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About the Journal
SAKA Journal of Applied Artificial Intelligence is a peer-reviewed scientific journal that publishes high-quality research focusing on the application of artificial intelligence across various disciplines and real-world contexts. The journal aims to provide an international academic platform for researchers, practitioners, and professionals to share innovative findings, methodologies, and practical applications of artificial intelligence. The journal is committed to promoting interdisciplinary collaboration and advancing knowledge in applied artificial intelligence to address contemporary challenges in society, industry, and academia. It welcomes original research articles, review papers, and case studies from scholars worldwide.
Current Issue
SAKA Journal of Applied Artificial Intelligence is proud to publish its inaugural issue, Volume 1, Number 1, July 2026, marking the beginning of its commitment to disseminating high-quality scientific research in the field of applied artificial intelligence. This issue features five original research articles exploring various applications of machine learning in the healthcare domain, with particular emphasis on disease prediction, medical data classification, and patient clustering using state-of-the-art artificial intelligence techniques. The published articles reflect multidisciplinary collaboration among researchers and practitioners from Universitas Sahid Surakarta, the Department of Communication and Informatics of Klaten Regency, Universitas Muhammadiyah Surakarta, and the industrial sector represented by CV. Mitra Jaya Sejahtera, Madiun. Through this inaugural publication, SAKA JAAI aims to serve as a platform for advancing innovative artificial intelligence research and fostering collaboration among academia, government institutions, and industry in addressing real-world challenges through intelligent technologies.
Articles
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Heart Disease Prediction Using Machine Learning Approach with Data Preprocessing Optimization
Abstract Views: 24 PDF Downloads: 12 -
K-Means Clustering for Liver Patient Stratification Using Elbow Method and Silhouette Score Validation
Abstract Views: 21 PDF Downloads: 9 -
Performance Comparison of Naive Bayes, Decision Tree, and Random Forest for Heart Disease Prediction
Abstract Views: 27 PDF Downloads: 12 -
Classification Of Stroke Risk Based On Machine Learning: A Comparative Study Of Naive Bayes And Decision Tree
Abstract Views: 19 PDF Downloads: 11 -
Blood Cell Classification Based on Feature Importance with the Naïve Bayes Algorithm on the BCCD Dataset
Abstract Views: 23 PDF Downloads: 6

