Decision Support Systems and Electronic Commerce

ISSN: 0167-9236
Imprint: NORTH-HOLLAND

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Impact Factor: 1.119
Issues per year: 8

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Decision Support Systems welcomes contributions on the concepts and operational basis for DSSs, techniques for implementing and evaluating DSSs, DSS experiences, and related studies. In treating DSS topics, manuscripts may delve into, draw-on, or expand such diverse areas as artificial intelligence, cognitive science, computer supported ... click here for full Aims & Scope

Decision Support Systems welcomes contributions on the concepts and operational basis for DSSs, techniques for implementing and evaluating DSSs, DSS experiences, and related studies. In treating DSS topics, manuscripts may delve into, draw-on, or expand such diverse areas as artificial intelligence, cognitive science, computer supported cooperative work, data base management, decision theory, economics, linguistics, management science, mathematical modeling, operations management psychology, user interface management systems, and others. The common thread of articles published in the journal will be their relevance to theoretical, technical DSS issues.

Authors planning to submit papers to the journal should ensure that their work is relevant to the topics normally considered to be part of the field of decision support systems.

The Journal's research papers tend to fall into the following six topic departments:

1. DSS Foundations. e.g. DSS principles, concepts, and theories; frameworks, formal languages, and methods for DSS research; tutorials about the nature of DSS; assessments of the DSS field.

2. DSS Development-Functionality. e.g. methods, tools, and techniques for developing the underlying functional aspects of a DSS; solver/model management; data management in DSSs; rule management and AI in DSSs; coordinating a DSS's functionality within its user interface.

3. DSS Development-Interfaces. e.g. methods, tools, and techniques for developing the overt user interface of a DSS; managing linguistic, presentation, and user knowledge in a DSS; DSS help facilities; coordinating a DSS's interface events with its functionality events.

4. DSS Impacts and Evaluation. e.g. DSS economics; DSS measurement; DSS impacts on individual users, multiparticipant users, organizations, and societies; evaluating/justifying DSSs.

5. DSS Reference Studies. e.g. reference discipline tutorials for DSS researchers; emerging technologies relevant to DSS characteristics or DSS development; related studies on such topics as communication support systems, computer supported cooperative work, negotiation support systems, research support systems, task support systems.

6. DSS Experiences, Management, and Education. e.g. experiences in developing or operating DSSs; systems solutions to specific decision support needs; approaches to managing DSSs; DSS instruction/training approaches.

Hide Aims & Scope

Editor-in-Chief: Contact the Editor
A.B. Whinston


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Decision Sciences Alert
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Note to Contributors: Online submission is now available for this journal. To submit your article online, go to: http://ees.elsevier.com/decsup

Call for papers for a special issue: Theme: Quantitative Methods for Detection of Financial Fraud

Call for Papers: Special Issue on 'Enterprise Risk and Security Management: Data, Text and Web Mining'

• Articles
• Issues

Recent | Top 10 Cited | Most Downloaded

Fri Dec 26 09:47:01 GMT 2008

1. Semantic Web Constraint Language and Its Application to an Intelligent Shopping Agent
   Publication year: 2008
   Source: Decision Support Systems, In Press, Accepted Manuscript, Available online 25 December 2008
   Hak-Jin, Kim , Wooju, Kim , Myungjin, Lee
   
   
   
   Semantic Web society was initially focused only on data, but then gradually moved toward knowledge. If a vision of the Semantic Web is to enhance humans' decision-making assisted by machines, a missing but important part is knowledge about constraints on data and concepts represented by ontology. This paper proposes a Semantic Web Constraint Language (SWCL) based on OWL, and shows its effectiveness in representing and solving an internet shopper's decision-making problems by implementing a shopping agent in the Semantic Web environment.
2. Software Project Effort Estimation with Voting Rules
   Publication year: 2008
   Source: Decision Support Systems, In Press, Accepted Manuscript, Available online 24 December 2008
   Stefan, Koch , Johann, Mitlöhner
   
   Abstract: Social choice deals with aggregating the preferences of a number of voters into a collective preference. We will use this idea for software project effort estimation, substituting the voters by project attributes. Therefore, instead of supplying numeric values for various project attributes that are then used in regression or similar methods, a new project only needs to be placed into one ranking per attribute, necessitating only ordinal values. Using the resulting aggregate ranking the new project is a gain placed between other projects whose actual expended effort can be used to derive an estimation. In this paper we will present...
3. Editorial Board
   Publication year: 2009
   Source: Decision Support Systems, Volume 46, Issue 2, January 2009, Page IFC
   [No author name available]
4. Avatar e-mail versus traditional e-mail: Perceptual difference and media selection difference
   Publication year: 2009
   Source: Decision Support Systems, Volume 46, Issue 2, January 2009, Pages 451-467
   Younghwa, Lee , Kenneth A., Kozar , Kai R., Larsen
   
