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All Journal Jurnal Teknologi dan Manajemen Informatika Jurnal Nasional Pendidikan Teknik Informatika (JANAPATI)
Pulung Nurtantio Andono
Faculty of Computer Science, Universitas Dian Nuswantoro, Semarang, Indonesia
Computer Science & IT Decision Sciences, Operations Research & Management Education Engineering

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Market Value Tier Classification of Indonesian Football Players using Ensemble Machine Learning and SHAP Analysis Cinantya Paramita; Malfino Wildan Akhya; Pulung Nurtantio Andono
Jurnal Teknologi dan Manajemen Informatika Vol. 11 No. 2 (2025): Desember 2025
Publisher : Universitas Merdeka Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26905/jtmi.v11i2.16399

Abstract

The persistent discrepancy between actual transfer fees and the theoretical market values of football players highlights the need for a more objective and data-driven framework for player valuation. This study aims to classify the market value tiers of Indonesian Liga 1 players in the 2024/2025 season using an ensemble-based machine learning approach integrated with SHAP interpretability analysis. The dataset comprises 226 players with 27 attributes encompassing demographic, career, performance, physiological, and socio-economic dimensions. The research process involved secondary data collection, preprocessing, feature engineering, and percentile-based label construction, followed by model training using Random Forest, XGBoost, CatBoost, and a Stacking Ensemble. Experimental results show that the CatBoost model achieved the best performance, attaining an accuracy of 89%, a Macro-F1 score of 0.85, and an F1(High-Tier) of 0.78, demonstrating its robustness in handling heterogeneous and imbalanced data. SHAP analysis identified minutes played, age, and social media exposure as the most influential variables determining market value tiers. These findings demonstrate that combining ensemble learning with model interpretability can yield a transparent, adaptive, and practical framework for data-driven player valuation. The proposed approach provides actionable insights for football clubs and analysts in optimising player recruitment and developing fairer, evidence-based transfer strategies.
Basic Pose Classification of Pendet Bali Dance Images Using Multilayer Perceptron (MLP) Based on GLCM Feature Extraction and Otsu Thresholding M Hendriawan Hadi; Pulung Nurtantio Andono; Arief Soeleman
Jurnal Nasional Pendidikan Teknik Informatika: JANAPATI Vol. 14 No. 3 (2025)
Publisher : Prodi Pendidikan Teknik Informatika Universitas Pendidikan Ganesha

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23887/janapati.v14i3.103051

Abstract

Traditional dance classification presents a significant challenge due to the complexity of body basic poses and visual similarities across gestures. This study aims to develop an intelligent system for classifying Pendet Balinese dance basic poses using artificial intelligence and image processing techniques. The research applies a quantitative experimental approach combining Otsu Thresholding for segmentation, Gray Level Co-occurrence Matrix (GLCM) for feature extraction, and Multilayer Perceptron (MLP) for classification. A total of 690 labeled images from 15 distinct Pendet basic poses were collected from professional dancers and preprocessed into binary form using Otsu’s method to isolate the dancer from the background. Subsequently, GLCM features, energy, contrast, correlation, and homogeneity, were extracted across four directions. These features served as input for the MLP classifier, trained using a 10-fold cross-validation technique. The model achieved an overall classification accuracy of 82.75%, with high precision and recall for several basic pose types such as Ngelog, Ngeseh, and Nyeregseg. However, some basic poses with overlapping poses presented classification difficulties. The confusion matrix analysis indicated the model's capacity to differentiate most basic pose types effectively. This research demonstrates the feasibility of using MLP combined with texture-based feature extraction for cultural motion classification. The findings contribute to the digital preservation of traditional Indonesian dance and offer a foundation for developing educational and archival tools. Future improvements may involve the use of deep learning techniques and temporal video data for enhanced performance.
Co-Authors Arief Soeleman Cinantya Paramita M Hendriawan Hadi Malfino Wildan Akhya