Information required at different management levels

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Konsep Sistem Informasi

Pertemuan 6

Decision Support Systems

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Sistem

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Levels of Management Decision

Making

• Strategic management

–eksekutif mengembangkan tujuan, strategi,

kebijakan dan sasaran organisasi

–Sebagai bagian dari strategic planning process

• Tactical management

–manajer dan profesional bisnis dalam tim yang

bekerja sendiri

–Membangun rencana jangka pendek dan

menengah, jadwal kerja dan anggaran

–menentukan prosedur, kebijakan dan tujuan

bisnis untuk subunit mereka

tys@stikom.edu - ningtyas78@gmail.com

Levels of Management Decision

Making

• Operational management

–manajer atau anggota tim bekerja sendiri

–Membuat rencana jangka pendek seperti jadwal produksi mingguan

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Information Quality

• Produk dari informasi yang mempunyai

karakteristik, atribut atau kualitas Yng

memnjadikan informsi lebih berarti

• Informasi mempunyai tiga dimensi, yaitu:

–Time / waktu

–Content / isi

–Form / bentuk

Attributes of Information Quality

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Decision Structure

• Structured – situasi di mana prosedur yang harus diikuti pada saat keputusan sangat dibutuhkan dapat ditentukan terlebih dahulu

• Unstructured – situasi dimana tidak mungkin untuk menentukan terlebih dahulu prosedur pengambilan keputusan untuk menentukan keputusan

• Semi structured - prosedur pengambilan keputusan yang dapat ditentukan terlebih dahulu, tetapi tidak cukup untuk menghasilkan keputusan yang direkomendasikan

Information Systems to support decisions

Management

Information Systems

Decision Support Systems

Decision support provided

Provide information about the performance of the

organization

Provide information and techniques to analyze specific problems

Information form and frequency

Periodic, exception, demand, and push reports and responses

Interactive inquiries and responses

Information format

Prespecified, fixed format Ad hoc, flexible, and

adaptable format

Information processing methodology

Information produced by extraction and manipulation of business data

Information produced by analytical modeling of business data

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Decision Support Trends

• Personalisasi analisis keputusan secara

proaktif

• Web-Based applications

• Keputusan di tingkat lebih rendah dari

manajemen dan oleh tim dan perorangan

• Business intelligence applications

Business Intelligence Applications

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Decision Support Systems

• DSS

–Memberikan dukungan informasi interaktif untuk

manajer dan profesional bisnis selama proses pengambilan keputusan

–Menggunakan :

• Analytical models • Specialized databases

• A decision maker’s own insights and judgments • Interactive computer-based modeling

–Mendukung semi structured business decisions

Decision Support Systems

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DSS components

DSS Model base

• Model base

–Sebuah komponen perangkat lunak yang terdiri dari model yang digunakan dalam rutinitas komputasi dan analisis matematis mengungkapkan hubungan antar variabel

–Examples:

• Linear programming models,

• Multiple regression forecasting models

• Capital budgeting present value models

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Management Information

Systems

• MIS

–Menghasilkan produk informasi yang

mendukung kebutuhan pengambilan

keputusan setiap hari oleh manajer dan profesional bisnis

–Laporan yang ditetapkan sebelumnya, menampilkan informasi dan tanggapan dari informasi

–Mendukung structured decisions

http://www.prism-world.com/Shared/Media/Images/ContentImages/KPI%20Dashboard%20Screen%20Shot.JPG

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MIS Reporting Alternatives

• Periodic Scheduled Reports

–Pre specified format on a regular basis

• Exception Reports

–Reports about exceptional conditions

–May be produced regularly or when exception

occurs

• Demand Reports and Responses

–Information available when demanded

• Push Reporting

–Information pushed to manager

Online Analytical Processing

• OLAP

–Memungkinkan para manager and analysts untuk meneliti dan memanipulasi banyak data detail dan penggabungan dari berbagai sudut pandang

–dilakukan secara interaktif secara real time dengan respon cepat

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Online Analytical Processing

http://www.files32.com/images/radarcube_asp_net_olap_control_for_ms_analysis-1731-scr.jpeg