   The study investigates how knowledge workers perceive avatar e-mail differently from traditional e-mail, and how they select traditional versus avatar e-mail when different levels of task equivocality and different types of communication direction are present. Three field studies were conducted with knowledge workers who have used avatar and traditional e-mail. This study demonstrates that overall perception toward avatar e-mail is significantly different from traditional e-mail with respect to media richness and social presence characteristics. In addition, this study found individuals used different e-mail selection approaches when conducting tasks with different equivocality (high versus low equivocal tasks) and tasks for different...
5. Understanding Risk-taking Behavior of Groups: A "Decision Analysis" Perspective
   Publication year: 2008
   Source: Decision Support Systems, In Press, Accepted Manuscript, Available online 24 December 2008
   Joseph S., Valacich , Saonee, Sarker , Jamie, Pratt , Mike, Groomer
   
   Past research has extensively investigated the effect of media, especially focusing on how anonymity increases risk-related behaviors of groups when using computer-mediated communication (CMC). This study extends prior research by examining the differences in group risk-taking behaviors between face-to-face groups and completely non-anonymous CMC groups (i.e., groups working in a fully identified, synchronous CMC environment similar to popular instant messaging systems utilized widely within organizations). Drawing on the "decision analysis" perspective, a key framework for understanding organizational decision-making, the study also examines the effects of the firm's risk preferences as well as the type of information distribution amongst group members...
6. Software Project Effort Estimation with Voting Rules
   Publication year: 2008
   Source: Decision Support Systems, In Press, Accepted Manuscript, Available online 24 December 2008
   Stefan, Koch , Johann, Mitlöhner
   
   Abstract: Social choice deals with aggregating the preferences of a number of voters into a collective preference. We will use this idea for software project effort estimation, substituting the voters by project attributes. Therefore, instead of supplying numeric values for various project attributes that are then used in regression or similar methods, a new project only needs to be placed into one ranking per attribute, necessitating only ordinal values. Using the resulting aggregate ranking the new project is a gain placed between other projects whose actual expended effort can be used to derive an estimation. In this paper we will present...
7. Introducing functional classification theory to land use planning by means of decision tables
   Publication year: 2008
   Source: Decision Support Systems, In Press, Accepted Manuscript, Available online 10 December 2008
   Frank, Witlox , Marc, Antrop , Peter, Bogaert , Philippe, De Maeyer , Ben, Derudder , ...
8. Classification Algorithm Sensitivity to Training Data with Non Representative Attribute Noise
   Publication year: 2008
   Source: Decision Support Systems, In Press, Accepted Manuscript, Available online 3 December 2008
   Michael, Mannino , Yanjuan, Yang , Young, Ryu
9. Classification Algorithm Sensitivity to Training Data with Non Representative Attribute Noise
   Publication year: 2008
   Source: Decision Support Systems, In Press, Accepted Manuscript, Available online 3 December 2008
   Michael, Mannino , Yanjuan, Yang , Young, Ryu
10. Visualized Cognitive Knowledge Map Integration for P2P Networks
    Publication year: 2008
    Source: Decision Support Systems, In Press, Accepted Manuscript, Available online 3 December 2008
    Fu-ren, Lin , Jen-Hung, Yu

View Full-Text in ScienceDirect

Selengkapnya...

Decision Support Systems and Electronic Commerce

ISSN: 0167-9236
Imprint: NORTH-HOLLAND

Actions

• Submit Article
• Order Journal
• Recommend to Friend
• Bookmark this Page

Statistics

Impact Factor: 1.119
Issues per year: 8

Tips Title

This is my tip content

Decision Support Systems welcomes contributions on the concepts and operational basis for DSSs, techniques for implementing and evaluating DSSs, DSS experiences, and related studies. In treating DSS topics, manuscripts may delve into, draw-on, or expand such diverse areas as artificial intelligence, cognitive science, computer supported ... click here for full Aims & Scope

Decision Support Systems welcomes contributions on the concepts and operational basis for DSSs, techniques for implementing and evaluating DSSs, DSS experiences, and related studies. In treating DSS topics, manuscripts may delve into, draw-on, or expand such diverse areas as artificial intelligence, cognitive science, computer supported cooperative work, data base management, decision theory, economics, linguistics, management science, mathematical modeling, operations management psychology, user interface management systems, and others. The common thread of articles published in the journal will be their relevance to theoretical, technical DSS issues.

Authors planning to submit papers to the journal should ensure that their work is relevant to the topics normally considered to be part of the field of decision support systems.

