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International Journal of Artificial Intelligence
Published by Lamintang Education & Training Centre
ISSN : 24077275     EISSN : 26863251     DOI : https://doi.org/10.36079/lamintang.ijai
Core Subject : Science,
Computer Science & IT Decision Sciences, Operations Research & Management
The aim is to publish high-quality articles dedicated to Artificial Intelligence. IJAI published in biannual, and in Indonesian, Malay and English.
Arjuna Subject : -
Articles 67 Documents
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Design and Evaluation of a Fuzzy Logic Based Intrusion Detection System for Network Security Ayomitope Isijola; Emmanuel Afuadajo; Michael Asefon; Ufuoma Ogude; Jamiu Akande; Promise Joseph
International Journal of Artificial Intelligence Vol 12 No 2: December 2025
Publisher : Lamintang Education and Training Centre, in collaboration with the International Association of Educators, Scientists, Technologists, and Engineers (IA-ESTE)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36079/lamintang.ijai-01202.870

Abstract

With the proliferation of networked systems, intrusion detection systems (IDS) have become vital in identifying and mitigating cyber threats and unauthorized access. Traditional IDS approaches, such as signature-based and anomaly-based methods, often struggle to detect novel attacks and tend to generate high false alarm rates. This study presents a robust, fuzzy logic-based IDS designed to detect network intrusions and assess their risk levels while minimizing false positives. The IDS classifies network intrusions by analyzing parameters such as source bytes, destination bytes, and packet rates, categorizing them into risk levels through defined fuzzy rules. Implemented in Python using libraries like scikit-fuzzy and pandas, the system utilizes the KDD Cup 99 dataset, a widely recognized IDS benchmark. Fuzzy membership functions and inference rules were defined for the primary input variables, enabling the system to infer intrusion likelihood. The IDS was tested using both two-variable and multi-variable input setups. It achieved a precision of 0.89, a recall of 0.85, and an F1-score of 0.87 in the multi-variable scenario. Results indicate that the fuzzy logic-based IDS achieves a balanced trade-off between detection accuracy and interpretability. It offers a transparent decision-making framework suitable for real-time applications due to its adaptability and potential for integration with live data streams. This research proposes future improvements by creating a foundation for hybrid intrusion detection systems (IDS) that integrate fuzzy logic and machine learning to enhance accuracy and interpretability. It recommends future research on adaptive fuzzy rules, real-time data processing, and explainable AI (XAI) to improve system flexibility, responsiveness, and transparency in cybersecurity applications.
Automatic Pose Recognition in Basketball Videos Using Entropy, Mean and Standard Deviation Aliga Paul; Joshua Nehinbe; Kingsley Eghonghon Ukhurebor
International Journal of Artificial Intelligence Vol 12 No 2: December 2025
Publisher : Lamintang Education and Training Centre, in collaboration with the International Association of Educators, Scientists, Technologists, and Engineers (IA-ESTE)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36079/lamintang.ijai-01202.908

Abstract

Most existing models for automatic action recognition in basketball videos lack privacy-friendly analytics, versatility and explainability. So, coaches, players and analysts often invest substantial resources by relying heavily on visual appearance, ball tracking and court context. Unfortunately, this method can be resource-intensive and potentially susceptible to unforeseeable intrusions. This study proposes an entropy-based analytical model for automatic recognition of key basketball actions, designed to optimize the video review process to address the above limitations. The model is implemented with Python programming language to analyze entropy arrays, the mean and standard deviation values derived from 22 basketball game videos. Evaluation suggests that the model flagged basketball_Video2, Video3 and Video9 as containing key moments deserving closer inspection. This has successfully reduced the input datasets to just three critical videos (with mean and standard deviation pairs of 1.96 & 0.33, 2.05 & 0.31, and 1.94 & 0.20) that warrant detailed examination. This targeted filtering significantly improves review efficiency by conserving time and resources and effectively eliminated 19 videos deemed redundant or of lower priority. The approach demonstrates high precision in identifying impactful gameplay moments and addresses a long-standing challenge with workload reduction in basketball analytics without sacrificing review accuracy. Consequently, this method not only supports privacy-conscious analytics but also provides coaches, players and sports analysts with a more focused, resource-efficient framework they can adopt for performance evaluation and strategic decision-making in basketball.
Student Expense Tracking System Using OCR Ahmad Fadli Saad; Muhammad Hairil Shaharudin; Achmad Yani; Abdi Manaf; Andi Almeira Zocha Ismail; Andi Regina Acacia Ismail
International Journal of Artificial Intelligence Vol 12 No 2: December 2025
Publisher : Lamintang Education and Training Centre, in collaboration with the International Association of Educators, Scientists, Technologists, and Engineers (IA-ESTE)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36079/lamintang.ijai-01202.929

