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p-Index From 2021 - 2026
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All Journal IAES International Journal of Artificial Intelligence (IJ-AI) Journal of Information Technology and Cyber Security
Hammad, Jehad A. H.
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Computer Science & IT Decision Sciences, Operations Research & Management Electrical & Electronics Engineering Engineering

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Dialect Classification of the Javanese Language Using the K-Nearest Neighbor Filby, Brilliant; Pujianto, Utomo; Hammad, Jehad A. H.; Wibawa, Aji Prasetya
Journal of Information Technology and Cyber Security Vol. 2 No. 2 (2024): July
Publisher : Department of Information Systems and Technology, Faculty of Intelligent Electrical and Informatics Technology, Universitas 17 Agustus 1945 Surabaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30996/jitcs.12213

Abstract

Indonesia is rich in ethnic and cultural diversity, each reflected in its unique linguistic characteristics. One way to preserve the Javanese language is by conducting research on its dialects. This study aims to classify three main dialects in Java Island—East Java, Central Java, and West Java—using text data from online sources. The classification process includes preprocessing (tokenizing, case folding, and word weighting), data balancing with the Synthetic Minority Oversampling Technique (SMOTE), and classification using the K-Nearest Neighbor (K-NN) algorithm. This study highlights the importance of dialect recognition in supporting the preservation of the Javanese language and the development of linguistic technology applications. Testing using 10-fold cross-validation showed the best performance at , with an accuracy of 94.05%, precision of 95.83%, and recall of 94.44%. These findings significantly support computational linguistics research and the preservation of regional languages.
Serious game intelligent transportation system based on internet of things Nugroho, Fresy; Buditjahjanto, I Gusti Putu Asto; Pebrianti, Dwi; Hammad, Jehad A. H.; Fachri, Moch; Lestari, Tri Mukti; Maharani, Dian; Nurrahma’N, Alfina
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 15, No 1: February 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v15.i1.pp177-190

Abstract

This research examines the implementation of the preference ranking organization method for enrichment evaluation (PROMETHEE) approach for multi-criteria decision-making in a character recommendation system for serious games. The method calculates character skill values across multiple criteria and generates rankings of the best characters according to game environment conditions derived from closed-circuit television (CCTV) based traffic detection. Image processing algorithms were applied to classify congestion levels into quiet, moderate, and busy categories, which directly influence gameplay modes. Experimental results show that PROMETHEE rankings vary across maps (e.g., A6 ranked highest in quiet mode, while B2 dominated in busy mode), demonstrating the system’s contextual adaptability. Usability testing with 50 participants yielded an average system usability scale (SUS) score of 78.9, while expert evaluation using game design factor questionnaire (GDFQ) produced a mean of 4.19/5, both indicating high acceptance and positive user experience. These findings confirm that PROMETHEE is effective in generating context-aware recommendations, providing both strategic depth and engagement. The study concludes that integrating traffic data into serious game design can enrich intelligent transportation systems (ITS) education and awareness, with future improvements possible through real-time player feedback adaptation and machine learning–based traffic prediction.
Co-Authors Aji Prasetya Wibawa Dian Maharani, Dian Fachri, Moch Filby, Brilliant Fresy Nugroho I Gusti Putu Asto Buditjahjanto Lestari, Tri Mukti Nurrahma’N, Alfina Pebrianti, Dwi Utomo Pujianto