The Journal's research papers tend to fall into the following six topic departments:

1. DSS Foundations. e.g. DSS principles, concepts, and theories; frameworks, formal languages, and methods for DSS research; tutorials about the nature of DSS; assessments of the DSS field.

2. DSS Development-Functionality. e.g. methods, tools, and techniques for developing the underlying functional aspects of a DSS; solver/model management; data management in DSSs; rule management and AI in DSSs; coordinating a DSS's functionality within its user interface.

3. DSS Development-Interfaces. e.g. methods, tools, and techniques for developing the overt user interface of a DSS; managing linguistic, presentation, and user knowledge in a DSS; DSS help facilities; coordinating a DSS's interface events with its functionality events.

4. DSS Impacts and Evaluation. e.g. DSS economics; DSS measurement; DSS impacts on individual users, multiparticipant users, organizations, and societies; evaluating/justifying DSSs.

5. DSS Reference Studies. e.g. reference discipline tutorials for DSS researchers; emerging technologies relevant to DSS characteristics or DSS development; related studies on such topics as communication support systems, computer supported cooperative work, negotiation support systems, research support systems, task support systems.

6. DSS Experiences, Management, and Education. e.g. experiences in developing or operating DSSs; systems solutions to specific decision support needs; approaches to managing DSSs; DSS instruction/training approaches.

Hide Aims & Scope

Editor-in-Chief: Contact the Editor
A.B. Whinston


Abstracts/Articles on ScienceDirect

Decision Sciences Alert
subscribe/unsubscribe

Note to Contributors: Online submission is now available for this journal. To submit your article online, go to: http://ees.elsevier.com/decsup

Call for papers for a special issue: Theme: Quantitative Methods for Detection of Financial Fraud

Call for Papers: Special Issue on 'Enterprise Risk and Security Management: Data, Text and Web Mining'

• Articles
• Issues

Recent | Top 10 Cited | Most Downloaded

Fri Dec 26 09:47:01 GMT 2008

1. Semantic Web Constraint Language and Its Application to an Intelligent Shopping Agent
   Publication year: 2008
   Source: Decision Support Systems, In Press, Accepted Manuscript, Available online 25 December 2008
   Hak-Jin, Kim , Wooju, Kim , Myungjin, Lee
   
   
   
   Semantic Web society was initially focused only on data, but then gradually moved toward knowledge. If a vision of the Semantic Web is to enhance humans' decision-making assisted by machines, a missing but important part is knowledge about constraints on data and concepts represented by ontology. This paper proposes a Semantic Web Constraint Language (SWCL) based on OWL, and shows its effectiveness in representing and solving an internet shopper's decision-making problems by implementing a shopping agent in the Semantic Web environment.
2. Software Project Effort Estimation with Voting Rules
   Publication year: 2008
   Source: Decision Support Systems, In Press, Accepted Manuscript, Available online 24 December 2008
   Stefan, Koch , Johann, Mitlöhner
   
   Abstract: Social choice deals with aggregating the preferences of a number of voters into a collective preference. We will use this idea for software project effort estimation, substituting the voters by project attributes. Therefore, instead of supplying numeric values for various project attributes that are then used in regression or similar methods, a new project only needs to be placed into one ranking per attribute, necessitating only ordinal values. Using the resulting aggregate ranking the new project is a gain placed between other projects whose actual expended effort can be used to derive an estimation. In this paper we will present...
3. Editorial Board
   Publication year: 2009
   Source: Decision Support Systems, Volume 46, Issue 2, January 2009, Page IFC
   [No author name available]
4. Avatar e-mail versus traditional e-mail: Perceptual difference and media selection difference
   Publication year: 2009
   Source: Decision Support Systems, Volume 46, Issue 2, January 2009, Pages 451-467
   Younghwa, Lee , Kenneth A., Kozar , Kai R., Larsen
   
   The study investigates how knowledge workers perceive avatar e-mail differently from traditional e-mail, and how they select traditional versus avatar e-mail when different levels of task equivocality and different types of communication direction are present. Three field studies were conducted with knowledge workers who have used avatar and traditional e-mail. This study demonstrates that overall perception toward avatar e-mail is significantly different from traditional e-mail with respect to media richness and social presence characteristics. In addition, this study found individuals used different e-mail selection approaches when conducting tasks with different equivocality (high versus low equivocal tasks) and tasks for different...
5. Understanding Risk-taking Behavior of Groups: A "Decision Analysis" Perspective
   Publication year: 2008
   Source: Decision Support Systems, In Press, Accepted Manuscript, Available online 24 December 2008
   Joseph S., Valacich , Saonee, Sarker , Jamie, Pratt , Mike, Groomer
   
