Sistem Pendukung Keputusan
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Sistem Pendukung Keputusan
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Sistem Pendukung Keputusan
- 1. 1 Pengenalan Esensi-esensi utk Sistem Informasi Eleventh Edition MODUL James A. O’Brien 9 Sistem Pendukung Keputusan Irwin/McGraw-Hill Copyright © 2002, The McGraw-Hill Companies, Inc. All rights reserved.
- 2. James A. O’Brien Pengenalan Esensi2 untuk Sistem Informasi Edisi sebelas 2 Tujuan-tujuan Modul • Meng-identifikasi perubahan2 yg terjadi dalam bentuk dan penggunaan dukungan keputusan dalam perusahaan2 e-bisnis • Meng-identifikasi alternatif2 peranan dan pelaporan sistem informasi manajemen • Memaparkan bagaimana prosesing analitikal on-line dapat memenuhi kebutuhan2 informasi penting dari para manajer • Menjelaskan konsep Sistem Pendukung Keputusan dan perbedaannya dengan sistem informasi manajemen tradisional. Irwin/McGraw-Hill Copyright © 2002, The McGraw-Hill Companies, Inc. All rights reserved.
- 3. James A. O’Brien Pengenalan Esensi2 untuk Sistem Informasi Edisi sebelas 3 Tujuan-tujuan Modul (lanjutan) • Menjelaskan bahwa sistem2 informasi berikut dapat mendukung kebutuhan2 informasi dari para eksekutif, manajer, dan profesional bisnis: – A) Sistem Informasi Eksekutif – B) Portal2 Informasi Perusahaan – C) Portal2 Pengetahuan Perusahaan • Meng-identifikasi bahwa jaringan syaraf, logika samar, algoritme2 genetik, realitas maya, dan agen2 kecerdasan dapat digunakan dalam bisnis. • Memberikan contoh2 dari cara2 sistem pakar dapat digunakan dalam situasi2 pengambilan keputusan bisnis. Irwin/McGraw-Hill Copyright © 2002, The McGraw-Hill Companies, Inc. All rights reserved.
- 4. James A. O’Brien Pengenalan Esensi2 untuk Sistem Informasi Edisi sebelas 4 Berbagai Aplikasi Pendukung Keputusan e-Bisnis Manajemen Rantai Pasokan Manajemen Hubungan Pelanggan Monitoring Kinerja Perusahaan Situasi: Analisis Skenario What-if Pegawai- Manajer/ Pelanggan/Mitra Manajemen Pengetahuan/Inovasi Irwin/McGraw-Hill Copyright © 2002, The McGraw-Hill Companies, Inc. All rights reserved.
- 5. James A. O’Brien Pengenalan Esensi2 untuk Sistem Informasi Edisi sebelas 5 Berbagai Keputusan dalam e-Bisnis Manajemen Strategik Manajemen Taktikal Manajemen Operasional Karakteristik2 Keputusan Tak terstruktur Keputusan2 Informasi Semi-terstruktur Terstruktur Irwin/McGraw-Hill Copyright © 2002, The McGraw-Hill Companies, Inc. All rights reserved.
- 6. James A. O’Brien Pengenalan Esensi2 untuk Sistem Informasi Edisi sebelas 6 Pelaporan2 Sistem Informasi Manajemen Pelaporan2 periodik yang terjadwal Pelaporan2 periodik yang terjadwal PPeellaappoorraann22 EEkksseeppssii Pelaporan Permintaan Pelaporan Permintaan dan Respon2 dan Respon2 PPeellaappoorraann22 PPuusshh Pelaporan2 Sistem Informasi Manajemen Yg Penting Irwin/McGraw-Hill Copyright © 2002, The McGraw-Hill Companies, Inc. All rights reserved.
- 7. James A. O’Brien Pengenalan Esensi2 untuk Sistem Informasi Edisi sebelas 7 Pemrosesan Analitikal On-line Data diambil dari database2 korporasi dan tahapan2 dalam suatu database multi-dimensional OLAP PC Klien Korporasi Server OLAP Database2 Database Multi-dimensional Software OLAP untuk WEB •DB Operasional •Data Marts •Warehouse Data Irwin/McGraw-Hill Copyright © 2002, The McGraw-Hill Companies, Inc. All rights reserved.
