3-1 Contoh Penerapan Tahapan Pengambilan Keputusan.

Presentasi berjudul: "3-1 Contoh Penerapan Tahapan Pengambilan Keputusan."— Transcript presentasi:

1 3-1 Contoh Penerapan Tahapan Pengambilan Keputusan

2 Memilih Gadget yang “Tepat” Anda sebagai konsumen telepon seluler (ponsel), tentunya ingin memiliki ponsel yang sesuai dengan kebutuhan. Kriteria: Harga? Fasilitas? Kamera? Bluetooth? Operating System? Touch screen? Pilihannya terlalu banyak –Nokia, Samsung, Sony Erricsson, Motorola, LG, Nexian, etc. Menerapkan teknik DSS untuk menyusun prioritas pilihan gadget.

3 Phase-1: Intelligence Mengumpulkan data (data management) –Sumber? –Bentuk/format data seperti apa? Sesuai dengan yang diharapkan? –Bagaimana mengolah data tersebut? Preferensi (kebutuhan) –Kriteria ponsel yang dicari? –Bagaimana mengekstraksi kriteria ponsel dari dalam basis data?

4 Phase-2: Design Menetapkan struktur model. Bagaimana mengarahkan bentuk data menjadi matriks berikut? AlternatifKriteriaNilai Keputusan ??? 1. 2. 3. Bobot ??

5 Phase-2: Design Input user –Masukkan dari user, dimulai dari menentukan kriteria. Misal: Harga: –100.000 s/d 500.000 –500.001 s/d 1.000.000 –1.000.001 s/d 2.000.000 –2.000.001 s/d 4.000.000 –Lebih dari 4.000.001 Fasilitas: –Keyboard QWERTY –Kamera: 1 Megapixel | 2 Megapixel | 5 Megapixel –Touch-screen –Operating System –Dll.

6 Phase-2: Design Bagaimana melakukan seleksi data berdasarkan kriteria yang dipilih? –Query Mapping, petakan data hasil query ke dalam tabel: –Kolom ‘Alternatif’ diisi nama/seri ponsel –Kolom ‘Kriteria’ diisi data kriteria/fasilitas

7 Phase-2: Design Contoh hasil pemetaan AlternatifKriteria HargaKamer a Teknolog i RadioMP3 Nokia A1.150r b 2 MP2GYa Samsung B1.475r b 1.5 MP3GYaTdk Motorola C1.250r b 3 MP3GTdkYa

8 Phase-2: Design Penetapan nilai Sudah berupa nilai dengan unit yang berbeda Belum berupa nilai Caranya??? AlternatifKriteria HargaKamer a Teknolog i RadioMP3 Nokia A1.150r b 2 MP2GYa Samsung B1.475r b 1.5 MP3GYaTdk Motorola C1.250r b 3 MP3GTdkYa

9 Phase-2: Design Penetapan nilai non-numerik –Gunakan skala ordinal Nilai ‘Ya’ definisikan sebagai “Suka” Nilai ‘Tidak’ definisikan sebagai “Tidak suka” –Skala: 1 : Tidak suka | 2 : Biasa saja | 3 : Suka –Maka: Ya  3 Tidak  1

10 Phase-2: Design AlternatifKriteria HargaKamer a Teknolog i RadioMP3 Nokia A1.150r b 2 MP2GYa Samsung B1.475r b 1.5 MP3GYaTdk Motorola C1.250r b 3 MP3GTdkYa AlternatifKriteria HargaKamer a Teknolog i RadioMP3 Nokia A1.150r b 2 MP2G33 Samsung B1.475r b 1.5 MP3G31 Motorola C1.250r b 3 MP3G13

11 Phase-2: Design Pemilihan metode (model management) –CPI, karena menggunakan satuan yang berbeda-beda di setiap keriteria –Penetapan bobot  Total bobot = 1 Mulai proses penghitungan AlternatifKriteria HargaKamer a Teknolog i RadioMP3 Nokia A1.150r b 2 MP2G33 Samsung B1.475r b 1.5 MP3G31 Motorola C1.250r b 3 MP3G13 BOBOT0.40.250.150.1

12 Phase-3 & 4 Phase-3 (Choice) –Urutan prioritas: 1. Motorola C (155.97) 2. Nokia A (148.33) 3. Samsung B (138.80) Phase-4 (Implementation) –Realisasi pembelian gadget.

