Penerapan Logistic Regression untuk Prediksi Kelas Karier Pembalap MotoGP Berdasarkan Data Historis - Wildan, Alfan Khoirul; Hasbi Firmansyah; Wahyu Asriyani (Journal of Multidisciplinary Inquiry in Science, Technology and Educational Research, 2025)
The career development of MotoGP riders is influenced by various factors from the early stages of their racing journey, including racing experience, technical performance, and team support. The complexity of these factors makes historical data analysis essential for understanding patterns in riders’ career progression. The use of Logistic Regression is relevant because it can model the probability of career classes based on measurable performance variables, allowing the classification process to be more objective and structured compared to conventional descriptive assessments. The dataset used in this study contains rider data from the beginning of their careers through their advancement into specific classes such as Moto3, Moto2, MotoGP, 125cc, and MotoE. Data processing was conducted using RapidMiner, involving data loading, model training using the Polynomial by Binomial Classification scheme, model application, and performance evaluation through a confusion matrix and accuracy measurement. The results show that Logistic Regression achieved a classification accuracy of 82.48%. Therefore, Logistic Regression can be utilized as an initial model for classifying MotoGP riders’ career stages, although further development is needed to improve performance for minority classes.