- 8. James A. O’Brien Pengenalan Esensi2 untuk Sistem Informasi Edisi sebelas 8 Sistem Pendukung Keputusan AAnnaalliissiiss WWhhaatt IIff AAnnaalliissiiss KKeeppeekkaaaann AAnnaalliissiiss GGooaall--SSeeeekkiinngg AAnnaalliissiiss OOppttiimmiissaassii Model2 Analitikal Penting dalam Sistem Pendukung Keputusan Model2 Analitikal Penting dalam Sistem Pendukung Keputusan Irwin/McGraw-Hill Copyright © 2002, The McGraw-Hill Companies, Inc. All rights reserved.
- 9. James A. O’Brien Pengenalan Esensi2 untuk Sistem Informasi Edisi sebelas 9 Portal2 Informasi Perusahaan dan SPK/DSS Internet Intranet Extranet Gateway Portal Informasi Perusahaan Interface Portal Informasi Perusahaan utk Pemakai Agen2 Penelu-suran Agen2 Penelu-suran Data Mining OOLLAAPP Data Mining Manajemen Pengetahu-an/ Inovasi Manajemen Pengetahu-an/ Inovasi DSS Model2 What-If Model2 Kepekaan Model2 Goal-Seeking Model2 Optimisasi Fungsi2 Manajemen Database Data Mart Aplikasi2 Bisnis Lainnya Database Operasional Database Analitikal Basis Pengetahuan Irwin/McGraw-Hill Copyright © 2002, The McGraw-Hill Companies, Inc. All rights reserved.
- 10. James A. O’Brien Pengenalan Esensi2 untuk Sistem Informasi Edisi sebelas 10 Atribut2 Perilaku Intelijen • Berfikir dan melakukan penalaran • Menggunakan nalar utk menjawab masalah2 • Belajar atau paham dari pengalaman • Memperoleh dan menerapkan pengetahuan • Memperlihatkan kreativitas dan imaginasi • Berurusan dgn situasi2 kompleks atau menegangkan • Respon secara cepat dan berhasil terhadap situasi2 baru. • Mengenali kepentingan nisbi dari unsur2 dalam suatu situasi • Menangani informasi yang meragukan, tak lengkap, atau mengandung kesalahan2 Irwin/McGraw-Hill Copyright © 2002, The McGraw-Hill Companies, Inc. All rights reserved.
- 11. James A. O’Brien Pengenalan Esensi2 untuk Sistem Informasi Edisi sebelas 11 Aplikasi2 Kecerdasan Buatan (AI) Aplikasi2 Sains Kognitif Aplikasi2 Sains Kognitif Kecerdasan Buatan Kecerdasan Buatan Aplikasi2 Robotik Aplikasi2 Robotik Aplikasi2 Interface Alami Aplikasi2 Interface Alami •Sistem Pakar •Logika Kabur/Fuzzy •Algoritme2 Genetik •Jaringan syaraf/ Neural Networks •Persepsi2 Visual •Locomosi •Navigasi •Taktilitas •Bahasa Manusia •Pengenalan Suara •Interface Multisensori •Realitas Maya/Virtual Irwin/McGraw-Hill Copyright © 2002, The McGraw-Hill Companies, Inc. All rights reserved.
- 12. James A. O’Brien Pengenalan Esensi2 untuk Sistem Informasi Edisi sebelas 12 Bidang2 Aplikasi Kecerdasan Buatan dalam Bisnis JJaarriinnggaann SSyyaarraaff SSiisstteemm LLooggiikkaa FFuuzzzzyy AAllggoorriittmmee22 GGeenneettiikk RReeaalliittaass MMaayyaa//VViirrttuuaall AAggeenn22 KKeecceerrddaassaann SSiisstteemm PPaakkaarr Bidang2 aplikasi Kecerdasan Buatan dalam Bisnis Bidang2 aplikasi Kecerdasan Buatan dalam Bisnis Irwin/McGraw-Hill Copyright © 2002, The McGraw-Hill Companies, Inc. All rights reserved.
