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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.
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Articles 9 Documents
 1 
Search results for , issue "Vol 9, No 2 (2025): December" : 9 Documents clear
Modeling the Driving Factors of Educational Technology Innovation in Indonesian Universities: A Hybrid ISM–ANP Approach Abadi, Satria; Majid, Mad Helmi ab; Marwanta, Y. Yohakim; Susianto, Didi
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

Abstract

This study aims to model the critical enablers driving technological innovation in higher education institutions in Indonesia by integrating Interpretive Structural Modeling (ISM) and Analytic Network Process (ANP). The hybrid approach provides both structural and quantitative insights into the interrelationships among eight identified enablers: policies and regulations, digital infrastructure, faculty competence, technology incentives, industry collaboration, student literacy, innovation culture, and data security. The ISM results classify policies and regulations and digital infrastructure as driving factors that form the foundational layer of innovation ecosystems. Meanwhile, faculty competence, technology incentives, and industry collaboration serve as linkage factors that bridge strategic policies and operational implementation, whereas student literacy, innovation culture, and data security emerge as dependent factors representing the system’s outcomes. The ANP results reinforce the ISM structure, revealing that policies and regulations (0.215) and digital infrastructure (0.187) have the highest influence, followed by faculty competence (0.142) and industry collaboration (0.130). The combined ISM–ANP framework demonstrates that sustainable educational technology innovation requires a synergistic interaction between governance, human resources, and digital culture. The findings provide a comprehensive model that can guide universities and policymakers in formulating evidence-based digital transformation strategies within the Indonesian higher education context
Salt Quality Classification Using Backpropagation Neural Network and K-Nearest Mahmudi, Anas; Abidin, Zainul; Razak, Angger Abdul Razak Abdul
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.v9i2.1505

Abstract

Salt quality plays a vital role in determining its usability across various sectors, including food, pharmaceuticals, and industrial applications. Traditional methods of classifying salt quality, which rely heavily on manual inspection and laboratory testing, are often time-consuming, costly, and prone to human error. In response to these limitations, this study explores the implementation of machine learning techniques—specifically, Backpropagation Neural Network (BPNN) and K-Nearest Neighbor (K-NN)—to classify salt quality based on its physical and chemical properties. The features used in this research include NaCl concentration, moisture content, magnesium levels, sulfat, insoluble, calcium, NaCL(wb) and NaCL(db) which are commonly used indicators of salt purity and grade. The BPNN model is designed to handle complex and non-linear relationships within the dataset by adjusting weights through iterative backpropagation during training. Meanwhile, the K-NN algorithm serves as a simpler, instance-based learning method that classifies samples based on the majority class of their nearest neighbors in the feature space. Comparative experiments were conducted to evaluate the classification and computational efficiency of both models. Results indicate that both methods are effective in classifying salt into predefined quality categories. However, BPNN consistently outperforms K-NN in terms of time efficiency and generalization, particularly when handling noisy or overlapping data. The findings underscore the potential of integrating artificial intelligence into quality control systems in the salt industry, offering a faster, more objective, and scalable solution for ensuring product standards.
Evaluation Of A Feature-Concatenated Model For Multiclass Diagnosis Of Pulmonary Diseases on An Imbalanced Dataset Ajitomo, Wahyu; Tyas, Dyah Aruming; Harjoko, Agus
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.v9i2.1519

Abstract

Lung diseases such as pneumonia, tuberculosis, and COVID-19 pose serious global health challenges, particularly in X-ray image classification where class distribution is often imbalanced. To address this issue, this study proposes a hybrid model based on concatenated CNN architectures and applies class weighting using focal loss multiclass. The dataset consists of 7,135 X-ray images divided into four main classes: pneumonia, tuberculosis, COVID-19, and normal. Focal loss with a gamma parameter of 2.0 is employed to enhance the model’s focus on minority classes. Evaluation results show that combined models such as DenseNet121 + VGG16 and VGG16 + ResNet50 achieve F1-scores of up to 0.87, outperforming single models. Grad-CAM visualizations also indicate that the combined models can recognize pathological areas more comprehensively and accurately. This approach proves effective in improving the accuracy and sensitivity of AI-based diagnostic systems.
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.
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.
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
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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