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INDONESIA
International Journal of Artificial Intelligence and Science
Published by Asosiasi Doktor Sistem Informasi Indonesia
ISSN : -     EISSN : 30642728     DOI : https://doi.org/10.63158/IJAIS
Core Subject : Science,
Computer Science & IT
The International Journal of Artificial Intelligence and Science (IJAIS) is independently organized and managed by the Asosiasi Doktor Sistem Informasi Indonesia (ADSII). IJAIS is an open-access journal designed for researchers, lecturers, and students to publish their findings in the fields of Artificial Intelligence and Science. IJAIS serves as a platform for sharing innovative and original research, showcasing the latest advancements and technological developments in Artificial Intelligence and Science.
Arjuna Subject : Ilmu Komputer (semua kategori) - Kecerdasan Buatan
Articles 2 Documents
 1 
Mobile Ad Hoc Network (MANET) Performance in Disaster Recovery Mabina, Alton
International Journal of Artificial Intelligence and Science Vol. 2 No. 2 (2025): September
Publisher : Asosiasi Doktor Sistem Informasi Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.63158/IJAIS.v2i2.16

Abstract

This study evaluates the performance of Mobile Ad Hoc Networks (MANETs) in disaster recovery, addressing the gap in existing research that primarily focuses on network performance metrics. The study aims to provide a comprehensive evaluation using the Balanced Scorecard (BSC) framework, considering financial, user, process, and innovation perspectives. A quantitative approach is employed, synthesizing data from existing literature, case studies, and empirical research on MANET deployments in disaster scenarios. Key performance indicators (KPIs) are categorized into the four BSC dimensions: network efficiency (process), cost-effectiveness (financial), usability (user), and innovation capacity. The study finds that MANETs significantly enhance communication resilience during disasters but face challenges in scalability, energy consumption, and security. The BSC framework identifies high deployment feasibility and operational efficiency but highlights limitations in long-term sustainability and integration with satellite/terrestrial networks. Unlike previous studies focused solely on technical parameters, this research offers a holistic evaluation by integrating the BSC framework, providing a more comprehensive analysis. The findings suggest that adaptive routing, AI-driven optimizations, and hybrid MANET-Satellite models could improve network performance. Future research should explore real-world deployments, energy-efficient protocols, and enhanced security models using blockchain.
Web-Based Electric Bicycle Fault Diagnosis Using the Backward Chaining Method Nugroho, Satrio Wicaksono; Dwi Nor Amadi; Pradityo Utomo; Candra Budi Susila
International Journal of Artificial Intelligence and Science Vol. 2 No. 2 (2025): September
Publisher : Asosiasi Doktor Sistem Informasi Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.63158/IJAIS.v2i2.35

Abstract

This study aims to develop a web-based expert system for diagnosing electric bicycle faults using the backward chaining method. It addresses the limitation of previous systems that did not support user input of fault hypotheses. The research stages include literature review, data collection (31 faults and 5 symptoms), implementation of web-based inference, and black box testing. The results demonstrate that the system successfully accommodates user-input hypotheses and related symptoms, then matches them with rules to generate diagnoses. Functional testing confirms all features operate as intended. The research novelty lies in: (1) the first comprehensive knowledge base for electric bicycles (31 faults), (2) an interactive web interface supporting hypothesis input, and (3) dynamic database storage for rule updates.

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