- 13. James A. O’Brien Pengenalan Esensi2 untuk Sistem Informasi Edisi sebelas 13 Agen-agen Kecerdasan Tutor2 Interface Agen2 Presentasi Agen2 Navigasi Jaringan Agen2 yang Memainkan- Peranan Agen2 Interface Pemakai Agen2 Manajemen Informasi Agen2 Penelusuran Perantara2 Informasi Saringan2 Informasi Irwin/McGraw-Hill Copyright © 2002, The McGraw-Hill Companies, Inc. All rights reserved.
- 14. James A. O’Brien Pengenalan Esensi2 untuk Sistem Informasi Edisi sebelas 14 Komponen2 Sistem Pakar SSiisstteemm PPaakkaarr Basis Pe-ngetahuan Saran2 Pakar Pemakai Workstation Program2 Interface Pemakai Program Mesin Inferensi PPeennggeemmbbaannggaann SSiisstteemm PPaakkaarr Injiniring Pengetahuan Workstation Program Penyedia Pengetahuan Pakar dan/atau Injinir Pengetahuan Irwin/McGraw-Hill Copyright © 2002, The McGraw-Hill Companies, Inc. All rights reserved.
- 15. James A. O’Brien Pengenalan Esensi2 untuk Sistem Informasi Edisi sebelas 15 Aplikasi2 Sistem Pakar MMaannaajjeemmeenn K Keeppuuttuussaann DDiiaaggnnoossttiikk//TTrroouubblelesshhoooottiinngg PPeemmeellihihaarraaaann//PPeennjajaddwwaalalann DDeessaaiinn//KKoonnffiigguurraassii SSeelleekkssii//KKllaassiiffiikkaassii Kategori2 Aplikasi Utama Sistem Pakar MMoonnititoorriinngg//KKoonnttrrooll PPrroosseess Irwin/McGraw-Hill Copyright © 2002, The McGraw-Hill Companies, Inc. All rights reserved.
- 16. James A. O’Brien Pengenalan Esensi2 untuk Sistem Informasi Edisi sebelas 16 Rangkuman Modul • Sistem Pendukung Keputusan dlm bisnis selalu berubah. Tumbuhnya intranet2, ekstranet2, dan lain2 teknologi web untuk perusahaan telah mempertinggi permintaan berbagai teknik analitikal yg dipersonalisasikan, proaktif, dan web-enabled utk mendukung DSS. • Sistem Informasi harus mendukung berbagai tingkat pengambilan keputusan manajemen dan keputusan2 itu sendiri. Ini mencakup kegiatan tiga tingkatan manajemen: strategik, taktikal, dan operasional. Irwin/McGraw-Hill Copyright © 2002, The McGraw-Hill Companies, Inc. All rights reserved.
- 17. James A. O’Brien Pengenalan Esensi2 untuk Sistem Informasi Edisi sebelas 17 Rangkuman Modul (lanjutan) • Prosesing analitikal on-line digunakan untuk menganalisis hubungan2 yg rumit diantara data dalam jumlah besar yg tersimpan dalam database2 multidimensional. Data mining menganalisis data historikal yang amat banyak yang terkandung dalam warehouse2 data. • Sistem Pendukung Keputusan adalah sistem informasi interaktif berbasis-komputer yg menggunakan software DSS dan suatu basis model, yang menyediakan informasi untuk mendukung pengambilan keputusan semi-terstruktur dan tak-terstruktur. Irwin/McGraw-Hill Copyright © 2002, The McGraw-Hill Companies, Inc. All rights reserved.
- 18. James A. O’Brien Pengenalan Esensi2 untuk Sistem Informasi Edisi sebelas 18 Rangkuman Modul (lanjutan) • Domain2 aplikasi utama dalam kecerdasan buatan mencakup sejumlah aplikasi dalam sains2 kognitif, robotik, dan interface2 alami. • Bidang2 aplikasi utama AI mencakup: – Jaringan Syaraf (Neural Networks) – Logika Samar (Fuzzy Logic) – Algoritme2 Genetik (Genetic Algorithms) – Realitas Maya (Virtual Reality) – Agen2 Kecerdasan (Intelligent Agents) Irwin/McGraw-Hill Copyright © 2002, The McGraw-Hill Companies, Inc. All rights reserved.
