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International Journal of Artificial Intelligence Research
Published by STMIK Dharma Wacana
ISSN : -     EISSN : 25797298     DOI : -
Core Subject : Science, Engineering,
Computer Science & IT Electrical & Electronics Engineering
International Journal Of Artificial Intelligence Research (IJAIR) is a peer-reviewed open-access journal. The journal invites scientists and engineers throughout the world to exchange and disseminate theoretical and practice-oriented topics of Artificial intelligent Research which covers four (4) majors areas of research that includes 1) Machine Learning and Soft Computing, 2) Data Mining & Big Data Analytics, 3) Computer Vision and Pattern Recognition, and 4) Automated reasoning. Submitted papers must be written in English for initial review stage by editors and further review process by minimum two international reviewers.
Arjuna Subject : -
Articles 621 Documents
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Application of Convolutional Neural Network Based on ResNet18 for Alzheimer Disease Classification Indarto, Aan; Kusrini, Kusrini
International Journal of Artificial Intelligence Research Vol 9, No 2 (2025): December
Publisher : Universitas Dharma Wacana

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29099/ijair.v9i2.1504

Abstract

Alzheimer's disease is a form of progressive dementia that significantly impacts the quality of life of patients and their families. Early detection based on Magnetic Resonance Imaging (MRI) can support faster and more accurate diagnosis, but manual classification requires high expertise and is subjective. This study aims to develop an Alzheimer's MRI image classification model using a Convolutional Neural Network (CNN) based on ResNet18 with transfer learning to classify data into four categories: Mild Demented, Moderate Demented, Non-Demented, and Very Mild Demented. The MRI dataset was processed through pre-processing involving 128×128 grayscale conversion, pixel intensity normalization, and class balancing using class weighting. The model was trained using the Adam optimizer (lr=0.0001) with Early Stopping (patience=7) over 50 epochs. Evaluation using the validation set showed that the model achieved high accuracy for the Non-Demented class. The result indicates that ResNet18 with transfer learning can achieve an accuracy of 94.4%, making this model an effective approach for medium-scale classification of Alzheimer's MRI images.
Power Analysis Of A 100 Watt Micro Hydro Power Generator Using An Internet Of Things (IoT) Web Services Based On The Code Igniter Framework Yunior, Yudhis Thiro Kabul
International Journal of Artificial Intelligence Research Vol 9, No 1.1 (2025)
Publisher : Universitas Dharma Wacana

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29099/ijair.v9i1.1.1633

Abstract

Small-scale Microhydro power plants (100 Watt) require a real-time and accurate power monitoring system to improve efficiency and maintenance. This research aims to develop an Internet of Things (IoT)-based electrical power analysis system by utilizing a Web Service based on the CodeIgniter Framework to process and display data online. The system consists of sensor modules (voltage and current) using ZMPT101B and ACS712, an ESP32 microcontroller for data transmission, and a CodeIgniter backend that provides a RESTful API for data storage and processing. Power (P), voltage (V), current (I), and energy (kWh) data are displayed on a web dashboard with graphic visualization using Chart.js. The research method uses a Research and Development (R&D) approach with stages of needs analysis, system design, implementation, and testing. The test results show that the system is able to monitor power with 95% accuracy compared to digital multimeter measurements, and has a data transmission latency of <2 seconds. This solution can be applied to small-scale Microhydro power plants for IoT-based monitoring with low cost and high scalability.
Explainable AI-Based Real-Time Hybrid System for Blockchain Anomaly Detection: A Multi-Cryptocurrency Perspective Shabaan, Amira Hamdi; Elkaffa, Saleh Mesbah; A. Said, Gamal Abd El-Nasser; Badawy, Ossama Mohamed
International Journal of Artificial Intelligence Research Vol 9, No 2 (2025): December
Publisher : Universitas Dharma Wacana