Abstract

Nowadays student schedules are packed with their academic and curricular activities. Therefore, Students are no longer tracking their expenses because it is so hard to keep track with their expenses when they a have busy life. The aim of this research is to help students easily track their expenses by automating the process of extracting information from receipts. This research presents a student tracking expenses system using Optical Character Recognition (OCR) technology. The method that was used to develop the system was Website Development Life Cycle (WDLC). The system also uses Image Processing that implements OCR into the system. The system has been tested with a set of sample receipts, and the results show that it is able to accurately extract the relevant information with a high level of efficiency. The initial of this research involved designing the system, which was achieved through the creation of a detailed mockup and wireframe to establish a clear vision for its design. Then, it focused on developing the system, incorporating OCR technology to extract text from receipts. Thorough functional testing ensured that all system features, including user identification, image upload and OCR processing, expenditure management, budget setting, and data visualization, functioned as intended. The system offers users accurate and dependable capabilities for spending pattern analysis, budget management, and expense monitoring. Furthermore, the usability testing was conducted using the Post-Study System Usability Questionnaire (PSSUQ) from 30 students. The mean score of the System Usefulness, Information Quality and Overall Satisfaction is above 4 which indicates that it was appreciated by the students or respondents. Therefore, this system can be a valuable tool for students to manage their finances and make informed decisions about their spending.
The Development of Sensors for Microplastic Detection Using Artificial Intelligence Bhanuprasad Telu; Madhavi Konne; Lokabhiram Gunda; Vishnu Vardhan Gurram; Hari Narayana Nakka; Siddu Bhavirisetti; Pandu Ranga Surya Satyam Devapati
International Journal of Artificial Intelligence Vol 12 No 2: December 2025
Publisher : Lamintang Education and Training Centre, in collaboration with the International Association of Educators, Scientists, Technologists, and Engineers (IA-ESTE)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36079/lamintang.ijai-01202.934

Abstract

The increasing spread of microplastics throughout the world aquatic ecosystems is a significant ecological and health risk, which highlights an immediate need to develop sophisticated strategies of detection and characterization. The existing analytical approaches to microplastic quantification and identification are commonly not only labor-intensive but also time-consuming and restricted in terms of throughput especially in complicated matrices like soil, river water as well as biosolid fertilizers. Therefore, high-speed, dependable and affordable detection systems are the key to successful environmental surveillance and control measures. To break those limitations, this paper examines the means of integrating artificial intelligence with sophisticated sensor technologies and provides a detailed analysis of the current solutions and suggests new ones to detect microplastic better. In particular, this paper explores the usage of machine learning algorithms to process sensor data, thus making it possible to more efficiently and timely identify, quantify, and even classify microplastic particles. This research paper will seek to give a comprehensive history of some of the sensor modalities, including spectroscopies, optical, and electrochemical techniques, as well as a critical analysis of the AI models, such as deep learning and machine learning, that can be used together to create strong microplastic detection systems. The challenges that this integration tackles include high detection limit, and inability to operate in a portable mode, which is characteristic of the traditional approaches, leading to higher-end, real-time monitoring.
A Standard Deep Learning-Based Model That Integrates R3D-18 With RNN for Clip Classification and Transition Point Detection in Basketball Video Footages Aliga Paul; Joshua Nehinbe; Kingsley Eghonghon Ukhurebor
International Journal of Artificial Intelligence Vol 13 No 1: June 2026
Publisher : Lamintang Education and Training Centre, in collaboration with the International Association of Educators, Scientists, Technologists, and Engineers (IA-ESTE)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36079/lamintang.ijai-01301.883

