SISTEM PENUNJANG
KEPUTUSAN
IF041 - 3
© 2009 Fakultas Teknologi Informasi Universitas Budi Luhur
Jl. Ciledug Raya Petukangan Utara Jakarta Selatan 12260
Website: http://fti.bl.ac.id Email: sekretariat_fti@bl.ac.id
Turban, Aronson, and Liang
Decision Support Systems and Intelligent Systems,
Seventh Edition
PERTEMUAN-4
CHAPTER 4
MODELING AND ANALYSIS
Tujuan Pembelajaran
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Memahami konsep dasar MSS modeling.
Menjelaskan interaksi MSS models.
Memahami model class yang berbeda.
Menyusun pengambilan keputusan dari beberapa
alternatif.
 Mempelajari bagaimana menggunakan spreadsheets
dalam MSS modeling.
 Memahami konsep optimization, simulation, dan
heuristics.
 Mempelajari untuk menyusun linear program
modeling.
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Tujuan Pembelajaran
 Memahami kemampuan linear programming.
 Mengkaji metode pencarian untuk MSS models.
 Menentukan perbedaan antara algorithms, blind
search, heuristics.
 Menangani multiple goals.
 Memahami sensitivity, automatic, what-if analysis,
goal seeking.
 Mengetahui topik utama dari model management.
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 Caution:
Not to master the topics, but gaining
familiarity with the important concepts
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Dupont Simulates Rail Transportation System and
Avoids Costly Capital Expense Vignette
 Simulasi Promodel dibuat untuk memberikan
gambaran mengenai sistem transportasi.
 Menerapkan what-if analyses
 Visual simulation
 Mengidentifikasi beragam kondisi
 Mengidentifikasi kemacetan
 Memungkinkan untuk menurunkan jumlah
armada tanpa mengurangi jumlah yang
diantarkan
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MSS Modeling
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Elemen utama kedua dalam DSS, setelah database
Berbagai jenis model
Setiap model memiliki teknik yang berbeda
Memungkinkan adanya pengkajian ulang untuk
alternatif solusi
 Seringkali sebuah DSS melibatkan Multiple models
 Trend menuju transparansi
 Multidimensional modeling ditunjukkan seperti halnya
spreadsheet
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Simulasi
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Menelusuri masalah
Mengidentifikasi alternatif solusi
Dapat berorientasi obyek
Meningkatkan proses pengambilan keputusan
Memberikan gambaran dampak dari alternatif
keputusan
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DSS Models
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Algorithm-based models
Statistic-based models
Linear programming models
Graphical models
Quantitative models
Qualitative models
Simulation models
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Identifikasi Masalah
 Memahami dan menganalisa lingkungan luar
 Should identify the organizational culture and the corporate decisionmaking processes (who makes decisions, degree of centralization, and
so on).
 Mengidentifikasi variable dan hubungan
 Variabel  decision, result, uncontrollable, and others.
 Influence diagrams
 Cognitive maps
 Forecasting
 Essential for construction and manipulation of models because when a
decision is implemented, the results usually occur in the future.
 Ditingkatkan dengan e-commerce
 Meningkatkan jumlah informasi yang tersedia melalui teknologi
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Kategori Model
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Static Models
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Gambaran sederhana dari situasi
Single interval
Time can be rolled forward, a photo at a time
Biasanya berulang  repeat with identical conditions
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Dynamic Model
 Merepresentasikan situasi yang kerap berubah
 Ex: 5-year profit-and-loss projection in which the input data, such as
costs, prices, and quantities, change from year to year.
 Time dependent
 In determining how many checkout points should be open in a
supermarket, it is important to take into consideration the time of day
because different numbers of customers arrive during each hour.
 Kondisi yang beragam
 Generate dan mewakili trends atau pola yang terjadi pada waktu tertentu.
 They also show averages per period, moving averages, and
comparative analyses (e.g., profit this quarter against profit in the
same quarter of last year).