   Past research has extensively investigated the effect of media, especially focusing on how anonymity increases risk-related behaviors of groups when using computer-mediated communication (CMC). This study extends prior research by examining the differences in group risk-taking behaviors between face-to-face groups and completely non-anonymous CMC groups (i.e., groups working in a fully identified, synchronous CMC environment similar to popular instant messaging systems utilized widely within organizations). Drawing on the "decision analysis" perspective, a key framework for understanding organizational decision-making, the study also examines the effects of the firm's risk preferences as well as the type of information distribution amongst group members...
6. Software Project Effort Estimation with Voting Rules
   Publication year: 2008
   Source: Decision Support Systems, In Press, Accepted Manuscript, Available online 24 December 2008
   Stefan, Koch , Johann, Mitlöhner
   
   Abstract: Social choice deals with aggregating the preferences of a number of voters into a collective preference. We will use this idea for software project effort estimation, substituting the voters by project attributes. Therefore, instead of supplying numeric values for various project attributes that are then used in regression or similar methods, a new project only needs to be placed into one ranking per attribute, necessitating only ordinal values. Using the resulting aggregate ranking the new project is a gain placed between other projects whose actual expended effort can be used to derive an estimation. In this paper we will present...
7. Introducing functional classification theory to land use planning by means of decision tables
   Publication year: 2008
   Source: Decision Support Systems, In Press, Accepted Manuscript, Available online 10 December 2008
   Frank, Witlox , Marc, Antrop , Peter, Bogaert , Philippe, De Maeyer , Ben, Derudder , ...
8. Classification Algorithm Sensitivity to Training Data with Non Representative Attribute Noise
   Publication year: 2008
   Source: Decision Support Systems, In Press, Accepted Manuscript, Available online 3 December 2008
   Michael, Mannino , Yanjuan, Yang , Young, Ryu
9. Classification Algorithm Sensitivity to Training Data with Non Representative Attribute Noise
   Publication year: 2008
   Source: Decision Support Systems, In Press, Accepted Manuscript, Available online 3 December 2008
   Michael, Mannino , Yanjuan, Yang , Young, Ryu
10. Visualized Cognitive Knowledge Map Integration for P2P Networks
    Publication year: 2008
    Source: Decision Support Systems, In Press, Accepted Manuscript, Available online 3 December 2008
    Fu-ren, Lin , Jen-Hung, Yu

View Full-Text in ScienceDirect

Selengkapnya...

Pengenalan "Apa Itu Komputer"

- Penulis: Romi Satria Wahono - Bahasa: Indonesia - Format file: HTML - Publisher: IlmuKomputer.Com - Tahun terbit: Juli 2003 Definisi Komputer berasal dari bahasa latin computare yang mengan dung arti menghitung. Karena luasnya bidang garapan ilmu komputer, para pakar dan peneliti sedikit berbeda dalam mendefinisikan termininologi komputer. Menurut Hamacher [1], komputer adalah mesin penghitung elektronik yang cepat dan dapat menerima informasi input digital, kemudian memprosesnya sesuai dengan program yang tersimpan di memorinya, dan menghasilkan output berupa informasi. Menurut Blissmer [2], komputer adalah suatu alat elektonik yang mampu melakukan beberapa tugas sebagai berikut: - menerima input - memproses input tadi sesuai dengan programnya - menyimpan perintah-perintah dan hasil dari pengolahan - menyediakan output dalam bentuk informasi Sedangan Fuori [3] berpendapat bahwa komputer adalah suatu pemroses data yang dapat melakukan perhitungan besar secara cepat, termasuk perhitungan aritmetika dan operasi logika, tanpa campur tangan dari manusia. Untuk mewujudkan konsepsi komputer sebagai pengolah data untuk menghasilkan suatu informasi, maka di perlukan sistem komputer (computer system) yang elemennya terdiri dari hardware, software dan brainware. Ketiga elemen sistem komputer tersebut harus saling berhubungan dan membentuk kesatuan. Hardware tidak akan berfungsi apabila tanpa software, demikian juga sebaliknya. Dan keduanya tiada bermanfaat apabila tidak ada manusia (brainware) yang mengoperasikan dan mengendalikannya. Hardware atau Perangkat Keras: peralatan yang secara fisik terlihat dan bisa djamah. Software atau Perangkat Lunak: program yang berisi instruksi/perintah untuk melakukan pengolahan data. Brainware: manusia yang mengoperasikan dan mengendalikan sistem komputer. Penggolongan Komputer Literatur terbaru tentang komputer melakukan penggolongan komputer berdasarkan tigal hal: data yang diolah, penggunaan, kapasitas/ukurannya, dan generasinya. Berdasarkan Data Yang Diolah Komputer Analog Komputer Digital Komputer Hybrid Berdasarkan Penggunannya Komputer Untuk Tujuan Khusus (Special Purpose Computer) Komputer Untuk Tujuan Umum (General Purpose Computer) Berdasarkan Kapasitas dan Ukurannya Komputer Mikro (Micro Computer) Komputer Mini (Mini Computer) Komputer Kecil (Small Computer) Komputer Menengah (Medium Computer) Komputer Besar (Large Computer) Komputer Super (Super Computer) Berdasarkan Generasinya Komputer Generasi Pertama (1946-1959) Komputer Generasi Kedua (1959-1964) Komputer Generasi Ketiga (1964-1970) Komputer Generasi Keempat (1979-sekarang) Komputer Generasi Kelima Referensi V. Carl Hamacher, Zvonko G. Vranesic, Safwat G. Zaky, Computer Organization (5th Edition), McGraw-Hill, 2001. Robert H. Blissmer, Computer Annual, An Introduction to Information Systems 1985-1986 (2nd Edition), John Wiley & Sons, 1985. William M. Fuori, Introduction to the Computer: The Tool of Business (3rd Edition), Prentice Hall, 1981.