Editor's Notes
- Using information systems to support business decision making has been one of the primary thrusts of the business use of information technology. However, the e-commerce revolution spawned by the Internet and the World Wide Web is expanding the information and decision support uses and expectations of a company’s employees, managers, customers, suppliers, and other business partners. The figure highlights several of the major e-business decision support applications that are being customized, personalized, and Web-enabled for use in e-business and e-commerce. These e-business decision support applications are being rapidly made available to employees, managers, customers, suppliers, and other business partners of an internetworked e-business enterprise. Teaching Tips This slide corresponds to Figure 9.3 on pp. 295 and relates to the material on pp. 294.
- To succeed in e-business and e-commerce, companies need information systems that can support the diverse information and decision making needs of business professionals. The type of information required by decision makers in a company is directly related to the level of management and the amount of structure in the decision situation. Strategic Planning and Control. Top executives develop overall organizational goals, strategies, policies, and objectives through long-range strategic planning. They also monitor the strategic performance of the organization and its overall direction. As a result, they are typically involved in making unstructured decisions; that is decisions where decision procedures to be followed cannot be specified in advance. Tactical Planning and Control. Middle managers develop short- and medium-range plans and budgets and specify the policies, procedures, and objectives for subunits of the organization. They also acquire and allocate resources and monitor performance of organizational subunits at the department, division, and other workgroup levels. Hence, these managers make more semi-structured decisions in which only some of the decision procedures can be specified in advance. Operational Planning and Control. Supervisory managers develop short-term planning devices such as production schedules. Supervisors are front-line managers who direct the actions of non-management employees. Their IS needs are often linked to the processing, monitoring, and evaluating of physical products. Thus, their decisions are more structured; that is to say, they can be specified in advance. Teaching Tips This slide corresponds to Figure 9.4 on pp. 296 and relates to the material on pp. 295-296.
- The Management Information System concept, also called information reporting systems, was the original type of management support system. MIS produce information products that support many of the day-to-day decision-making needs of the organization. Three major reporting alternatives include: Periodic Scheduled Reports. This traditional form of providing information to managers uses a prespecified format designed to provide managers with information on a regular basis. Typical examples include weekly sales analysis reports and monthly financial statements. Exception Reports. These are generated when a specific set of conditions occur. The IS can be designed to produce exception reports when some process exceeds given parameters and requires management action. Exception reports reduce information overload. They also promote management by exception -- intervening only when decisions need to be made. Demand Reports and Responses. These provide information whenever a manager demands it. For example, DBMS query languages and report generators allow managers at online workstations to get immediate responses or reports to their requests for information. Push Reporting. Many companies are using webcasting software to selectively broadcast reports and other information to the networked PCs of managers and specialists over their corporate intranets. In this manner, information is pushed to a manager’s networked workstation. Teaching Tips This slide corresponds to the material on pp. 297-298.
- Online Analytical Processing (OLAP) is a capability of management, decision support, and executive information systems that enables managers and analysts to interactively examine and manipulate large amounts of detailed and consolidated data from many perspectives. Basic analytical operations include: Consolidation. This involves the aggregation of data. It can be simple roll-ups or complex groupings involving interrelated data. For example, sales offices can be rolled up to districts and districts rolled up to regions. Drill-Down. OLAP can go in the reverse direction and automatically display detailed data that comprises consolidated data. For example, the sales by individual products or sales reps that make up a region's sales can be accessed easily. Slicing and Dicing. This refers to the ability to look at the database from different viewpoints. For example, one slice of a database might show all sales of a product within regions. Another slice might show all sales by sales channel. By allowing rapid alternative perspectives, slicing and dicing allows managers to isolate the information of interest for decision making. Teaching Tips This slide corresponds to Figure 9.7 pp. 299 and relates to the material on pp. 298-299.