OLAP Analytical Operations

• Consolidation

–Aggregation of data

• Drill-down

–Display detail data that comprise consolidated data

• Slicing and Dicing

–Ability to look at the database from different viewpoints

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OLAP Technology

Geographic Information Systems

• GIS

–DSS yang menggunakan geographic databases untuk menyusun dan

menampilkan peta dan tampilan gambar lainnya

–Mendukung keputusan yang berpengaruh pada distribusi geografis orang-orang dan sumber daya lainnya

–Biasa disebut dengan Global Position Systems (GPS) devices

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Geographic Information Systems

http://proceedings.esri.com/library/userconf/proc96/TO150 /PAP150/DS-APP.GIF

Data Visualization Systems

• DVS

–DSS yang mewakili data kompleks dengan menggunakan bentuk grafis interaktif tiga dimensi seperti bagan, grafik, dan peta

–Peralatan DVS membantu pengguna untuk secara interaktif menyortir, membagi,

menggabungkan, dan mengatur data sementara itu dalam bentuk grafik nya.

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BioCichlid

BioCichlid 3D hierarchical visualization of an RNAPII-related network of yeast. Blue nodes indicate proteins while red nodes indicate genes. Blue edges indicate physical interactions, red edges indicate genetic interactions and yellow edges indicate transcriptional regulations from transcription factors to genes.

http://bioinformatics.oxfordjournals.org/content/25/4/543/F1.expansion.html

Using DSS

• What-if Analysis

–End user makes changes to variables, or relationships among variables, and observes the resulting changes in the values of other variables

• Sensitivity Analysis

–Value of only one variable is changed repeatedly and the resulting changes in other variables are observed

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Using DSS

• Goal-Seeking

–Set a target value for a variable and then

repeatedly change other variables until the target value is achieved

–How can analysis

• Optimization

–Goal is to find the optimum value for one or

more target variables given certain constraints

–One or more other variables are changed

repeatedly until the best values for the target variables are discovered

Data Mining

• Tujuan utama adalah untuk memberikan dukungan keputusan pada manajer dan

profesional bisnis melalui knowledge discovery • Analyzes vast store of historical business data

• Tries to discover patterns, trends, and

correlations hidden in the data that can help a company improve its business performance

• Use regression, decision tree, neural network, cluster analysis, or market basket analysis

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Market Basket Analysis

• One of most common data mining for

marketing

• The purpose is to determine what

products customers purchase together

with other products

Executive Information Systems

• EIS

–Combine many features of MIS and DSS

–Provide top executives with immediate and easy access to information

–About the factors that are critical to

accomplishing an organization’s strategic

objectives (Critical success factors)

–So popular, expanded to managers, analysts and other knowledge workers

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http://www.accurasoft.com/comp_executive_information_system.htm

Features of an EIS

• Information presented in forms tailored to

the preferences of the executives using

the system

–Customizable graphical user interfaces

–Exception reporting

–Trend analysis

–Drill down capability

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Enterprise Interface Portals

• EIP

–Web-based interface

–Integration of MIS, DSS, EIS, and other

technologies

–Gives all intranet users and selected extranet

users access

–To a variety of internal and external business

applications and services

• Typically tailored to the user giving them a personalized digital dashboard

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Enterprise Information Portal

Components

Knowledge Management Systems

• The use of information technology to

help gather, organize, and share

business knowledge within an

organization

• Enterprise Knowledge Portals

–EIPs that are the entry to corporate

intranets that serve as knowledge management systems

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Enterprise Knowledge Portals

Artificial Intelligence (AI)

• A field of science and technology based

on disciplines such as computer science,

biology, psychology, linguistics,

mathematics, and engineering

• Goal is to develop computers that can

simulate the ability to think, as well as

see, hear, walk, talk, and feel

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Attributes of Intelligent

Behavior

• Think and reason

• Use reason to solve problems

• Learn or understand from experience • Acquire and apply knowledge

• Exhibit creativity and imagination

• Deal with complex or perplexing situations • Respond quickly and successfully to new

situations

• Recognize the relative importance of elements in a situation

• Handle ambiguous, incomplete, or erroneous information

Domains of Artificial Intelligence

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Cognitive Science

• Based in biology, neurology, psychology,

etc.

• Focuses on researching how the human

brain works and how humans think and

learn

Robotics

• Based in AI, engineering and physiology

• Robot machines with computer

intelligence and computer controlled,

humanlike physical capabilities

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Natural Interfaces

• Based in linguistics, psychology, computer science, etc.

