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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 28 Documents
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Personalized Energy Optimization in Smart Homes Using Adaptive Machine Learning Models: A Feature-Driven Approach Matthew Oyeniran; Adekunle J.D.; Sule H.S.; Folorunso O.; Alagbe S.A; Anifowoshe T. J.; Robert C. O.; Ebonyem B. N.; Ideh E. G.; Oyelakin S. O.; Ogu C. K.
International Journal of Artificial Intelligence and Science Vol. 2 No. 1 (2025): March
Publisher : Asosiasi Doktor Sistem Informasi Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.63158/IJAIS.v2.i1.20

Abstract

The increase in demand for efficient energy smart homes has necessitates the personalized optimization strategies to have a reduction in energy consumption while maintaining user comfort. This research develops a Personalized Energy Optimization System using adaptive machine learning models to analyze household energy patterns and predict consumption in real time. Leveraging the Appliances Energy Prediction Dataset from the UCI repository, we applied supervised learning algorithms such as Gradient Boosting, XGBoost, CatBoost, LightGBM, and Random Forest to identify key factors influencing energy use, including occupancy patterns, appliance usage, and environmental conditions. Through feature engineering, normalization, and one-hot encoding, we enhanced model performance and interpretability. Among the evaluated models, LightGBM achieved the highest accuracy (R²: 0.999573, RMSE: 0.013526), outperforming others in predicting energy consumption. The findings offer data-driven insights for dynamic energy management, optimizing household efficiency, and promoting sustainability.
Web-Based Electric Bicycle Fault Diagnosis Using the Backward Chaining Method Satrio Wicaksono Nugroho; 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 Arjulian Fatah; 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.v2i2.40

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 Aria Setiaji; Aria Hendrawan; Bernadus Very Christioko; Lenny Margaretta Huizen
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 M Ihksan Alfiansyah; Ari Muzakir
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.
AI-Driven Intrusion Detection in Healthcare Systems: A Systematic Review of Techniques, Evaluation Practices, and Real-World Deployment Challenges Belinda Ndlovu
International Journal of Artificial Intelligence and Science Vol. 3 No. 1 (2026): March
Publisher : Asosiasi Doktor Sistem Informasi Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.63158/IJAIS.v3i1.58

Abstract

Cyberattacks threaten healthcare data security and clinical continuity. Although artificial intelligence (AI) promises improved intrusion detection, high accuracy rarely translates into clinical deployment. This systematic review examines evaluation practices and deployment conditions in healthcare intrusion detection research. Following PRISMA 2020, searches across ScienceDirect, Scopus, PubMed, MDPI and SpringerLink covered studies published between 2020 and 2024. Eleven studies met the inclusion criteria. Findings were triangulated against subsequent large-scale reviews to assess consistency across broader evidence. Classical machine learning dominated, with limited adoption of hybrid, federated or transformer-based approaches. Reported accuracy averaged 98.66%, predominantly using benchmark or synthetic datasets under controlled conditions. False positive rates, alert volumes, inference latency and robustness to distribution shift were rarely reported. No included study demonstrated sustained operation in a live clinical network. The review introduces the Healthcare IDS Deployment Readiness Assessment (HIDRA), comprising five dimensions and five ordinal readiness levels, alongside a minimum reporting set and six testable propositions. No study exceeded readiness level 2 of 5. HIDRA offers authors, reviewers and procurement teams a reusable instrument for assessing deployment readiness and identifying gaps between experimental performance and clinical feasibility.
Awareness and Adoption of Big Data Technology in Public Sector Auditing in Tanzania: Mediation Effect of Knowledge Management Practices. Albert Raymond Moshi; Alfre Sife; George Matto
International Journal of Artificial Intelligence and Science Vol. 3 No. 1 (2026): March
Publisher : Asosiasi Doktor Sistem Informasi Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.63158/IJAIS.v3i1.60

Abstract

Big data technology enables auditors to identify patterns and anomalies across extensive datasets, supporting improved audit quality. However, its adoption in Tanzania’s public sector auditing remains limited. This study examines the mediating role of knowledge management practices in the relationship between awareness and readiness to adopt big data technology. Drawing on Diffusion of Innovation (DOI) theory and Knowledge Management Theory (KMT), it proposes that awareness provides the foundation for adoption readiness, while knowledge creation, sharing and application translate awareness into practical capabilities. Data were collected from 221 randomly selected respondents and analysed using descriptive statistics and binary logistic regression. The findings indicate that knowledge management practices mediate the relationship between awareness and adoption readiness, highlighting their role in developing skills for technological adoption. Descriptive results reveal high operational awareness but low technical awareness among auditors, indicating a technical skills gap. These findings suggest that awareness alone is insufficient without mechanisms for acquiring, sharing and applying relevant knowledge. Public sector auditing institutions in Tanzania should strengthen knowledge management through targeted training programmes and knowledge sharing platforms. The study contributes an integrated DOI–KMT perspective explaining how awareness and knowledge management jointly support readiness for big data adoption in public sector auditing.
Understanding Artificial Intelligence Ethics and Safety: Foundations, Challenges, and Governance Muhammad Sami Intizar; Aqsa Siddique; Maria Sikandar; Madiha Sikandar; Maria Farooq
International Journal of Artificial Intelligence and Science Vol. 3 No. 1 (2026): March
Publisher : Asosiasi Doktor Sistem Informasi Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.63158/IJAIS.v3i1.61

Abstract

The deployment of artificial intelligence (AI) in safety-critical domains has made ethics and safety priorities. This paper examines AI ethics and safety by synthesizing technical, philosophical, and governance perspectives. It analyzes ethical principles, including fairness, transparency, privacy, accountability, and safety, while examining tensions in their implementation. Technical approaches, including robustness mechanisms, adversarial training, and interpretability methods, are considered alongside frameworks for value alignment and corrigibility. The discussion highlights the challenges of translating values into operational requirements, when competing objectives and contextual differences complicate implementation. The paper also assesses the governance landscape, comparing regulatory approaches across jurisdictions and evaluating binding and non-binding instruments for AI development. It argues that governance requires integrating technical safeguards, ethical reflection, and institutional accountability throughout the AI lifecycle. Moving beyond principle-based declarations, the paper emphasizes auditable and enforceable practices that connect ethical commitments with implementation and oversight. This perspective supports responsible innovation while addressing the risks associated with safety-critical AI deployment.

Page 3 of 3 | Total Record : 28

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