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Catur Eri Gunawan
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Prodi Sistem Informasi Fakultas Sains dan Teknologi, Universitas Islam Negeri Raden Fatah Palembang. Jln. Prof. K.H. Zainal Abidin Fikri KM. 3.5 Palembang 30126.
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INDONESIA
JUSIFO : Jurnal Sistem Informasi
Published by Universitas Islam Negeri Raden Fatah Palembang
ISSN : 2460092X     EISSN : 26231662     DOI : -
Core Subject :
JUSIFO (Jurnal Sistem Informasi) is an Information System Journal that published by Program Studi Sistem Informasi, Fakultas Sains dan Teknologi, Universitas Islam Negeri Raden Fatah Palembang. JUSIFO (Jurnal Sistem Informasi) publishes numerous research articles concerning the articles that integrate technological disciplines with Information System Audit, Software Engineering, Decision Support System, Artificial Intelligence, Application of Information Technology, Social Informatics. JUSIFO (Jurnal Sistem Informasi) is published twice a year; in June and December. In this journal, reviewers will review any submitted paper. Review process employs a double-blind review, which means that both the reviewer and author identities are concealed from the reviewers, and vice versa. We hope that the articles published by JUSIFO (Jurnal Sistem Informasi) can make a real contribution and have a widely impact.
Arjuna Subject : Ilmu Komputer (semua kategori) - Sistem Informasi
Articles 153 Documents
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Exploring the Affordances of AI-Enabled Livestock Monitoring Systems in Rural Agricultural Communities Luthfi Ramadani; Widyatasya Agustika Nurtrisha; Faqih Hamami; Nur Ichsan Utama; Riska Yanu Fa’rifah
JUSIFO : Jurnal Sistem Informasi Vol 11 No 2 (2025): JUSIFO (Jurnal Sistem Informasi) | December 2025
Publisher : Program Studi Sistem Informasi, Fakultas Sains dan Teknologi, Universitas Islam Negeri Raden Fatah Palembang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.19109/jusifo.v11i2.31181

Abstract

Productivity and sustainability remain persistent challenges in livestock farming across developing countries, particularly in rural contexts where digital transformation progresses unevenly. Advances in artificial intelligence (AI) offer opportunities to support livestock management; however, empirical understanding of how such technologies are perceived and utilized in rural settings remains limited. This study examines the perceived affordances of an AI-enabled livestock monitoring system in a rural community in Central Java, Indonesia. Guided by the Technology–Organization–Environment (TOE) framework, a qualitative case study approach was employed using semi-structured interviews with livestock farmers and local government officials. The findings indicate that the realization of AI-related affordances is shaped by technological conditions, including system capabilities, infrastructure limitations, and user readiness. Organizational factors—such as innovation awareness, government–community relationships, and the continuity of support programs—also influence affordance realization. Environmental conditions, particularly training adequacy, public trust, and rural geographic characteristics, further affect technology use. Overall, the study highlights that AI affordances in rural livestock systems are socio-technical and context-dependent, emphasizing the importance of context-sensitive design and implementation strategies to support sustainable livestock management.
IT Governance Maturity Assessment of the BRAVO Application Using an Integrated COBIT 2019 and ITIL 4 Framework Zahra Diva Putri Munaspin; Dwi Rosa Indah; Habi Baturohmah; Ali Ibrahim; M. Rudi Sanjaya; M. Husni Syahbani
JUSIFO : Jurnal Sistem Informasi Vol 11 No 2 (2025): JUSIFO (Jurnal Sistem Informasi) | December 2025
Publisher : Program Studi Sistem Informasi, Fakultas Sains dan Teknologi, Universitas Islam Negeri Raden Fatah Palembang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.19109/jusifo.v11i2.31602

Abstract

This study evaluates the maturity of IT governance supporting the BRAVO (BPKB Registration Vehicle Online) application at the Traffic Directorate of the South Sumatra Regional Police, a law enforcement institution delivering digital public services. An integrated evaluation approach combining COBIT 2019 and ITIL V4 frameworks was employed to assess governance and service management practices. Using design factor analysis, RACI-based respondent mapping, maturity level assessment, and gap analysis, the study focused on three key governance objectives: MEA03 (Managed Compliance with External Requirements), DSS02 (Managed Service Requests and Incidents), and DSS03 (Managed Problems). The findings indicate that MEA03 and DSS02 have achieved Maturity Level 3, reflecting structured and consistently implemented processes, while DSS03 remains at Maturity Level 2, indicating limited institutionalization of problem management practices. The gap analysis reveals significant maturity gaps between current and targeted levels, highlighting the need for governance strengthening, improved documentation, and enhanced analytical use of service data. This study demonstrates that integrating COBIT 2019 and ITIL V4 provides a coherent framework for bridging IT governance and service management, offering practical insights for improving digital public service delivery in law enforcement and other public sector organizations.
Hybrid Fuzzy-AHP and Machine Learning with Sensitivity Analysis for Urban Flood Risk Assessment Agus Riyanto; Dwi Ismiyana Putri; Gita Puspa Artiani; Ali Khumaidi
JUSIFO : Jurnal Sistem Informasi Vol 11 No 2 (2025): JUSIFO (Jurnal Sistem Informasi) | December 2025
Publisher : Program Studi Sistem Informasi, Fakultas Sains dan Teknologi, Universitas Islam Negeri Raden Fatah Palembang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.19109/jusifo.v11i2.32227

Abstract

Urban flooding poses a growing challenge in rapidly urbanizing regions due to the combined effects of climate variability, land-use change, and infrastructure limitations. This study proposes a hybrid framework integrating the Fuzzy Analytical Hierarchy Process (Fuzzy-AHP), ensemble machine learning, and sensitivity analysis to support urban flood risk assessment. Fuzzy-AHP is employed to incorporate expert judgment and address uncertainty through triangular fuzzy numbers, while Random Forest and XGBoost are used to capture non-linear relationships and temporal patterns in heterogeneous flood-related data. The framework is applied to 1,008 observations from 12 districts in Bekasi City, Indonesia, covering the period 2018–2024. Model performance indicates strong discriminatory capability in distinguishing flood and non-flood conditions. Sensitivity analysis is explicitly positioned as a policy-oriented diagnostic and prioritization tool, enabling the identification of influential variables relevant for seasonal planning and early warning strategies. The results highlight the dominant role of climate-related factors, particularly rainfall and temporal variables, in shaping urban flood risk. Overall, the proposed framework demonstrates the complementary integration of expert knowledge and data-driven learning, offering a transferable methodological reference for flood risk assessment in complex urban environments.

Page 16 of 16 | Total Record : 153

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