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29099/ijair.v9i2.1571

Abstract

This study achieves a 5% improvement in AUC-ROC and a 2.5% increase in recall compared to state-of-the-art anomaly detection methods in blockchain networks. Blockchain technologies have rapidly evolved, offering transparency and security across decentralized systems. However, detecting anomalies and fraudulent activities remains a significant challenge. This research proposes a unified hybrid framework integrating Graph Neural Networks (GNNs), Transformers, and XGBoost within a federated learning environment for real-time anomaly detection in multi-cryptocurrency blockchain networks. Unlike previous works, this model employs explainable AI (XAI) methods (SHAP and LIME) to enhance interpretability and trust. The framework utilizes PSO-based hyperparameter optimization, reducing convergence time by 20%. Experimental evaluations on benchmark datasets (Elliptic, Bitcoin-OTC, and Ethereum) demonstrate superior performance in precision, recall, and FPR compared to CARE-GNN and GeniePath. The results confirm the proposed model’s scalability, transparency, and real-time efficiency, making it suitable for deployment in high-frequency blockchain monitoring systems.  
A Multi-Feature Fusion Framework for Sentiment Analysis Based on Textual and Affective Signals Alkaabi, Hussein Ala'a; jasim, ali kadhim
International Journal of Artificial Intelligence Research Vol 9, No 2 (2025): December
Publisher : Universitas Dharma Wacana

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29099/ijair.v9i2.1634

Abstract

Sentiment analysis of social media content, particularly on platforms like Twitter, presents significant challenges due to the informal, brief, and context-dependent nature of user-generated text. Traditional lexicon-based and shallow machine learning approaches often fail to capture nuanced sentiment expressions, especially in the presence of slang, abbreviations, sarcasm, and emotionally charged language. To address these limitations, this paper proposes a novel tri-stream feature fusion framework that integrates contextual semantics, sequential dependencies, and affective signals for robust sentiment classification. The framework employs RoBERTa to extract rich contextual embeddings, Bidirectional Long Short-Term Memory (BiLSTM) networks to capture word-order and temporal patterns, and lexicon-based emotion vectors to enhance emotional cue detection. These heterogeneous features are concatenated at the representation level to form a comprehensive feature space, which is subsequently used to predict sentiment polarity via a fully connected neural network classifier. Extensive experiments conducted on the Sentiment140 dataset, comprising 1.6 million labeled tweets, demonstrate that the proposed approach significantly outperforms conventional baselines and recent hybrid models, achieving an accuracy of 92.1%. Additionally, ablation studies and misclassification analyses reveal each feature stream’s complementary contributions and highlight challenges in detecting sarcasm and implicit sentiment. Future work will integrate sarcasm-aware components and external knowledge sources to further enhance model interpretability and robustness.
Performance Improvement Analysis of Design and Build Construction Project Managers of State Builidngs Kusumawati, Jujuk
International Journal of Artificial Intelligence Research Vol 9, No 1.1 (2025)
Publisher : Universitas Dharma Wacana

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29099/ijair.v9i1.1.1654

Abstract

Project managers of planning and construction of government buildings are experts of the implementing contractor who determine the timeliness of the implementation of design and build construction. In order for timely implementation, project managers not only have higher education and long experience, but must have a good work culture and work behavior as well. Therefore, it is necessary to examine project performance based on the work performance of project managers
Performance of Deep Face Recognition Models under Adaptive Margin Loss: A Real-Time Evaluation Aditama, Kevin Muhammad Tegar; Nugroho, Anan; Subiyanto, Subiyanto; Pongoh, Arthur Gregorius
International Journal of Artificial Intelligence Research Vol 9, No 1.1 (2025)
Publisher : Universitas Dharma Wacana

