Application of Data Mining for Predicting the Success of Sambo Athletes in Medan City Using the Naïve Bayes Method - Achmad Zoemirrotin Siregar; Ali Ikhwan (SEAN Institute, 2026)
Sambo is one of the martial arts sports that has been developing in Indonesia, including in the city of Medan. In the process of athlete development, analytical methods are needed to assist coaches and sports organizations in predicting athlete success more objectively and based on data. This study aims to apply data mining techniques to predict the success of Sambo athletes in Medan using the Naïve Bayes method. This method was chosen because it has good classification capabilities and can process data with a relatively simple and efficient computational process. The data used in this study were obtained from training results and athlete performance records, including several variables such as age, training duration, training intensity, physical condition, technical skills, and competition achievement history. The research process begins with data collection, data preprocessing, model development using the Naïve Bayes algorithm, and evaluation of prediction results. The resulting classification model is then used to determine the success category of athletes, such as having high potential for success or lower potential. The results of the study indicate that the application of the Naïve Bayes method is able to provide predictions of Sambo athletes' success with a fairly good level of accuracy. This system can assist coaches and sports administrators in the selection and development process of athletes in a more effective and targeted manner. Therefore, the implementation of data mining using the Naïve Bayes method can be an alternative solution to support decision-making in the development of Sambo athletes in Medan.