Selengkapnya...

Pengenalan Klasifikasi Ilmu Komputer

- Penulis: Romi Satria Wahono - Bahasa: Indonesia - Format file: HTML - Publisher: IlmuKomputer.Com - Tahun terbit: April 2003 Dasar Klasifikasi Ilmu komputer adalah disiplin ilmu yang melingkupi cabang ilmu yang cukup luas, dari masalah teori-teori dasar sampai teknologi aplikasi. Pengklasifikasian Ilmu Komputer biasanya merefer ke Matriks Dennings, yaitu salah satu matriks penggolongan Ilmu Komputer yang diciptakan oleh Peter J. Dennings [1][2]. Klasifikasi ini mengalami beberapa perbaikan, dimana versi terakhir adalah versi tahun 1999 [2][3]. Dalam versi terakhir ini ilmu komputer terbagi dalam 12 subbidang (versi sebelumnya adalah 9 subbidang). 12 subbidang Ilmu Komputer ini adalah: Algoritma dan Struktur Data (Algorithms and Data Structures) Bahasa Pemrograman (Programming Languages) Arsitektur (Architecture) Sistem Operasi dan Jaringan (Operating Systems and Networks) Software Engineering Database dan Sistim Retrieval Informasi (Database and Information Retrieval Systems) Artificial Intelligence dan Robotik (Artificial Intelligence and Robotics) Grafik (Graphics) Human Computer Interaction Ilmu Komputasi (Computational Sciences) Organizational Informatics BioInformatik (BioInformatics) Dennings memberi catatan khusus untuk bidang BioInformatik sebagai bidang baru yang merupakan gabungan antara Ilmu Komputer dan Biologi, dan saat ini mengalami perkembangan yang cukup signifikan. Kemudian seiring dengan peningkatan ilmu dan teknologi, ada kemungkinan matriks ini akan mengalami perbaikan lagi di kelak kemudian hari. Baris dalam matriks Denning ini menggambarkan bidang-bidang dalam ilmu komputer. Sedangkan kolom pada matriks menggambarkan paradigma bidang-bidang tersebut, yang direfleksikan dalam tiga hal: Teori (Theory), Abstraksi (Abstraction), dan Desain (Design). Teori: adalah berlandaskan pada pendekatan matematika, dimana untuk mendapatkan suatu teori yang valid, harus melalui proses-proses sbb: pendefinisian (definition) pembuatan teorema (theorema) pembuktian (proof) penginterpretasian hasil (interpret result) Abstraksi: atau pemodelan (modeling), adalah berlandaskan pada metode eksperimen ilmiah, dimana dalam melakukan invesitigasi terhadap suatu fenomena, harus melalui proses-proses sbb: membentuk hipotesa (form a hypothesis) membuat suatu model dan melakukan prediksi (construct a model and make a predition) mendesain eksperimen dan mengumpulkan data (design an experiment and collect data) menganalisa hasil (analyze hasil) Desain: adalah berlandaskan pada pendekatann engineering (teknik), dimana pada saat mendesain sebuah sistem atau device untuk memecahkan masalah, harus melalui proses-proses sbb: menyatakan requirement (state requirements), menyatakan spesifikasi (state specifications) melakukan desain dan implementasi sistem (design and implement the system) melakukan pengetesan terhadap sistem (test the system) Dari penjelasan diatas, bisa kita pahami bahwa yang bergerak dalam masalah penelitian ilmu komputer akan banyak berhubungan dengan dua kolom pertama matriks (Teori dan Abstraksi). Sedangkan yang bergerak dalam masalah yang lebih teknis dengan memakai pendekatan engineering, akan lebih banyak berkecimpung dalam ruang lingkup dua kolom terakhir matriks (Abstraksi dan Desain). Klasifikasi Ilmu Komputer Rangkumkan lengkap klasifikasi ilmu komputer berdasarkan Matriks Denning versi 1999 adalah seperti dibawah. Penulis sengaja melakukan perbaikan, penerjemahan, dan penyingkatan, untuk lebih mempermudah pemahaman terhadap klasifikasi ilmu komputer ini. Teori Abstraksi Desain Algoritma dan Struktur Data Teori Komputabilitas Algoritma Paralel dan Terdistribusi Program Aplikasi Teori Komputasi Kompleks Komputasi Paralel Algoritma Efisien dan Optimal Teori Graf Kriptografi Algoritma dan Teori Probabilistik Bahasa Pemrograman Bahasa Formal dan Automata BNF Bahasa Pemrograman Turing