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Applied Information System and Management (AISM)

Publisher :

Department of Information Systems, Faculty of Science and Technology

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Peer reviewed only
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Open Access
  • Information Systems
e-ISSN :

2621-2544

Issue Frequency :

Half-Yearly

p-ISSN :

2621-2536

Est. Year :

2019

Mobile :

82114627822

DOI :

YES

Country :

Indonesia

Language :

English

APC :

YES

Impact Factor Assignee :

Google Scholar

Email :

aism.journal@uinjkt.ac.id

Journal Descriptions

Applied Information System and Management (AISM) is a peer-reviewed, scientific journal published by the Department of Information Systems, Faculty of Science and Technology, Universitas Islam Negeri (UIN) Syarif Hidayatullah Jakarta, Indonesia collaborated with Communication Forum of Indonesian Scientific Journal Managers (Forum Komunikasi Pengelola Jurnal Ilmiah Indonesia-Forkom PJII). The aim is to publish the theoretical, methodological, and practical issues of Information Systems and its management. Its scope covers a critical view of the development, implementation, evaluation, operation, management, policy, and strategy issues of Information Systems. AISM is a bilingual journal in Bahasa Indonesia and English. However, starting in Vol. 6 No. 2, October 2023, the articles are in English. AISM is published twice a year, (2 issues/year, May and October). The journal provides archival-quality papers that advance the state-of-the-art and practical applications of IS-enabled technologies and IS-based service management. It also brings accessible articles describing original research in the inherently interdisciplinary world of Information Technology and Information Systems. Theoretical research contributions (presenting new concepts and techniques) and applied contributions (reporting on experiences and experiments with actual systems) are encouraged.


Applied Information System and Management (AISM) is :

International, Peer-Reviewed, Open Access, Refereed, Information Systems , Online or Print, Half-Yearly Journal

UGC Approved, ISSN Approved: P-ISSN - 2621-2536, E-ISSN - 2621-2544, Established in - 2019, Impact Factor

Provide Crossref DOI

Not indexed in Scopus, WoS, DOAJ, PubMed, UGC CARE

Indexing

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Publications of AISM

Research Article
  • dott image Rahul Vaghela
  • dott image March, 2021

Credit Card Fraud Detection Using Machine Learning Approach

Using new spam technologies to carry out internet banking fraud refers to shifting and withdrawing money from the user’s balance account without it’s authorization. Credit card fraud pop...

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