Abstract

Research on basketball video clip analysis is rapidly emerging as a vital tool for coaching, player development, scouting, recruitment of coaches and players. Such analysis enhances post-game review, tactical planning, practice design, in-game adjustments, visual feedback and decision-making. However, many existing models lack robust architectures for transition detection, limiting their effectiveness. Additionally, high costs, limited customization, internet dependency and complex interfaces restrict widespread adoption of most models across professional and grassroots levels. To address these challenges, this paper implements a deep learning model with Python language. The model integrates Residual 3D Network with 18 layers (R3D-18) to capture spatial-temporal features, Convolutional neural networks (CNNs) to perform feature extraction and Recurrent Neural Network (RNN) to accomplish temporal modeling of offline basketball videos. Using entropy thresholding and top-k (i.e., top-10) accuracy metrics on four basketball datasets, the model performs clip-wise and video-level action classification with exceptional confidence, achieving 99.92% and 99.52% certainty that all the evaluative footages depict basketball videos. The model further detected no transitions in basketball_video1 and basketball_video3 while notable transitions appeared at clips 8 and 7 in basketball_video2 and basketball_video4, respectively corresponding to spikes in uncertainty likely caused by activity changes like switching from basketball to running. Average entropy scores of 4.787, 3.5341, 3.7912 and 3.1976 across datasets were reported, reflecting potentially elusive variations and uncertainties within clip-level predictions due to nuanced activity shifts despite strong overall classification confidence.
Capability-Specific Degradation Patterns in Quantized Small Language Models Emil Rahimov
International Journal of Artificial Intelligence Vol 13 No 1: June 2026
Publisher : Lamintang Education and Training Centre, in collaboration with the International Association of Educators, Scientists, Technologists, and Engineers (IA-ESTE)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36079/lamintang.ijai-01301.1050

Abstract

Post-training quantization is the default method for deploying small language models (one to four billion parameters) on consumer and edge hardware, yet its effect is usually summarized by a single aggregate accuracy score that can conceal severe failures in individual capabilities. This paper presents a capability-level analysis of four-bit quantization for seven open instruction-tuned small language models drawn from five architecture families: Qwen2.5, Llama-3.2, Gemma-2, Phi-3.5, and SmolLM2. Each model is evaluated at sixteen-bit floating point and at four-bit precision across six capabilities, namely factual knowledge, commonsense reasoning, mathematical reasoning, multilingual mathematical reasoning, code generation, and instruction following, yielding eighty-four controlled evaluations. Instead of reporting only mean accuracy, we construct a per-capability degradation map and test, using Kendall's rank correlation, whether capabilities degrade in a consistent order across architectures. The results show that degradation is strongly capability-specific: multilingual mathematical reasoning and code generation are the most fragile capabilities, with relative losses of up to fifty-seven percent, whereas commonsense reasoning is almost entirely preserved. However, the ordering of degradation is only weakly consistent across architectures, with a mean Kendall's tau of 0.29, indicating that the most and least fragile capabilities are shared but the overall ranking is architecture-dependent. Smaller models degrade more in magnitude. We conclude that small-model quantization should be evaluated per capability rather than in aggregate.
Fuzzy Logic for Heart Disease Prediction Muhammad Raihan Rusli; Normalisa
International Journal of Artificial Intelligence Vol 13 No 1: June 2026
Publisher : Lamintang Education and Training Centre, in collaboration with the International Association of Educators, Scientists, Technologists, and Engineers (IA-ESTE)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36079/lamintang.ijai-01301.1057

Abstract

Cardiovascular disease remains one of the leading causes of mortality worldwide, emphasizing the importance of early and accurate risk prediction systems. This study proposes a web-based heart disease prediction system utilizing a fuzzy logic approach to effectively manage uncertainty and imprecision in clinical data. As illustrated in the system interface, the model incorporates five primary input parameters: blood pressure, blood sugar level, cholesterol level, body mass index (BMI), and family history. These parameters are transformed into linguistic variables through fuzzification and evaluated using a rule-based fuzzy inference system constructed from expert knowledge and recent research findings. The fuzzy logic framework enables flexible decision-making that closely resembles human reasoning, overcoming limitations of conventional threshold-based diagnostic methods. The system produces an interpretable heart disease risk classification, such as low or high risk, which is presented through an interactive web interface along with a prediction history feature. Based on a review of related studies published within the last five years, fuzzy logic and neuro-fuzzy models have shown strong performance, transparency, and suitability for clinical decision support. The proposed system demonstrates the practicality of integrating fuzzy logic into web applications to support early heart disease risk assessment and preventive healthcare.

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