Analisis Kontribusi Pemain Sepak Bola Eropa Berbasis C4.5 - Mochammad Eric Adyatma; Rakha Adhiyasya; Rifaldi Ryandhika; Muhammad Farhan Fadholi; Muhammad Rafly Ajiansyah; Yorrel Jensek Isak; Andrianus Zalogo; Lisbet Dakhi (Jurnal Ilmu Komputer dan Informatika | E-ISSN : 3063-9026, 2026)
Modern football has evolved into a data-driven industry where statistical analysis is widely used to objectively evaluate player performance. This study aims to classify the level of goal contribution of top European football players using the C4.5 algorithm implemented through RapidMiner. The dataset is derived from player statistics of top European leagues in the 2022–2023 season, with key attributes including Shot on Target Percentage (SoT%), Shot-Creating Actions (SCA), standardized playing time (90s), and short and total passing accuracy. The research methodology consists of data selection, preprocessing, data transformation, data mining, and model evaluation. The C4.5 algorithm is applied using the Gini Index criterion with pruning techniques to prevent overfitting. Model validation is conducted using 10-Fold Cross Validation. The results show that the classification model achieves an accuracy of 90.67%, with SoT% identified as the most influential variable, followed by SCA and playing time. The generated decision tree provides clear and interpretable rules, making it useful as a decision-support tool for evaluating player contributions based on data-driven analysis.