13 OVERVIEW KOMPONEN DSS © 2005 Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang 3-13

14 © 2005 Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang 3-14 Data Management Subsystem Components: –Database –Database management system –Data directory –Query facility

15 © 2005 Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang 3-15

16 © 2005 Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang 3-16 Database Interrelated data extracted from various sources, stored for use by the organization, and queried –Internal data, usually from TPS –External data from government agencies, trade associations, market research firms, forecasting firms –Private data or guidelines used by decision-makers

17 © 2005 Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang 3-17 Database Management System Extracts data Manages data and their relationships Updates (add, delete, edit, change) Retrieves data (accesses it) Queries and manipulates data Employs data dictionary

18 © 2005 Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang 3-18 Data Directory Catalog of all data –Contains data definitions –Answers questions about the availability of data items –Source –Meaning –Allows for additions, removals, and alterations

19 © 2005 Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang 3-19 Model Management Subsystem Components: –Model base –Model base management system –Modeling language –Model directory –Model execution, integration, and command processor

20 © 2005 Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang 3-20 Models Strategic –Supports top management decisions Tactical –Used primarily by middle management to allocate resources Operational –Supports daily activities Analytical –Used to perform analysis of data

21 © 2005 Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang 3-21 Model Base Management System Functions: –Model creation –Model updates –Model data manipulation –Generation of new routines Model directory: –Catalog of models –Definitions

22 © 2005 Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang 3-22 Model Management Activities Model execution –Controls running of model Model command processor –Receives model instructions from user interface –Routes instructions to MBMS or module execution or integration functions Model integration –Combines several models’ operations

23 © 2005 Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang 3-23 User Interface System Knowledge-based system Data management and DBMS Model management and MBMS User Interface Management System (UIMS) Natural Language Processor Input Action Languages Output Display Language UsersPrinters, Plotters PC Display Based on Figure 3.6, Schematic View of the User Interface

24 © 2005 Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang 3-24 User Interface Management System GUI Natural language processor Interacts with model management and data management subsystems Examples –Speech recognition –Display panel –Tactile interfaces –Gesture interface

25 © 2005 Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang 3-25 Knowledge-Based Management System Expert or intelligent agent system component Complex problem solving Enhances operations of other components May consist of several systems Often text-oriented DSS

26 © 2005 Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang 3-26 DSS Hardware De facto standard Web server with DBMS: –Operates using browser –Data stored in variety of databases –Can be mainframe, server, workstation, or PC –Any network type –Access for mobile devices

27 © 2005 Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang 3-27 DSS Classifications Alter –Extent to which outputs can directly support or determine the decision –Data oriented or model oriented Holsapple and Whinston –Text oriented, database oriented, spreadsheet oriented, solver oriented, rule oriented, or compound Intelligent

28 © 2005 Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang 3-28 DSS Classifications Donovan and Madnick –Institutional –Problems of recurring nature Ad hoc –Problems that are not anticipated or are not repetitive Hackathorn and Keen –Personal support, group support, or organizational support

29 © 2005 Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang 3-29 DSS Classifications GSS v. Individual DSS –Decisions made by entire group or by lone decision maker Custom made v. vendor ready made –Generic DSS may be modified for use Database, models, interface, support are built in Addresses repeatable industry problems Reduces costs

30 © 2005 Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang 3-30 Web and DSS Data collection Communications Collaborations Download capabilities Run on Web servers Simplifies integration problems Increased usability features

31 © 2005 Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang 3-31