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All Journal ILKOMNIKA: Journal of Computer Science and Applied Informatics JGGAG (Journal of Games, Game Art, and Gamification)
Nur, Muhammad Rizqi
Unknown Affiliation
Arts Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Other

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Integrating Machine Learning Utility in Tabular Data Synthesizer Training using Loss Function Learning Nur, Muhammad Rizqi; Indraswari, Rarasmaya
ILKOMNIKA Vol 6 No 2 (2024): Volume 6, Nomor 2, Agustus 2024
Publisher : Lembaga Penelitian dan Pengabdian Masyarakat

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28926/ilkomnika.v6i2.646

Abstract

Machine learning (ML) utility has been the main evaluation metrics for data synthesizers. However, because ML utility cannot be simply calculated, none of the previous synthesizers were trained to reach the same level of ML utility as a training objective. This study aims to integrate ML utility into data synthesizer training using a transformer-based model as a learned loss function. The transformer was trained to estimate ML utility of synthetic datasets, then it’s integrated by backpropagating the difference between estimated and expected value. The integration has significantly improved the average ML utility of LCT-GAN and Realtabformer. The ML utility of LCT-GAN improved by 0.0158 for Contraceptive dataset, 0.031 for Insurance dataset, and 0.0561 for Treatment dataset. The ML utility of Realtabformer improved by 0.02 for Contraceptive dataset and 0.0024 for Insurance dataset. The increase affects the dataset distribution, correlation between features, and privacy, but the direction varies. Correlation coefficients indicate that synthetic data distribution gets closer to real data as ML utility improves. In addition to ML utility integration, this study has also shown that patterns between rows in a dataset can be learned, so better synthesizers can be developed based on them.
YOLO-based Mobile Legends Match Result Parsing Nur, Muhammad Rizqi; Indraswari, Rarasmaya
Journal of Games, Game Art, and Gamification Vol. 9 No. 1 (2024)
Publisher : Bina Nusantara University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21512/jggag.v9i1.11070

Abstract

MOBA competitive gaming can benefit from AI advancement. However, data availability is a major issue for Mobile Legends, as opposed to more mature MOBA. In order to obtain high amount of data, it has to be crowdsourced, but it is only viable to collect screenshots. In this paper we propose a framework to automatically parse mobile legends match result screenshots based on YOLO. YOLO is used to locate and classify objects. Text objects are then parsed with OCR. The results are evaluated and compared with older approach using CNN classifiers. The new approach is 25 times faster while achieving the same perfect performance as the old CNN classifier approach.
Co-Authors Ahmad Yusuf Amin, Faris Mushlihul Indraswari, Rarasmaya