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All Journal JADECS (Journal of Art, Design, Art Education and Culture Studies) Jurnal Penelitian Pendidikan IPA (JPPIPA) JOURNAL OF APPLIED INFORMATICS AND COMPUTING Building of Informatics, Technology and Science Abdimasku : Jurnal Pengabdian Masyarakat Jurnal Teknik Informatika (JUTIF)
Al zami, Farrikh
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Agriculture, Biological Sciences & Forestry Arts Humanities Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Chemistry Computer Science & IT Economics, Econometrics & Finance Education Health Professions Industrial & Manufacturing Engineering Materials Science & Nanotechnology Physics Social Sciences

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Journal : JOURNAL OF APPLIED INFORMATICS AND COMPUTING

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Improving YOLO Performance with Advanced Data Augmentation for Soccer Object Detection Puspita, Rahayuning Febriyanti; Naufal, Muhammad; Al Zami, Farrikh
Journal of Applied Informatics and Computing Vol. 9 No. 6 (2025): December 2025
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v9i6.11256

Abstract

This study developed an object detection system for soccer games using the YOLOv8m algorithm with four main classes: player, goalkeeper, referee, and ball. The dataset, consisting of 372 annotated images, exhibited class imbalance, with significantly fewer ball instances compared to players. The basic YOLOv8m architecture was used without internal modifications, but adjustments were made to the output layer and fine-tuning of the pre-trained weights to adapt to the new dataset. Two models were compared: one without and one with advanced augmentation techniques (mosaic, mixup, cutmix). The experimental results showed an increase in mAP@50 from 74.9% to 81.4% in the augmented model, with a statistically significant difference (p < 0.01). However, model performance still decreased under extreme conditions such as high occlusion, rapid movement, and uneven lighting. The combination of data augmentation, output layer adaptation, and fine-tuning proved effective in improving object detection accuracy and provided the basis for the development of a real-time artificial intelligence-based soccer match analysis system.
Optimized LSTM with TSCV for Forecasting Indonesian Bank Stocks Salsabila, Rizka Mars; Fahmi, Amiq; Al Zami, Farrikh
Journal of Applied Informatics and Computing Vol. 9 No. 6 (2025): December 2025
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v9i6.11314

Abstract

Volatility in financial markets presents complex forecasting challenges for investors, particularly within emerging economies such as Indonesia. This study proposes an optimized Long Short-Term Memory (LSTM) model for forecasting the stock prices of five significant Indonesian banks: BBCA, BBRI, BMRI, BBNI, and BBTN, utilizing daily OHLCV data (Open, High, Low, Close, Volume) and technical indicators from 2020 to 2025. The dataset comprises over 6,000 daily records, segmented using a sliding window approach to preserve temporal structure and enhance learning efficiency. Concurrently, the model architecture comprising dual LSTM layers with dropout regularization was refined through systematic hyperparameter tuning to enhance predictive performance. Model evaluation employed 5-fold Time Series Cross-Validation (TSCV), a sequential validation technique that mitigates data leakage and explicitly overcomes the limitations of conventional k-fold methods by preserving chronological integrity. Performance metrics included MSE, RMSE, MAE, R², and MAPE. The experiment results demonstrate the model’s robustness in capturing long-term dependencies within financial time series. BBCA and BMRI achieved superior accuracy (R² > 0.95), with BBCA recording the lowest MAPE of 2.34%. Despite market fluctuations, the model maintained consistent reliability across all test folds. This study overcomes a methodological limitation by integrating LSTM with TSCV in expanding markets, offering actionable insights for investors, analysts, and policymakers, and serving as a reference for adaptive AI-based, more informed forecasting tools. Moreover, the proposed framework holds promise for broader application across other financial sectors and regional markets with similar volatility characteristics.
Co-Authors Affandy Affandy Andreuw Meda, Ridodio Arta Moro Sundjaja, Arta Moro Ashari, Ayu Assaqty, Mohammad Iqbal Saryuddin Chaerul Umam Fahmi Amiq Hadi, Heru Pramono Indra Gamayanto Irwan, Rhedy Kusumawati, Yupie L. Budi Handoko Marjuni, Aris Megantara, Rama Aria Muhammad Naufal, Muhammad Nazella, Desvita Dian Ocky Saputra, Filmada Prasetya, Rudy Eko Pulung Nurtantio Andono Purwanto Purwanto Puspita, Rahayuning Febriyanti Ramadhan Rakhmat Sani Riyanto, Ahmad Salsabila, Rizka Mars Sasono Wibowo Sendi Novianto Sirait, Tamsir Hasudungan Soeleman, M. Arief Suharnawi Suharnawi