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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 59 Documents
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The Role of AI in Enhancing Healthcare Access and Service Quality in Resource-Limited Settings Farhat, Rehman; Abideen Malik, Ahmad Raza; Sheikh, Abdullah Hussain; Noor Fatima, Ayesha
International Journal of Artificial Intelligence Vol 11 No 2: December 2024
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-01102.709

Abstract

The integration of Artificial Intelligence (AI) in healthcare has become a critical factor in improving healthcare delivery, particularly in resource-limited environments. In countries like Pakistan, where healthcare access is a major challenge, AI-powered solutions such as telemedicine, diagnostic tools, and health chatbots have the potential to revolutionize healthcare service delivery. This research aims to explore the effectiveness of AI in improving healthcare access, diagnosis time, and service quality, while identifying the challenges faced in its implementation. A mixed-methods approach was employed, utilizing surveys, interviews, and case studies with healthcare professionals, AI developers, and patients in both rural and urban areas. The findings revealed that AI-driven solutions significantly enhanced healthcare access, particularly in rural areas, by enabling remote consultations and reducing diagnostic time. However, challenges such as infrastructure limitations, low technology literacy, resistance to adoption, and the absence of robust policy frameworks were identified as key barriers to successful AI integration. The study suggests that improvements in technological infrastructure, training, and regulatory frameworks are essential for maximizing the impact of AI in healthcare. Future research should focus on exploring the long-term effects of AI on patient outcomes, investigating the role of policy in AI adoption, and examining how AI can be adapted to different cultural contexts in healthcare systems globally.
Development of an Arabic-Language Virtual Assistant for Public Services to Improve Accessibility of Government Services for Iraqis Alkarkhi, Dabbagh Adawiya; Al-Majedy, Zainab; Abbas, Mohammad Raziq; Aziz, Salim Hama
International Journal of Artificial Intelligence Vol 11 No 2: December 2024
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-01102.738

Abstract

This research explores the creation of an Arabic-language virtual assistant designed to enhance the accessibility of public services in Iraq. The study centers on developing a chatbot driven by Artificial Intelligence (AI) technologies, especially Natural Language Processing (NLP) and Machine Learning (ML), to help citizens in obtaining government services in Arabic, with a focus on the Iraqi dialect. The chatbot was created to address frequent questions regarding government services like passport renewals, birth registrations, and general inquiries. The system development employed platforms such as Google Dialogflow and TensorFlow to build an intuitive interface that can effectively handle and reply to user inquiries. Data collection comprised Arabic conversational data from Iraqi individuals, obtained via surveys and feedback on government services. The system's effectiveness was assessed through language comprehension accuracy, relevance of responses, and satisfaction of users, with findings indicating high satisfaction levels (88%), accuracy (95%), and relevance (92%). The results indicate that the chatbot greatly improves access to government services, minimizing the necessity for physical visits and increasing service efficiency. Nonetheless, issues regarding the complexities of the Iraqi Arabic dialect and voice recognition capabilities persist. The study finds that the chatbot presents a hopeful approach for addressing language and tech obstacles in providing public services. Future studies should aim at perfecting the language model and boosting voice input functionalities to better the chatbot's performance in Iraq’s public sector.
The Impact of AI Technologies on Precision Agriculture Imran Khan Yousafzai; Akram, Mumtaz Nudrat; Zia, Farid Khalil; Adanan, Khalid Haeder
International Journal of Artificial Intelligence Vol 11 No 2: December 2024
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-01102.776

Abstract

The adoption of artificial intelligence (AI) in precision agriculture offers transformative solutions to challenges such as climate change, resource scarcity, and inefficient traditional farming methods. This study evaluates the application of AI in improving crop health monitoring, yield prediction, and optimizing the use of natural resources like water and fertilizers. A quantitative research design was employed, utilizing field experiments and data collected from soil sensors, drones, and AI-based tools across ten diverse agricultural locations in Pakistan. The findings demonstrate that AI enables early detection of crop diseases and stress conditions, reducing response time and improving overall crop health. Predictive models powered by AI provide highly accurate yield estimations, facilitating better planning and resource allocation. Additionally, AI technologies optimize water and fertilizer usage, achieving reductions of up to 15% and 10%, respectively, without compromising crop yields. Despite technical and infrastructural challenges, the results underscore the potential of AI in enhancing sustainability and efficiency in agriculture. To maximize these benefits, collaboration between governments and private sectors is crucial in providing training, infrastructure, and region-specific solutions for farmers. Future research should explore integrating AI with automation technologies to further improve agricultural practices, including harvesting and distribution processes. This study highlights the importance of AI as a key enabler of sustainable food production and agricultural resilience.
A Proposed Multilayer Perceptron Model and Kernel Principal Component Analysis for the Prediction of Chronic Kidney Disease Iliyas, Iliyas Ibrahim; Boukari, Souley; Gital, Abdulsalam Ya’u
International Journal of Artificial Intelligence Vol 11 No 2: December 2024
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-01102.783