- Decision support systems (DSS) are computer-based systems that provide managers and business professionals interactive information support for semi-structured and unstructured decisions. Unlike management information systems, DSS rely on model bases. A model base is a software component that consists of models used in computational and analytical routines that mathematically express relationships between variables. There are various types of DSS analytical model bases. These include: What-If Analysis. An end user makes changes to variables, or relationships among variables, and observes the resulting change in the value of other variables. Sensitivity Analysis. A special type of what-if analysis in which the value of only one variable is changed repeatedly, and the resulting changes on other variables are observed. Goal-Seeking Analysis. Instead of observing how changes in a variable affect other variables, goal-seeking analysis sets a target value for a variable, and then repeatedly changes other variables until the target value is achieved. Optimization analysis. A more complex goal-seeking model. Instead of setting a specific target value for a variable, the goal is to find the optimum value for one or more target variables, given certain constraints. Teaching Tips This slide corresponds to Figure 9.13 on pp. 304 and relates to the material on pp. 303-305.
- Cross-platform integration is one of the main objectives of today’s e-business. As shown in the figure, newer DSS packages not only are capable of running under different computer platforms, but can be integrated with corporate data resources, including operational databases, data marts, and data warehouses. These packages are no longer limited to numeric input and response, but can use data visualization systems to represent complex data using interactive three dimensional graphical forms. This in turns helps users discover patterns and links between decision variables quicker and easier. As we stated earlier, the objective of today’s e-business is to provide information to anyone that needs it, whenever, and wherever they are. More and more companies are developing Enterprise Information Portals to provide web-enabled access to information. When deployed successfully, this portal provides a universal interface to both corporate knowledge and decision-making tools as well as a wealth of other tools. Teaching Tips This slide corresponds to Figure 9.18 on pp. 310 and relates to the material on pp. 309.
- Artificial intelligence (AI) is a field of science and technology based on disciplines such as computer science, biology, psychology, linguistics, mathematics, and engineering. The goal of AI is to develop computers that can think, as well as see, hear, walk, talk, and feel. A major thrust of AI is the development of computer functions normally associated with human intelligence, such as reasoning, learning, and problem solving. Alan Turing in 1950 proposed a test for determining if machines could think. According to the Turing test, a computer could demonstrate intelligence if a human interviewer, conversing with an unseen human and an unseen computer, could not tell which was which. Critics believe that no computer can truly pass the Turing test. They claim that developing intelligence to impart true humanlike capabilities to computers is simply not possible. But progress continues, and only time will tell if the ambitious goals of AI will be achieved. Teaching Tips This slide corresponds to Figure 9.21 on pp. 315 and relates to the material on pp. 315.
- Artificial Intelligence (AI) is a science and technology based on disciplines such as computer science, biology, psychology, linguistics, mathematics, and engineering. AI works to develop computer functions normally associated with human intelligence. Its goal is to develop computers that can think, see, hear, walk, talk, and even feel. The major application areas of AI can be grouped into three categories: Cognitive Science. Much of AI development is based upon research in human information processing, which focuses on understanding how the human brain works and how humans think and learn. Major applications in this area include: expert systems, learning systems, fuzzy logic, genetic algorithms, neural networks, and intelligent agents. Robotics. Robotics is concerned with deploying computers in ways that duplicate the actions (and even the appearance) of humans. Areas of development include visual perception, tactility, dexterity, locomotion, and navigation. Natural Interface. AI developers hope to make the human-computer interface as natural as possible. Natural language programming, speech recognition, multisensory interfaces, and virtual reality are all areas of development. Teaching Tips This slide corresponds to Figure 9.22 on pp. 316 and relates to the material on pp. 315-325.
- There are numerous AI application areas in business. These include: Neural Networks. Computing systems modeled after the brain’s mesh-like network of interconnected processing elements, called neurons. The interconnected processors in a neural network operate in parallel and interact dynamically. This enables the network to learn to recognize patterns and relationships in the data it processes. For example, a neural network can be used to learn which credit characteristics result in good or bad loans. Fuzzy Logic. A method of reasoning that allows for approximate values and inferences. This enables fuzzy systems to process incomplete data and quickly provide approximate, but acceptable solutions. Fuzzy systems are used in fuzzy process controller microchips that are incorporated in many Japanese appliances. Genetic Algorithms. Uses Darwinian randomizing and other mathematical functions to simulate an evolutionary process that yields increasingly better solutions to a problem. They are especially useful for situations in which thousands of solutions are possible and must be evaluated to produce an optimal solution. Virtual Reality. Is a computer-simulated reality that uses such devices as tracking headsets and data gloves to create virtual worlds that can be experienced through sight, sound, and touch. Current applications of virtual reality include computer-aided design, medical diagnostics, flight simulation, and 3-D video arcade games. On the next two slides we will focus on two very popular AI business areas. Teaching Tips This slide corresponds to Figure 9.22 on pp. 316 and relates to the material on pp. 316-327.
