1 Pertemuan 19 & 20 Managing Knowledge for the Digital Firm Matakuliah: J0454 / Sistem Informasi...

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Pertemuan 19 & 20Managing Knowledge for the Digital Firm

Matakuliah : J0454 / Sistem Informasi Manajemen

Tahun : 2006

Versi : 1 / 1

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Learning Outcomes

Pada akhir pertemuan ini, diharapkan mahasiswa

akan mampu :

• Mahasiswa akan dapat menunjukkan strategi penerapan Manajemen Pengetahuan C3

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Outline Materi

• Concept of Knowledge Management

• Knowledge Management in the Organization

• Organization Learning and KM

• Artificial Intelligence

• Expert Systems

• Case-Based Reasoning

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Organizational learningOrganizational learning

• Creation of new standard operating Creation of new standard operating procedures and business processes procedures and business processes reflecting experience reflecting experience

Knowledge managementKnowledge management

• Set of processes Set of processes

• Creates, gathers, stores, maintains, and Creates, gathers, stores, maintains, and disseminates knowledgedisseminates knowledge

KNOWLEDGE MANAGEMENT IN THE ORGANIZATION

Organizational Learning and Knowledge Management

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Knowledge AssetsKnowledge Assets• Organizational knowledge enabling Organizational knowledge enabling

the business to create valuethe business to create value

Chief Knowledge Officer (CKO)Chief Knowledge Officer (CKO)

• Senior executive in charge of Senior executive in charge of organization’s knowledge management organization’s knowledge management programprogram


Organizational Learning and Knowledge Management

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Tacit KnowledgeTacit Knowledge

• Expertise and experience not formally Expertise and experience not formally documenteddocumented

Best PracticesBest Practices

• Successful solutions or problem-solving Successful solutions or problem-solving methods developed by specific methods developed by specific organization or industryorganization or industry


Systems and Infrastructure for Knowledge Management

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Organizational MemoryOrganizational Memory

• Stored learning from organization’s Stored learning from organization’s history history

• Used for decision making and other Used for decision making and other purposespurposes


Systems and Infrastructure for Knowledge Management

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IT Infrastructure for Knowledge Management


Figure 10-1

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• Consists of creating or processing Consists of creating or processing informationinformation

• Divided into knowledge workers and data Divided into knowledge workers and data workersworkers

INFORMATION AND KNOWLEDGE WORK SYSTEMS

Information Work

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Office systemsOffice systems

• Manage and coordinate work of data and Manage and coordinate work of data and knowledge workersknowledge workers

• Connect work of local information workers Connect work of local information workers with all levels and functions of organizationwith all levels and functions of organization

• Connect organization to external worldConnect organization to external world

• Example: Word processing, voice mail, and Example: Word processing, voice mail, and imagingimaging


Distributing Knowledge: Office and Document Management Systems

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The Three Major Roles of Offices

Figure 10-2

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Document imaging systems

• Convert documents and images into digital form

• Can be stored and accessed by the computer

Knowledge repository• Documented knowledge in a single

location

Typical Office Systems


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Components of an Imaging System

Figure 10-3

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Web Publishing and Document Management

Figure 10-4

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Knowledge Work Systems (KWS)• Aid knowledge workers in creation and

integration of new knowledge

• Specialized tools for specific types of knowledge work

• User-friendly interface

Creating Knowledge: Knowledge Work Systems


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Changes in the Construction Project Management Process


Figure 10-5

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Requirements of Knowledge Work Systems


Figure 10-6

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• Computer-aided design (CAD)

• Virtual reality systems

• Virtual Reality Modeling Language (VRML)

• Investment workstations

Examples of Knowledge Work Systems


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• Groupware

• Intranets and Enterprise Knowledge Environments

• Enterprise information portals

• Teamware

Sharing Knowledge: Group Collaboration Systems and Enterprise Knowledge Environments


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An Enterprise Information Portal


Figure 10-7

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• Effort to develop computer-based systems that behave as humans

• Includes natural language, robotics, perceptive systems, expert systems, and intelligent machines

ARTIFICIAL INTELLIGENCE

What is Artificial Intelligence?

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• Artificial Intelligence:

– Stores information in active form

– Creates mechanism not subjected to human feelings

– Eliminates routine and unsatisfying jobs

– Enhances organization’s knowledge base

– Generates solution to specific problems

Why Business is Interested in Artificial Intelligence


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The Artificial Intelligence Family


Figure 10-8

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• Knowledge Base

• Rule-based Expert System

• Rule Base

• Knowledge Frames

Capturing Knowledge: Expert Systems


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Rules in an AI Program


Figure 10-9

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• AI shell

• Inference Engine

• Forward Chaining

• Backward Chaining

Capturing Knowledge: Expert Systems


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Figure 10-10

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Knowledge engineer

• Specialist eliciting information and expertise from other professionals

• Translates information into set of rules for an expert system


Building an Expert System

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Case-based Reasoning (CBR)

• Captures and stores collective knowledge

• Represents knowledge as database of cases and solutions

Organizational Intelligence: Case-Based Reasoning


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Figure 10-11

Casedatabase

1.

2.

3.

4.

5. 6.

NO YES

Successful?

System modifies thesolution to better fit

the problem

System finds closestfit and retrieves

solution

System storesproblem and

successful solutionin the database

System asks useradditional questions to

narrow the search

System searchesdatabase for similar

cases

User describes theproblem

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• Hardware or software emulating processing patterns of biological brain

• Put intelligence into hardware in form of a generalized capability to learn

Neural Networks

OTHER INTELLIGENT TECHNIQUES

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Inference Engines in Expert Systems


Figure 10-12

Synapse

Dendrite

Axon

Soma

Neuron

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Figure 10-13

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Biological Neurons of a Leech


Figure 10-14

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• Rule-based AI

• Tolerates imprecision

• Uses nonspecific terms called membership functions to solve problems

Fuzzy Logic


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Implementing Fuzzy Logic Rules in Hardware


Figure 10-15

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• Problem-solving methods

• Promote evolution of solutions to specified problems

• Use a model of living organisms adapting to their environment

Genetic Algorithms


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The Components of a Genetic Algorithm


Figure 10-16

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• Integration of multiple AI technologies into a single application

• Takes advantage of best features of technologies

Hybrid AI Systems


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• Software programs

• Use built-in or learned knowledge base to carry out specific, repetitive, and predictable tasks

Intelligent Agents


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Sumber Materi PPT

• Laudon, Kenneth C. and Jane P. Laudon (2004). Management Information Systems (8th Edition). Prentice Hall. Bab 10. Official PPT.

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