Machines Metode Parsing, Compiling, Interpretation Formal Semantics Translator, Kompiler, Interpreter Arsitektur Aljabar Boolean Arsitektur Nueman Produk Hardware (PC, Superkomputer, Mesin Von Neumann) Teori Coding Hardware Reliability Teori Switching Finite State Machine Sistem CAD dan Simulasi Logika Teori Finite State Machine Model Sirkuit, Data Path, Struktur Kontrol Sistem Operasi dan Jaringan Teori Concurrency Manajemen Memori, Job Scheduling Produk OS (UNIX, Windows, Mach, dsb) Teori Scheduling Model Komputer Terdistribusi File dan File Sistem Teori Manajemen Memori Networking (Protokol, Naming, dsb) Pustaka untuk Utilities (Editor, Formatter, Linker, dsb) Software Engineering Teori Reliability Metode Spesifikasi Bahasa Spesifikasi Program Verification and Proof Metode Otomatisasi Pengembangan Program Metodologi Pengembangan Software Temporal Logic Tool Pengembangan Software Tool untuk Pengembangan Software Database dan Sistim Retrieval Informasi Relational Aljabar dan Kalkulus Data Model Teknik Pendesainan Database (Relational, Hierarchical, Network, dsb) Teori Dependency Teori Concurrency Skima Database Teknik Pendesainan Database Sistem (Ingres, Dbase, Oracle, dsb) Performance Analysis Sorting dan Searching Representasi File untu Retrieval Hypertext System Statistical Inference Artificial Intelligence dan Robotik Teori Logika Knowledge Representation Logic Programming (Prolog) Semantik dan Sintatik Model untuk Natural Language Metode Pencarian Heuristic Neural Network Conceptual Dependency Model Reasoning dan Learning Sistem Pakar Kinematics and Dynamics of Robot Motion Model Memori Manusia, Autonomous Learning Teknik Pendesaian Software untuk Logic Programming Grafik Teori Grafik dan Warna Algoritma Komputer Grafik Pustaka untuk Grafik Geometri Dimensi Dua atau Lebih Model untuk Virtual Reality Grafik Standar Teori Chaos Metode Komputer Grafik Image Enhacement System Human Computer Interaction Risk Analysis Pattern Recognition Flight Simulation Cognitive Psychology Sistem CAD Usability Engineering Ilmu Komputasi Number Theory Discrete Approximations, Fast Fourier Transform and Poisson Solvers Pustaka dan Paket untuk Tool Penelitian (Chem, Macsyma, Mathematica, Maple, Reduce, dsb) Binary Representation Backward Error Propagation Teori Quantum Finite Element Models, Organizational Informatics Organizational Science Model dan Simlasi berhubungan dengan organizational informatics Management Information Systems Decision Support Systems Decision Sciences Organizational Dynamics Bioinformatik Teori Komputasi Model Komputasi DNA Kimia Organic Memory Devices Ilmu Biologi Protipe Retina dari Silikon Proyek Database Genom Manusia Medicine Model Database Genom Manusia Analisa Komputer Terhadap Struktur Enzim untuk Kesehatan ACM Computing Classification System (CCS) Association for Computing Machinary (ACM) sebagai asosiasi ilmiah bidang komputer tertua di dunia juga menyusun sistem klasifikasi untuk bidang komputasi (computing), yang terkenal dengan sebutan ACM Computing Classification System (CSS). ACM Computing Classification System terbagi menjadi tiga level, dimana sistem penyusunannya mirip dengan Dewey Decimal Classification System (DCC) yang saat ini digunakan sebagai standar penyusunan katalog buku di perpustakaan-perpustakaan. Sistem Klasifikasi ini terbagi menjadi tiga besar, berdasarkan tahun dikeluarkannya. Klasifikasi selengkapnya adalah seperti dibawah. Sistem Klasifikasi 1998 Sistem Klasifikasi 1991 Sistem Klasifikasi 1964 Referensi Peter Denning, et al., "Computing as a Discipline," Communications of ACM, 32, 1 (January), 9-23, 1989. Peter Denning, "Computer Science: the Discipline," In Encyclopedia of Computer Science (A. Ralston and D. Hemmendinger, Eds), 1999. A. Tucker, Jr. and P. Wegner, "Computer Science and Engineering: the Discipline and Its Impact," In Handbook of Computer Science and Engineering, CRC Press, Chapter 1, 1996.

