Contoh Penerapan
Tahapan Pengambilan
Keputusan
3-1
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.
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?
Phase-2: Design
• Menetapkan struktur model.
• Bagaimana mengarahkan bentuk
data menjadi matriks berikut?
Alternatif
Kriteria
Nilai Keputusan
???
1.
2.
3.
Bobot ??
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.
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
Phase-2: Design
• Contoh hasil pemetaan
Alternatif
Kriteria
Harga
Kamer
a
Teknolog
i
Radio
MP3
Nokia A
1.150r
b
2 MP
2G
Ya
Ya
Samsung B
1.475r
b
1.5 MP
3G
Ya
Tdk
Motorola C
1.250r
b
3 MP
3G
Tdk
Ya
Phase-2: Design
• Penetapan nilai
Alternatif
Kriteria
Harga
Kamer
a
Teknolog
i
Radio
MP3
Nokia A
1.150r
b
2 MP
2G
Ya
Ya
Samsung B
1.475r
b
1.5 MP
3G
Ya
Tdk
Motorola C
1.250r
3 MP
3G
Tdk
Ya
Sudah
berupa
nilai
Belum
berupa
nilai
b
dengan unit yang berbeda
Caranya???
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
Phase-2: Design
Alternatif
Kriteria
Harga
Kamer
a
Teknolog
i
Radio
MP3
Nokia A
1.150r
b
2 MP
2G
Ya
Ya
Samsung B
1.475r
b
1.5 MP
3G
Ya
Tdk
Motorola C
Alternatif
1.250r
b
3 MP
Tdk
Ya
Harga
Kamer
a
Teknolog
i
Radio
MP3
Nokia A
1.150r
b
2 MP
2G
3
3
Samsung B
1.475r
b
1.5 MP
3G
3
1
Motorola C
1.250r
3 MP
3G
1
3
3G
Kriteria
Phase-2: Design
• Pemilihan metode (model management)
– CPI, karena menggunakan satuan yang berbeda-beda di
setiap keriteria
– Penetapan bobot  Total bobot = 1
Alternatif
Kriteria
Harga
Kamer
a
Teknolog
i
Radio
MP3
Nokia A
1.150r
b
2 MP
2G
3
3
Samsung B
1.475r
b
1.5 MP
3G
3
1
C 1.250r
3 MP
3G
• Motorola
Mulai proses
penghitungan
1
3
0.1
0.1
b
BOBOT
0.4
0.25
0.15
Phase-3 & 4
• Phase-3 (Choice)
– Urutan prioritas:
• 1. Motorola C
• 2. Nokia A
• 3. Samsung B
(155.97)
(148.33)
(138.80)
• Phase-4 (Implementation)
– Realisasi pembelian gadget.
• OVERVIEW KOMPONEN DSS
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Data Management Subsystem
• Components:
– Database
– Database management system
– Data directory
– Query facility
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© 2005 Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition,
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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
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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
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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
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Model Management Subsystem
• Components:
– Model base
– Model base management system
– Modeling language
– Model directory
– Model execution, integration, and
command processor
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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
© 2005 Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition,
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Model Base Management System
• Functions:
– Model creation
– Model updates
– Model data manipulation
– Generation of new routines
• Model directory:
– Catalog of models
– Definitions
© 2005 Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition,
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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
© 2005 Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition,
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User Interface System
Data management
and DBMS
Knowledge-based
system
Model
management and
MBMS
User Interface Management System (UIMS)
Natural Language Processor
Input
Action
Languages
Based on Figure 3.6, Schematic
View of the User Interface
Users
Output
Display
Language
PC Display
Printers, Plotters
© 2005 Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition,
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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
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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
© 2005 Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition,
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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
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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
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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
© 2005 Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition,
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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
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Web and DSS
•
•
•
•
•
•
•
Data collection
Communications
Collaborations
Download capabilities
Run on Web servers
Simplifies integration problems
Increased usability features
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