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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
 1 
Search results for , issue "Vol. 2 No. 1 (2025): March" : 5 Documents clear
Classification of Aquatic Species in Cultivation Ponds via Image Processing and Machine Learning Setiawan, Arif; Wahyu Wibowo, Angga; Setiaji, Pratomo; Agus Triyanto, Wiwit; Arifin, Muhammad
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.i1.9

Abstract

Fish cultivation is a vital economic activity for coastal communities, yet traditional farming methods often face challenges such as environmental instability, feeding inefficiencies, and water pollution. Effective monitoring of underwater environments is essential to improve fish quality and farming efficiency. A crucial part of this process is the accurate classification of fish and non-fish objects. This study proposes a method for underwater classification using morphometric feature extraction and machine learning techniques. The research process involves six main steps: (1) preparation of Region of Interest (ROI) detection data, (2) extraction of morphometric features—length (L) and width (W), (3) feature computation, (4) data partitioning for training and testing, (5) classification using Support Vector Machine (SVM), Random Forest (RF), and K-Nearest Neighbor (KNN), and (6) evaluation using a confusion matrix. Among all models tested, the Random Forest algorithm yielded the highest accuracy at 93%, with classification results showing True Positive = 349, False Positive = 28, True Negative = 223, and False Negative = 0. The findings highlight RF’s potential for enhancing automated fish monitoring in smart aquaculture systems.
Enhancing Security Protocols for MANETs in 5G-Enabled Smart Healthcare Systems Mabina, Alton
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.i1.15

Abstract

Mobile Ad Hoc Networks (MANETs) and 5G technologies offer transformative capabilities for healthcare systems, especially in developing countries like Botswana. MANETs provide decentralized, flexible connectivity, while 5G ensures high-speed, low-latency communication—together enabling critical services such as telemedicine, real-time patient monitoring, and emergency response. However, their integration introduces significant security risks, including data breaches, unauthorized access, and system vulnerabilities. This paper proposes a Comprehensive Multi-Layer Security Framework to address these challenges, combining encryption, secure MANET routing protocols, 5G network slicing, blockchain authentication, and AI-driven intrusion detection. The framework aims to secure patient data at every network layer, enhancing system integrity, confidentiality, and availability. Implementation strategies include phased infrastructure development, workforce training, and the creation of data protection regulations. The study also emphasizes the importance of international cooperation and technology adaptation for resource-constrained environments. By adopting this model, Botswana can establish a secure, scalable healthcare infrastructure that supports innovation and improves access to quality care.
A Hybrid Image Processing Approach for Real-Time Face Recognition in Attendance Monitoring Agho, Davit Cany; Hendrawan, Aria
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.i1.17

Abstract

In the era of digital transformation, institutions are increasingly adopting automation to enhance administrative efficiency, particularly in human resource management. At Tanggirejo Village Hall, a critically low employee attendance rate of 46.45% in January 2024 exposed the limitations of manual attendance systems, which are prone to errors and manipulation. This study proposes a face recognition-based attendance system utilizing OpenCV’s Haar Cascade Classifier for face detection and the Local Binary Pattern Histogram (LBPH) for face recognition. A total of 500 grayscale facial images from 10 employees were collected and processed to train and test the system. Evaluation using a Confusion Matrix revealed an accuracy of 72%, precision of 93%, and recall of 75%. While a 27% error rate was observed—primarily due to lighting inconsistencies and limited training data—the system performed reliably in real-time scenarios. The integration of these lightweight algorithms allows for fast and accurate identification, suitable for resource-constrained environments. This solution not only addresses the local attendance challenges but also presents a scalable, automated model that can be adopted by similar institutions seeking to improve productivity and operational transparency through real-time employee monitoring.
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.

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