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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 15 Documents
 1   2 
Artificial Neural Network for Investigating the Impact of EMF on Ignition of Flammable Vapors in Gas Stations Umoren, Imeh; Inyang, Saviour; Etuk, Ubong; Akpanobong , Aloysius; James, Gabriel
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.i1.19

Abstract

The inadvertent ignition of flammable vapors by radio frequency (RF) radiation poses a significant safety risk in mega gas stations, necessitating the development of an intelligent predictive model for hazard prevention. This study proposes Artificial Neural Networks (ANN) Model to classify and predict ignition risks based on structured datasets obtained from smart sensing devices. The model formulation is based on the perceptron architecture, incorporating threshold logic units (TLUs) and multi-layer perceptron’s (MLPs) with backpropagation learning for enhanced predictive accuracy. The dataset, preprocessed to remove noise and redundancy, was divided into an 80:20 training-to-testing ratio and evaluated using cross-validation techniques. The experimental results show that the ANN-based model achieved an accuracy of 86%, demonstrating its effectiveness in identifying the impact of hazardous conditions. These findings underscore the robustness of the proposed approach, offering a reliable solution for mitigating ignition hazards in industrial environments. This research contributes to advancing safety protocols by leveraging on machine learning for predictive hazard assessment in flammable vapor-prone areas.
Personalized Energy Optimization in Smart Homes Using Adaptive Machine Learning Models: A Feature-Driven Approach Oyeniran, Matthew; J.D., Adekunle; H.S., Sule; O., Folorunso; S.A, Alagbe; T. J., Anifowoshe; C. O., Robert; B. N., Ebonyem; E. G., Ideh; S. O., Oyelakin; C. K., Ogu
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.
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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