- An Intelligent Agent (IA) is a software surrogate that fulfills a stated need or activity. The IA uses built-in and learned knowledge about how an end user behaves or in answer to posed questions, to implement a software solution -- such as the design of a presentation template or spreadsheet -- to solve a specific problem of interest to the end user. IAs can be grouped into two categories for business computing: User Interface Agents. Interface Tutors. These observe computer operations, correct user mistakes, and provide hints and advice on efficient software use. Presentation Agents. These show information in a variety of reporting and presentation forms and media based on user preferences. Network Navigation Agents. These discover paths to information and provide ways to view information that are preferred by a user. Role-Playing Agents. These play what-if games and other roles to help users understand information and make better decisions. Information Management Agents. Search Agents. These help users find files and databases, search for desired information, and suggest and find new types of information products, media, and resources. Information Brokers. These provide commercial services to discover and develop information resources that fit the business or personal needs of a user. Information Filters. These receive, find, filter, discard, save, forward, and notify users about products received or desired, including E-mail, voice mail, and all other information media. Teaching Tips This slide corresponds to Figure 9.28 on pp. 323 and relates to the material on pp. 323-324.
- An Expert System (ES) is a knowledge-based information system that uses its knowledge about a specific, complex application area to act as an expert consultant to end users. The components of an ES include: Knowledge Base. A knowledge base contains knowledge needed to implement the task. There are two basic types of knowledge: Factual knowledge. Facts, or descriptive information, about a specific subject area. Heuristics. A rule of thumb for applying facts and/or making inferences, usually expressed as rules. Inference Engine. An inference engine provides the ES with its reasoning capabilities. The inference engine processes the knowledge related to a specific problem. It then makes associations and inferences resulting in recommended courses of action. User Interface. This is the means for user interactions. To create an expert system a knowledge engineer acquires the task knowledge from the human expert using knowledge acquisition tools. Using an expert system shell, which contains the user interface and inference engine software modules, the KE then encodes the knowledge into the knowledge base. A reiterative approach is used to test and refine the expert system's knowledge base until it is deemed complete. Teaching Tips This slide corresponds to Figure 9.30 on pp. 325 and relates to the material on pp. 323-330.
- Expert Systems can be used to accomplish many business tasks: Decision Management. This includes systems that appraise situations or consider alternatives and make recommendations based on criteria supplied during the discovery process. Examples include loan portfolio analysis, employee evaluation, insurance underwriting, demographic forecasts. Diagnostic/Troubleshooting. This is the use of systems that infer underlying causes from reported symptoms and history. Examples include equipment calibration, help desk operations, software debugging, medical diagnosis. Maintenance/Scheduling. This includes systems that prioritize and schedule limited or time-critical resources. Examples include maintenance scheduling, production scheduling, education scheduling, project management. Design/Configuration. This is the use of systems that help configure equipment components, given existing constraints that must be taken into account. Examples include computer option installation, manufacturability studies, communications networks, optimum assembly plan. Selection/Classification. These are systems that help users choose products or processes from among large or complex sets of alternatives. Examples include material selection, delinquent account identification, information classification, suspect identification. Process Monitoring/Control. This includes systems that monitor and control procedures or processes. Examples include machine control (including robotics), inventory control, production monitoring, chemical testing. Expert systems provide a business with faster, consistent expertise. They also help preserve organizational knowledge. However, they are not without limitations. ES are not suitable for every problem situation. They excel only in solving specific types of problems in a limited domain of knowledge. They fail to solve problems requiring a broad knowledge base. Expert Systems are also difficult and costly to develop and maintain. Teaching Tips This slide corresponds to Figure 9.33 on pp. 328 and relates to the material on pp. 325-330.