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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 5 Documents
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Search results for , issue "Vol. 2 No. 2 (2025): September" : 5 Documents clear
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.
Selection of the Best Futsal Player at the Bhirawa Cup Event Using the Simple Multi Attribute Rating Technique Method Fatah, Arjulian; Utomo, Pradityo; Susila, Candra Budi
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.v2i2.40

Abstract

The selection of the best futsal player is an important aspect of a tournament, as it can motivate athletes to continuously improve their performance. However, manual selection processes tend to be subjective and prone to bias, thus reducing the objectivity of the assessment results. This study aims to design and develop a web-based decision support system using the Simple Multi-Attribute Rating Technique (SMART) method to assist the organizing committee in objectively and standardizedly evaluating player performance at the Bhirawa Cup 2024 futsal event. The research method used is the Waterfall software engineering model, which consists of the stages of requirements analysis, system design, implementation, testing, and maintenance. The system evaluates player performance based on four main criteria: contribution to the team, number of fouls, attitude, and leadership, each of which is assigned a weight according to its level of importance. The result of this study is a web-based decision support system that can be used by the event committee to assess and determine the best player. The strength of this system lies in its ability to present structured data and minimize assessment subjectivity. A suggestion for future development is to make the system accessible online to increase its flexibility
Speech-Driven Visitor Notification System Using Telegram Bot and Voice Activity Detection for Real-Time Retail Applications Setiaji, Aria; Hendrawan, Aria; Christioko, Bernadus Very; Huizen, Lenny Margaretta
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.v2i2.44

Abstract

In the retail industry, fast and responsive service is essential for maintaining customer satisfaction and loyalty. A key challenge faced by store owners is delayed responses to customer arrivals, leading to dissatisfaction and potential lost sales. This project develops an automatic notification system using Speech-to-Text technology and a Telegram bot to detect voice keywords and send real-time notifications to store owners. The system was developed using the prototype methodology, allowing for iterative testing and refinement to ensure it met user needs and functional requirements. It integrates the Deepgram API for accurate speech transcription, the Telegram Bot API for notifications, and a web interface for managing keywords and monitoring system status. To enhance efficiency, a Voice Activity Detection (VAD) module was added, ensuring that only human speech is processed, thereby reducing unnecessary processing. Experimental results showed that the system achieved 100% accuracy in quiet environments and 80% in noisy conditions. The system's response time was also impressive, with an average time of 3.72 seconds in quiet conditions and 3.8 seconds in noisy environments. Word Error Rate (WER) and Character Error Rate (CER) evaluations indicated perfect accuracy in quiet conditions (WER 0%, CER 0%) and slight errors in noisy conditions (WER 13.33%, CER 12.5%). Overall, the system effectively improved service speed and responsiveness, offering store owners a valuable tool for enhancing customer experience in retail environments.
Enhanced Detection of Indonesian Online Gambling Advertisements Using Multimodal Ensemble Deep Learning Alfiansyah, M Ihksan; Muzakir, Ari
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.v2i2.49

Abstract

The rapid growth of online gambling promotion on Indonesian social media creates significant challenges for automated moderation systems, particularly because the content often appears in multimodal forms, uses slang expressions, and disguises promotional intent. The purpose of this study is to improve the accuracy and robustness of gambling advertisement detection by proposing a multimodal ensemble deep learning framework that integrates information from text, images, and audio. The method combines three independent feature streams, namely native text, OCR-extracted text from images, and ASR-generated speech transcripts. These inputs are processed using three classifiers, namely CNN, BiLSTM, and IndoBERT, which are then fused using a weighted soft-voting ensemble strategy. A dataset consisting of 12,000 multimodal samples collected from Facebook, Instagram, TikTok, and YouTube was used for evaluation. The results show that the ensemble model achieves an accuracy of 95.42 percent, outperforming each individual classifier, with substantial improvements in handling noisy OCR and ASR outputs as well as implicit gambling slang. Compared with single-model baselines, the proposed approach reduces false positives by 18.6 percent and false negatives by 22.3 percent. The novelty of this study lies in the integration of multimodal feature streams with an optimized ensemble mechanism, enabling more reliable detection of concealed gambling promotional patterns. The findings provide a strong foundation for future research on adaptive moderation systems and real-time harmful content detection in Indonesian social media.

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