Selengkapnya...

Pengenalan Klasifikasi Ilmu Komputer

- Penulis: Romi Satria Wahono - Bahasa: Indonesia - Format file: HTML - Publisher: IlmuKomputer.Com - Tahun terbit: April 2003 Dasar Klasifikasi Ilmu komputer adalah disiplin ilmu yang melingkupi cabang ilmu yang cukup luas, dari masalah teori-teori dasar sampai teknologi aplikasi. Pengklasifikasian Ilmu Komputer biasanya merefer ke Matriks Dennings, yaitu salah satu matriks penggolongan Ilmu Komputer yang diciptakan oleh Peter J. Dennings [1][2]. Klasifikasi ini mengalami beberapa perbaikan, dimana versi terakhir adalah versi tahun 1999 [2][3]. Dalam versi terakhir ini ilmu komputer terbagi dalam 12 subbidang (versi sebelumnya adalah 9 subbidang). 12 subbidang Ilmu Komputer ini adalah: Algoritma dan Struktur Data (Algorithms and Data Structures) Bahasa Pemrograman (Programming Languages) Arsitektur (Architecture) Sistem Operasi dan Jaringan (Operating Systems and Networks) Software Engineering Database dan Sistim Retrieval Informasi (Database and Information Retrieval Systems) Artificial Intelligence dan Robotik (Artificial Intelligence and Robotics) Grafik (Graphics) Human Computer Interaction Ilmu Komputasi (Computational Sciences) Organizational Informatics BioInformatik (BioInformatics) Dennings memberi catatan khusus untuk bidang BioInformatik sebagai bidang baru yang merupakan gabungan antara Ilmu Komputer dan Biologi, dan saat ini mengalami perkembangan yang cukup signifikan. Kemudian seiring dengan peningkatan ilmu dan teknologi, ada kemungkinan matriks ini akan mengalami perbaikan lagi di kelak kemudian hari. Baris dalam matriks Denning ini menggambarkan bidang-bidang dalam ilmu komputer. Sedangkan kolom pada matriks menggambarkan paradigma bidang-bidang tersebut, yang direfleksikan dalam tiga hal: Teori (Theory), Abstraksi (Abstraction), dan Desain (Design). Teori: adalah berlandaskan pada pendekatan matematika, dimana untuk mendapatkan suatu teori yang valid, harus melalui proses-proses sbb: pendefinisian (definition) pembuatan teorema (theorema) pembuktian (proof) penginterpretasian hasil (interpret result) Abstraksi: atau pemodelan (modeling), adalah berlandaskan pada metode eksperimen ilmiah, dimana dalam melakukan invesitigasi terhadap suatu fenomena, harus melalui proses-proses sbb: membentuk hipotesa (form a hypothesis) membuat suatu model dan melakukan prediksi (construct a model and make a predition) mendesain eksperimen dan mengumpulkan data (design an experiment and collect data) menganalisa hasil (analyze hasil) Desain: adalah berlandaskan pada pendekatann engineering (teknik), dimana pada saat mendesain sebuah sistem atau device untuk memecahkan masalah, harus melalui proses-proses sbb: menyatakan requirement (state requirements), menyatakan spesifikasi (state specifications) melakukan desain dan implementasi sistem (design and implement the system) melakukan pengetesan terhadap sistem (test the system) Dari penjelasan diatas, bisa kita pahami bahwa yang bergerak dalam masalah penelitian ilmu komputer akan banyak berhubungan dengan dua kolom pertama matriks (Teori dan Abstraksi). Sedangkan yang bergerak dalam masalah yang lebih teknis dengan memakai pendekatan engineering, akan lebih banyak berkecimpung dalam ruang lingkup dua kolom terakhir matriks (Abstraksi dan Desain). Klasifikasi Ilmu Komputer Rangkumkan lengkap klasifikasi ilmu komputer berdasarkan Matriks Denning versi 1999 adalah seperti dibawah. Penulis sengaja melakukan perbaikan, penerjemahan, dan penyingkatan, untuk lebih mempermudah pemahaman terhadap klasifikasi ilmu komputer ini. Teori Abstraksi Desain Algoritma dan Struktur Data Teori Komputabilitas Algoritma Paralel dan Terdistribusi Program Aplikasi Teori Komputasi Kompleks Komputasi Paralel Algoritma Efisien dan Optimal Teori Graf Kriptografi Algoritma dan Teori Probabilistik Bahasa Pemrograman Bahasa Formal dan Automata BNF Bahasa Pemrograman Turing Machines Metode