• Includes natural language and speech recognition

• Development of multisensory devices that use a variety of body movements to operate

computers • Virtual reality

– Using multisensory human-computer interfaces that

enable human users to experience

computer-simulated objects, spaces and “worlds” as if they

actually exist

Expert Systems

• ES

–A knowledge-based information system

(KBIS) that uses its knowledge about a specific, complex application to act as an expert consultant to end users

–KBIS is a system that adds a knowledge base to the other components on an IS

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Expert System Components

• Knowledge Base

– Facts about specific subject area

– Heuristics that express the reasoning procedures of

an expert (rules of thumb)

• Software Resources

– Inference engine processes the knowledge and makes inferences to make recommend course of action

– User interface programs to communicate with end

user

– Explanation programs to explain the reasoning

process to end user

Expert System Components

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Methods of Knowledge Representation

• Case-Based

–

knowledge organized in

form of cases

–Cases: examples of past performance, occurrences and experiences

• Frame-Based

–

knowledge organized in

a hierarchy or network of frames

–Frames: entities consisting of a complex package of data values

Methods of Knowledge Representation

• Object-Based

–

knowledge organized in

network of objects

–Objects: data elements and the methods or processes that act on those data

• Rule-Based

–

knowledge represented in

rules and statements of fact

–Rules: statements that typically take the form of a premise and a conclusion

–Such as, If (condition) then (conclusion)

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Expert System Benefits

• Faster and more consistent than an

expert

• Can have the knowledge of several

experts

• Does not get tired or distracted by

overwork or stress

• Helps preserve and reproduce the

knowledge of experts

Expert System Limitations

• Limited focus

• Inability to learn

• Maintenance problems

• Developmental costs

• Can only solve specific types of

problems in a limited domain of

knowledge

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Suitability Criteria for Expert

Systems

• Domain: subject area relatively small and limited to well-defined area

• Expertise: solutions require the efforts of an expert

• Complexity: solution of the problem is a complex task that requires logical inference processing (not possible in conventional information processing)

• Structure: solution process must be able to cope with ill-structured, uncertain, missing and conflicting data

• Availability: an expert exists who is articulate and cooperative

Development Tool

• Expert System Shell

–Software package consisting of an expert system without its knowledge base

–Has inference engine and user interface programs

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Knowledge Engineer

• A professional who works with experts to

capture the knowledge they possess

• Builds the knowledge base using an

iterative, prototyping process

Neural Networks

• Computing systems modeled after the

brain’s mesh

-like network of

interconnected processing elements,

called neurons

• Interconnected processors operate in

parallel and interact with each other

• Allows network to learn from data it

processes

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Fuzzy Logic

• Method of reasoning that resembles

human reasoning

• Allows for approximate values and

inferences and incomplete or ambiguous

data instead of relying only on crisp data

• Uses terms such as “very high” rather

than precise measures

Genetic Algorithms

• Software that uses

–Darwinian (survival of the fittest), randomizing, and other mathematical functions

–To simulate an evolutionary process that can yield increasingly better solutions to a problem

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Virtual Reality (VR)

• Computer-simulated reality

• Relies on multisensory input/output devices such as

–a tracking headset with video goggles and

stereo earphones,

–a data glove or jumpsuit with fiber-optic sensors

that track your body movements, and

–a walker that monitors the movement of your

feet

Intelligent Agents

• A software surrogate for an end user or a

process that fulfills a stated need or

activity

• Uses its built-in and learned knowledge

base

• To make decisions and accomplish tasks

in a way that fulfills the intentions of a

user

• Also called

software robots

or

bots

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User Interface Agents

• Interface Tutors– observe user computer operations, correct user mistakes, and provide hints and advice on efficient software use • Presentation– show information in a variety of

forms and media based on user preferences • NetworkNavigation– discover paths to

information and provide ways to view information based on user preferences • Role-Playing – play what-if games and other

roles to help users understand information and make better decisions

Information Management Agents

• Search Agents – help users find files and databases, search for desired information, and suggest and find new types of

information products, media, and resources

• Information Brokers – provide commercial services to discover and develop information resources that fit the business or personal needs of a user

• Information Filters – receive, find, filter, discard, save, forward, and notify users about products received or desired