 Suatu kejadian mungkin saja tak berulang
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Decision-Making
 Certainty (Kepastian)
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Diasumsikan sebagai knowledge utuh
Dapat mengetahui semua hasil yang potensial
Mudah digunakan
Dapat menentukan solusi ulang dengan mudah
Sangat kompleks
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Decision-Making
 Uncertainty (Ketidak pastian)
 Beberapa hasil untuk setiap keputusan
 Kemungkinan yang terjadi untuk setiap hasil tidak
dapat diketahui
 Informasi yang tidak mencukupi
 Membutuhkan resiko dan keinginan untuk
mengambil resiko
 Pendekatan Pessimistic/optimistic
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Decision-Making
 Probabilistic Decision-Making
 Keputusan yang beresiko
 Probabilitas dari beberapa hasil yang
memungkinkan bisa saja terjadi
 Analisa Resiko
 Menghitung nilai untuk setiap alternatif
 Memilih nilai terbaik
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Influence Diagrams
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Model disajikan dengan grafis
Menyediakan relationship framework
Menguji ketergantungan antar variabel
Semua level disajikan detail
Menunjukkan dampak perubahan
Menunjukkan what-if analysis
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Influence Diagrams
Variables:
Decision
Intermediate
atau
uncontrollable
Result atau
outcome
(intermediate atau
final)
Tanda panah mengindikasikan jenis hubungan dan arah dari pengaruh
Certainty
Amount
in CDs
Interest
earned
Sales
Uncertainty
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Price
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Influence Diagrams
Random (risk)
~
Demand
Sales
Place tilde above
variable’s name
Preference
(double line arrow)
Sleep all
day
Graduate
University
Get job
Ski all
day
Anak panah bisa satu atau dua arah, tergantung pada arah
dari pengaruh
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An Influence Diagram For Profit Model
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Modeling dengan Spreadsheets
 Fleksibel dan mudah
 End-user modeling tool
 Memungkinkan penggunaan linear
programming dan analisa regresi
 Meliputi what-if analysis, data management,
macros
 Sempurna dan transparan
 Memasukkan Model Statis dan Dinamis
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Decision Tables
 Analisa keputusan untuk multi kriteria (multiple goals)
 Meliputi:
 Decision variables (alternatif)
 Uncontrollable variables (Variabel tak terkontrol, contoh:
the states of economy)
 Result variables (Variabel Hasil, contoh:projected yield)
 Menerapkan prinsip-prinsip certainty, uncertainty, and risk
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Decision Tree
 Penggambaran dari beberapa hubungan
 Pendekatan multi kriteria
 Menunjukkan hubungan yang kompleks ke dalam bentuk yang
lebih ringkas
 Tidak praktis, bila terlalu banyak alternatif
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Optimization via Mathematical
Programming
 Linear programming (LP)
Used extensively in DSS
 Mathematical Programming
Family of tools to solve managerial problems in
allocating scarce resources among various
activities to optimize a measurable goal
Ex: Distribution of machine time (the resource) among
various products (the activities)
Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson, 6th edition
Copyright 2001, Prentice
Hall, Upper Saddle River, NJ
25
LP Allocation
Problem Characteristics
1. Limited quantity of economic resources
2. Resources are used in the production of
products or services
3. Two or more ways (solutions, programs) to use
the resources
4. Each activity (product or service) yields a return
in terms of the stated goal
5. Allocation is usually restricted by constraints
Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson, 6th edition
Copyright 2001, Prentice
Hall, Upper Saddle River, NJ
26
LP Allocation Model
 LP allocation model is based on the following rational
economics assumptions:
1. Returns from allocations can be compared in a common unit
2. The return from any allocation is independent of other
allocations
3. Total return is the sum of different activities’ returns
4. All data are known with certainty
5. The resources are to be used in the most economical manner
 Optimal solution: the best, found algorithmically
Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson, 6th edition
Copyright 2001, Prentice
Hall, Upper Saddle River, NJ
27
Linear Programming
LP Problem is composed of:
 Decision variables  whose values are unknown and are
searched for
 Objective function  a linear mathematical function that relates
the decision variables to the goal and measures goal attainment
and is to be optimized
 Objective function coefficients  unit profit or cost coefficients
indicating the contribution to the objective of one unit of a
decision variable
 Constraints
 Capacities  upper and/or lower limits on the constraints and
variables
 Input-output (technology) coefficients
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Heuristic Programming
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Cuts the search
Gets satisfactory solutions more quickly and less expensively
Finds rules to solve complex problems
Finds good enough feasible solutions to complex problems
Heuristics can be
 Quantitative
 Qualitative (in ES)
Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson, 6th edition
Copyright 2001, Prentice
Hall, Upper Saddle River, NJ
29
When to Use Heuristics
1. Inexact or limited input data
2. Complex reality (that optimization models cannot be used)
3. Dapat diandalkan, exact algorithm not available
4. Computation time excessive
5. To improve the efficiency of optimization (ex: by producing
good starting solutions)
6. To solve complex problems
7. For symbolic processing (as in expert system)
8. For making quick decisions
Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson, 6th edition
Copyright 2001, Prentice
Hall, Upper Saddle River, NJ
30
Simulation
 Technique for conducting experiments with a computer on a
model of a management system
 Frequently used DSS tool
Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson, 6th edition
Copyright 2001, Prentice
Hall, Upper Saddle River, NJ
31
Major Characteristics of Simulation
 Imitates reality and capture its richness
 Technique for conducting experiments
 Descriptive, not normative tool  no automatic search for an
optimal solution
 Describes or predicts the characteristics of a given system
under different conditions
 Often to solve very complex, risky problems  cannot be
formulated for optimization
Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson, 6th edition
Copyright 2001, Prentice
Hall, Upper Saddle River, NJ
32
Simulation Methodology
Model real system and conduct repetitive experiments
1. Define problem  why a simulation approach is
appropriate
2. Construct simulation model  determine variables and
their relationships, data gathering
3. Test and validate model
4. Design experiments
5. Conduct experiments
6. Evaluate results
7. Implement solution
Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson, 6th edition
Copyright 2001, Prentice
Hall, Upper Saddle River, NJ
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