not ketemu) and (i ≤ maks_array) do
if(nama_var_array(i) = data_cari)
then
ketemu true
else
i i + 1
endif
endwhile
if (ketemu)
then
output(data_cari,’ ditemukan pada indeks ke-’,i)
else
output(data_cari,’ tidak ditemukan’)
endif
EndProcedure
18. WhatisBinarySearch
• Searching algorithm that divide group of data into
two parts (left and right).
• First, check data in the middle. If same with the
data that was sought then data is found. If not then
continue searching process to left or right (based
on condition).
• Group of data must be sorted before the searching
process.
19. Data that was sought: 7
Number
Result: ?
CaseExampleofBinarySearch
3 7 12 15 29
[1] [2] [3] [4] [5]
20. Case Example of Binary Search
Data that was sought: 7
Number
Result: ?
3 7 12 15 29
[1] [2] [3] [4] [5]
21. Case Example of Binary Search
Step 1 : Divide array into 2 parts. Count the middle
position (k) of array to start searching
k = (Ia + Ib) div 2
= (1 + 5) div 2
= 3
la : lower bound (for index)
lb : upper bound (for index)
3 7 12 15 29
[1] [2] [3] [4] [5]
Ia k Ib
Left Side Right Side
22. Case Example of Binary Search
Step 2 :
• check data in k. If it’s same with data that was sought then
stop search and data is found.
• If it’s not then check whether data was bigger or smaller than
data in k.
• If it’s bigger one then continue searching to right side and la
= k+1. if it’s smaller one then continue searching to the left
side and lb = k-1 (data wa sorted in ascending way).
3 7
1 2
Ia Ib
23. Case Example of Binary Search
Step 3 : repeat step 1 until step 2 until data is found or until
la>lb then stop searching.
Result : 7 is found in Number[2] and in the third looping.
3 7
1 2
Ia Ib
k
Left Side Right Side
24. Binary Search
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Procedure BinarySearch (Input nama_array : tipe_array)
{I.S. : elemen array yang terurut secara ascending sudah terdefinisi}
{F.S. : menampilkan data yg dicari ditemukan atau tidak ditemukan}
Kamus:
Ia, Ib, k : integer
ketemu : boolean
data_cari : tipedata
Algoritma:
input(data_cari)
Ia 1
Ib maks_array
ketemu false
while (not ketemu) and (Ia ≤ Ib) do
k (Ia + Ib) div 2
if (nama_var_array[k] = data_cari)
then
ketemu true
else
if (nama_var_array[k] < data_cari)
then
Ia k + 1
else
Ib k – 1
endif
endif
endwhile