Parsing, Compiling, Interpretation Formal Semantics Translator, Kompiler, Interpreter Arsitektur Aljabar Boolean Arsitektur Nueman Produk Hardware (PC, Superkomputer, Mesin Von Neumann) Teori Coding Hardware Reliability Teori Switching Finite State Machine Sistem CAD dan Simulasi Logika Teori Finite State Machine Model Sirkuit, Data Path, Struktur Kontrol Sistem Operasi dan Jaringan Teori Concurrency Manajemen Memori, Job Scheduling Produk OS (UNIX, Windows, Mach, dsb) Teori Scheduling Model Komputer Terdistribusi File dan File Sistem Teori Manajemen Memori Networking (Protokol, Naming, dsb) Pustaka untuk Utilities (Editor, Formatter, Linker, dsb) Software Engineering Teori Reliability Metode Spesifikasi Bahasa Spesifikasi Program Verification and Proof Metode Otomatisasi Pengembangan Program Metodologi Pengembangan Software Temporal Logic Tool Pengembangan Software Tool untuk Pengembangan Software Database dan Sistim Retrieval Informasi Relational Aljabar dan Kalkulus Data Model Teknik Pendesainan Database (Relational, Hierarchical, Network, dsb) Teori Dependency Teori Concurrency Skima Database Teknik Pendesainan Database Sistem (Ingres, Dbase, Oracle, dsb) Performance Analysis Sorting dan Searching Representasi File untu Retrieval Hypertext System Statistical Inference Artificial Intelligence dan Robotik Teori Logika Knowledge Representation Logic Programming (Prolog) Semantik dan Sintatik Model untuk Natural Language Metode Pencarian Heuristic Neural Network Conceptual Dependency Model Reasoning dan Learning Sistem Pakar Kinematics and Dynamics of Robot Motion Model Memori Manusia, Autonomous Learning Teknik Pendesaian Software untuk Logic Programming Grafik Teori Grafik dan Warna Algoritma Komputer Grafik Pustaka untuk Grafik Geometri Dimensi Dua atau Lebih Model untuk Virtual Reality Grafik Standar Teori Chaos Metode Komputer Grafik Image Enhacement System Human Computer Interaction Risk Analysis Pattern Recognition Flight Simulation Cognitive Psychology Sistem CAD Usability Engineering Ilmu Komputasi Number Theory Discrete Approximations, Fast Fourier Transform and Poisson Solvers Pustaka dan Paket untuk Tool Penelitian (Chem, Macsyma, Mathematica, Maple, Reduce, dsb) Binary Representation Backward Error Propagation Teori Quantum Finite Element Models, Organizational Informatics Organizational Science Model dan Simlasi berhubungan dengan organizational informatics Management Information Systems Decision Support Systems Decision Sciences Organizational Dynamics Bioinformatik Teori Komputasi Model Komputasi DNA Kimia Organic Memory Devices Ilmu Biologi Protipe Retina dari Silikon Proyek Database Genom Manusia Medicine Model Database Genom Manusia Analisa Komputer Terhadap Struktur Enzim untuk Kesehatan ACM Computing Classification System (CCS) Association for Computing Machinary (ACM) sebagai asosiasi ilmiah bidang komputer tertua di dunia juga menyusun sistem klasifikasi untuk bidang komputasi (computing), yang terkenal dengan sebutan ACM Computing Classification System (CSS). ACM Computing Classification System terbagi menjadi tiga level, dimana sistem penyusunannya mirip dengan Dewey Decimal Classification System (DCC) yang saat ini digunakan sebagai standar penyusunan katalog buku di perpustakaan-perpustakaan. Sistem Klasifikasi ini terbagi menjadi tiga besar, berdasarkan tahun dikeluarkannya. Klasifikasi selengkapnya adalah seperti dibawah. Sistem Klasifikasi 1998 Sistem Klasifikasi 1991 Sistem Klasifikasi 1964 Referensi Peter Denning, et al., "Computing as a Discipline," Communications of ACM, 32, 1 (January), 9-23, 1989. Peter Denning, "Computer Science: the Discipline," In Encyclopedia of Computer Science (A. Ralston and D. Hemmendinger, Eds), 1999. A. Tucker, Jr. and P. Wegner, "Computer Science and Engineering: the Discipline and Its Impact," In Handbook of Computer Science and Engineering, CRC Press, Chapter 1, 1996.

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