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29099/ijair.v9i1.1.1641

Abstract

Real-time face recognition systems encounter a critical trade-off between high-security demands and computational efficiency, particularly when deployed in unconstrained open-set environments. This study presents a comprehensive benchmarking of four distinct deep learning backbones ResNet100, GhostFaceNet, LAFS, and TransFace specifically trained using the Adaptive Margin Loss (AdaFace) function to handle image quality variations. The primary objective is to identify the optimal architecture for secure attendance systems operating on standard hardware with limited training data. The evaluation protocol employs a rigorous real-world open-set test to quantify performance using False Acceptance Rate (FAR) and False Rejection Rate (FRR). The experimental results demonstrate that ResNet100 establishes the highest security standard, achieving a 0.00% FAR at strict thresholds. Meanwhile, GhostFaceNet emerges as the most balanced solution for resource-constrained deployments, delivering competitive accuracy above 93% with significantly lower computational complexity. Conversely, the Vision Transformer (TransFace) fails to generalize in this low-data regime, resulting in unacceptable false acceptance rates. These findings definitively recommend GhostFaceNet for efficient edge-based implementations, while ResNet100 remains the superior choice for mission-critical security applications.
Application of Computer Vision and Pattern Recognition in Automated Quality Inspection of Industrial Products Nurdiyanto, Heri; Kindiasari, Aktansi; Sulistiyanto, Sulistiyanto
International Journal of Artificial Intelligence Research Vol 9, No 2 (2025): December
Publisher : Universitas Dharma Wacana

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29099/ijair.v9i2.1507

Abstract

Quality inspection is a critical process in industrial production to ensure that products meet predefined standards and specifications. Traditionally, quality inspection has relied heavily on manual visual checks, which are time-consuming, subjective, and prone to human error. This study explores the application of computer vision and pattern recognition techniques to develop an automated quality inspection system for industrial products. The proposed system employs high-resolution cameras and image processing algorithms to capture and analyze visual features of products in real-time on the production line. Key techniques utilized include feature extraction, edge detection, and texture analysis to identify defects such as scratches, dents, and dimensional inaccuracies. Pattern recognition algorithms, such as support vector machines (SVM) and convolutional neural networks (CNN), are trained on large datasets of product images to classify items as acceptable or defective with high accuracy. The system was tested on a dataset collected from a manufacturing facility producing metal components. Experimental results demonstrate that the automated system achieved an inspection accuracy of 98%, significantly outperforming manual inspection methods in terms of speed and consistency. Furthermore, the integration of this system into the production line reduced inspection time by approximately 70% and minimized production downtime. This research highlights the potential of intelligent informatics, particularly computer vision and pattern recognition, in enhancing the efficiency, reliability, and scalability of industrial quality control processes. The findings suggest that such automated systems can contribute significantly to the advancement of Industry 4.0 by enabling smart manufacturing practices and reducing dependence on manual labor. Future work will focus on extending the system to handle more complex products and dynamic production environments
Evaluation of IoT Regulatory Readiness in Indonesia and Policy Recommendations to Support Safe and Effective Implementation Robie, Rizqon; Munadi, Rendy; Jumhur, Helni Mutiarsih
International Journal of Artificial Intelligence Research Vol 9, No 1.1 (2025)
Publisher : Universitas Dharma Wacana