Abstract

unfortunately, this stage is mostly detected at a late stage, leading to dialysis or transplantation. Early detection is important for the effective management of CKD. ML has shown success in the early prediction of CKD by using an algorithm that learns and predicts without being programmed. ML requires appropriate datasets for this process, and one of the aspects is dimensionality reduction, which addresses the challenges of unnecessary tests, high-cost tests and the use of redundant tests. Principal Component Analysis (PCA) is a widely used method for dimensionality reduction; however, it relies on linear transformation to identify relationships within features. Medical datasets such as CKD exhibit complex nonlinear features, which is important for exploring alternative dimensionality reduction methods that can rely on nonlinear transformation. This study aims to propose an ML approach that utilises kernel PCA to reduce dimensionality based on nonlinearity structures and enhance the prediction of CKD. We evaluated seven ML models on the different kernel functions of PCA. The ML models included random forest (RF), decision tree (DT), multilayer perceptron (MLP), support vector machine (SVM), extreme gradient boosting (XgBoost), adaptive boosting (AdaBoost), logistic regression (LR), and gradient boosting. The kernel functions used for dimensionality reduction are cosine principal component analysis (CPCA), polynomial principal component analysis (PPCA), radial basis principal component analysis (RPCA), sigmoid principal component analysis (SPCA) and linear principal component analysis (LPCA). The results of the study revealed that the MLP with RPCA, SPCA and CPCA achieved good performance in predicting CKD, with an accuracy score of 99% on DB1, and that the MLP with RPCA and SPCA achieved good performance in predicting CKD, with an accuracy score of 100% on DB2. The study showed how kernel PCA, which effectively reduces high dimensionality-based nonlinearity relationships, can positively affect the performance of predictive models and the power of dimensionality reduction toward disease prediction.
Development of a Student Expense Tracking System Using Optical Character Recognition Shaharudin, Muhammad Hairil; Saad, Ahmad Fadli; Yani, Achmad; Manaf, Abdi
International Journal of Artificial Intelligence Vol 12 No 1: June 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-01201.741

Abstract

Personal financial literacy is a vital skill for university students, yet many struggle to track their daily expenses due to time constraints and low awareness. This study aims to design and develop a web-based Student Expense Tracking System using Optical Character Recognition (OCR) technology to address this issue. The system allows users to automatically extract and record spending information from receipt images, reducing manual input and improving financial awareness. The development followed the Web Development Life Cycle (WDLC) using the Waterfall model, comprising planning, design, development, and testing phases. Visual Studio Code, Python 3, and Tesseract OCR were employed in system implementation. Wireframes and mockups guided the interface design, while backend development focused on data storage and OCR integration. Functionality testing showed a 100% pass rate across ten scenarios, validating the system's performance in image processing, budget management, and spending visualization. Usability testing using the Post-Study System Usability Questionnaire (PSSUQ) with 30 participants yielded a mean score of 4.45 out of 5, indicating a high level of user satisfaction. The system scored highest on ease of use (4.6), visual design (4.7), and recommendation likelihood (4.8), confirming its intuitive interface and appeal. Slightly lower scores in user confidence (4.1) and data organization (4.2) point to opportunities for interface refinement and improved user guidance. This research concludes that OCR can effectively support financial tracking for students. Future enhancements with NLP and machine learning are recommended to automate expense categorization and improve analytical capabilities.
Evaluation of Perplexity and Syntactic Handling Capabilities of ClueAI Models on Japanese Medical Texts Haga, Tatsuhiro; Matsumoto, Keiyo; Asahiko, Ippei; Mizoguchi, Shunzo
International Journal of Artificial Intelligence Vol 12 No 1: June 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-01201.749

Abstract

This study aims to evaluate the effectiveness of a large Japanese language model, ClueAI, tailored to the medical domain, in the task of predicting Japanese medical texts. The background of this study is the limitations of general language models, including multilingual models such as multilingual BERT, in handling linguistic complexity and specific terminology in Japanese medical texts. The research methodology includes fine-tuning the ClueAI model using the MedNLP corpus, with a MeCab-based tokenization approach through the Fugashi library. The evaluation is carried out using the perplexity metric to measure the model's generalization ability in predicting texts probabilistically. The results show that ClueAI that has been tailored to the medical domain produces lower perplexity values than the multilingual BERT baseline, and is better able to understand the context and sentence structure of medical texts. MeCab-based tokenization is proven to contribute significantly to improving prediction accuracy through more precise morphological analysis. However, the model still shows weaknesses in handling complex syntactic structures such as passive sentences and nested clauses. This study concludes that domain adaptation provides improved performance, but limitations in linguistic generalization remain a challenge. Further research is recommended to explore models that are more sensitive to syntactic structures, expand the variety of medical corpora, and apply other Japanese language models in broader medical NLP tasks such as clinical entity extraction and classification.
A Policy Analysis of the Danish National AI Strategy: Ethical and Governance Implications for AI Ecosystems Lauritsen, Henrik; Hestbjerg, David; Pinborg, Lone; Pisinger, Christensen
International Journal of Artificial Intelligence Vol 12 No 1: June 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-01201.802