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29099/ijair.v9i1.1.1639

Abstract

The rapid development of Internet of Things (IoT) technology in Indonesia presents significant opportunities as well as regulatory challenges. Although IoT adoption continues to increase across various sectors, national policies remain fragmented and lack an integrated framework to support safe and effective implementation. This study assesses Indonesia's readiness for IoT regulation and formulates policy recommendations using a mixed-methods approach. The dataset used in this study comprises both secondary and primary data. Secondary data includes Indonesia's Cybersecurity data, Digital Infrastructure Status, IoT Regulations and Laws, Bappenas Studies, Data from Bappenas, and several policies in other countries, such as America, China, Japan, Korea, and Europe. Meanwhile, primary data was collected through questionnaires distributed to several elements, including 61.5% respondents from IoT users, 19.3% respondents from IoT business actors/IoT Startups, 15.8% academics, and 3.7% Government as regulators. The results of this data were then processed to determine government policy readiness by implementing DDPG, where the state space consists of 6 dimensions of leading regulatory readiness indicators (infrastructure, security, data protection, interoperability, institutional maturity, and economy). The action space is a 6-dimensional vector with continuous values in the range of [-1, 1], representing policy interventions in each dimension. The implementation applies reward functions, actor networks, and critic networks. Training data was applied for several episodes at 400 and 1000 episodes. The comparison results show that IoT regulations and policies in Indonesia should be designed with an adaptive approach based on Reinforcement learning, where the balance between data security, technology readiness, and market penetration can be dynamically adjusted to national and global conditions
Deep Reinforcement-Driven Clustering and Routing Protocol for Smart Vehicular Networks Riki, Riki; Widyarto, Setyawan
International Journal of Artificial Intelligence Research Vol 9, No 2 (2025): December
Publisher : Universitas Dharma Wacana

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29099/ijair.v9i2.1576

Abstract

This study proposes a Deep Reinforcement-Driven Clustering and Routing Protocol (DRCRP) to enhance energy efficiency and routing stability in smart vehicular networks. The protocol integrates an Actor–Critic deep reinforcement learning framework with Proximal Policy Optimization (PPO) to enable adaptive decision-making in dynamic Internet of Vehicles (IoV) environments. Through continuous learning, DRCRP adjusts cluster head selection and routing paths according to real-time vehicular mobility, residual energy, and link quality. Simulation experiments conducted using NS-2 and VanetMobiSim show that DRCRP achieves superior performance compared to benchmark algorithms such as AI-EECR, GWO-CH, and DMCNF. Quantitatively, the proposed model improved the Packet Delivery Ratio (PDR) by up to 4.3%, reduced End-to-End Delay by 18–22%, and lowered Energy Consumption by 12–16%. Moreover, DRCRP effectively minimized communication overhead and extended cluster head and member lifetimes, confirming its ability to balance reliability and energy efficiency. These results demonstrate the capability of reinforcement learning-based architectures to support intelligent, sustainable, and scalable vehicular communication systems under complex mobility conditions
Security Mitigation of the Open Journal System (OJS) Against Online Gambling Content Hijacking Using the ISSAF Framework Sarjimin, Sarjimin; Nugraheni, Anggit Gusti
International Journal of Artificial Intelligence Research Vol 9, No 2 (2025): December
Publisher : Universitas Dharma Wacana

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29099/ijair.v9i2.1546

Abstract

The urgency of this research is to identify the causes, develop mitigation methods, and enhance the security of OJS websites, as many are infiltrated or hijacked for online gambling or other harmful content. Securing OJS websites is never easy because attacks are increasingly diverse and innovative every day. OJS system security is essential to protect the information contained therein and protect the services provided by scientific journal publishers. The ISSAF framework, which uses a simulation approach similar to a real server, can serve as a basis for identifying OJS Website vulnerabilities in Webmin for a system administrator. The results of the identification in this study indicate that the leading cause of OJS web server attacks originates from outside the simulation environment, specifically the internet network via ports 80/443. Vulnerability Session Hijacking with Cookies receives a CVSS vulnerability score of 9.1. A vulnerability in the web server configuration folder structure, traceable by crawler tools, receives a CVSS vulnerability score of 5.3. Repeated login attempts to the OJS system are not banned, and blocking the Attacker's IP receives a CVSS vulnerability score of 6.5. A file with the .php extension was successfully uploaded; it may be a backdoor file with a CVSS vulnerability score of 5.3. Although the OJS PKP changed/forced the file to .txt, the malicious file could be exploited in the future by unauthorized users. The novelty of this research lies in a server simulation that mimics a real server and the ISSAF framework for assessing the security of the Webmin web-based system administration tool on OJS websites.

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