Abstract

The Danish National AI Strategy presents a structured approach to building an ethical and innovative AI ecosystem. It emphasizes four main pillars: ethical AI development, public data utilization, skills development, and strategic technology investment. The strategy has achieved notable success, especially in the education sector, where ethical principles like fairness, transparency, and accountability are well-integrated. However, issues such as algorithmic bias and fairness remain, indicating the need for ongoing refinement of ethical frameworks. Public data plays a central role in AI innovation, particularly in healthcare and education. Yet, challenges related to data privacy and access continue to pose obstacles, highlighting the importance of robust data governance. Skills development programs have helped prepare the workforce for AI-related roles, though limited employer participation, especially among small businesses, suggests the need for more inclusive outreach. Furthermore, while government and private funding have supported advanced AI research, the transition from innovation to practical application still faces gaps. This study employed a qualitative descriptive approach, utilizing document analysis and thematic analysis based on data from government publications and expert interviews with 20 stakeholders, including policymakers and AI specialists. The findings provide valuable insights into Denmark’s AI journey and serve as a reference for other countries aiming to implement responsible, inclusive, and sustainable AI strategies.
Network Anomaly Detection System using Transformer Neural Networks and Clustering Techniques Isijola, Ayomitope; Asefon, Michael; Ogude, Ufuoma; Sola, Adetoro Mayowa; Adebowale, Temiloluwa; Akunekwu, Isabella
International Journal of Artificial Intelligence Vol 12 No 1: June 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-01201.837

Abstract

This study proposes a hybrid approach for network anomaly detection by integrating a Transformer-based model with clustering techniques. The methodology begins with the application of K-means clustering as a preprocessing step to group similar network traffic data, thereby reducing data complexity and highlighting significant patterns. The clustered data is then fed into a Transformer model, which utilizes multi-head self-attention mechanisms to capture intricate temporal dependencies and contextual relationships within sequential data. This dual-stage approach enhances the model’s ability to differentiate between normal and anomalous behaviors in network traffic. Trained on a network security dataset, the system effectively identifies both common and rare attack types. According to the results, the suggested ensemble classifier outperformed existing deep learning models with an accuracy of over 99.5%, 98.5%, and 99.9% on the UNSW-NB15 dataset. The synergy between the unsupervised pattern recognition of clustering and the deep learning capabilities of Transformers enables a scalable and adaptable solution for real-world network security applications, making it suitable for proactive cyber threat detection and mitigation.
CAD for Robotics: Trends, Opportunities, Considerations, and Constraints Ismail, Andi Almeira Zocha; Ismail, Andi Regina Acacia; Abdulbaqi, Azmi Shawkat; Panessai, Ismail Yusuf
International Journal of Artificial Intelligence Vol 12 No 1: June 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-01201.849

Abstract

In Southeast Asia, the integration of Computer-Aided Design (CAD) into robotics development has become increasingly vital in meeting the growing demand for rapid, task-specific automation. CAD plays a central role in enhancing how robotic systems are designed, simulated, and prototyped—enabling improved design precision, reduced development time, and accelerated innovation. This paper investigates the current trends in CAD applications for robotics and highlights key opportunities, including collaborative design approaches, rapid prototyping capabilities, and the convergence of digital engineering tools. Furthermore, the study discusses critical technical considerations such as software interoperability, real-time simulation integration, and the need for upskilling in CAD-related competencies. Drawing from both academic research and industrial practice across Southeast Asian countries, the findings reveal a pressing need for tighter integration between CAD platforms, robotic simulation environments, and control systems. The analysis identifies several regional challenges, including limited access to advanced CAD tools, inconsistent adoption in educational curricula, and disparities in technical training infrastructure. The paper concludes with strategic recommendations to support the growth of CAD-driven robotics in the region: bridging the digital skills gap, improving access to design technologies, promoting cross-institutional collaboration, and encouraging targeted research to adapt CAD tools to local industrial needs. These efforts are crucial for enabling Southeast Asia to capitalize on CAD’s transformative potential in developing agile, affordable, and application-specific robotic solutions.

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