Prediksi Minat Belajar Siswa Berdasarkan Nilai Dan Intensitas Bermain Game Menggunakan Algoritma K-Nearest Neighbor

Abstract

This study aims to predict students' learning interest based on Islamic creed scores and their frequency of playing the Free Fire game using the KNN algorithm. The dataset used includes Islamic creed assignment scores, final exam (UAS) scores, game play time, frequency of play, and learning interest categories. The analysis process was carried out through several stages: data exploration, data cleaning and processing, selecting the best values, modeling, and disseminating the model results. The model was tested using the 5-fold cross validation method and confusion matrix to ensure that the prediction performance was adequate. The results showed that academic scores, especially Islamic creed final exam scores, had a greater influence on learning interest than the intensity of playing the Free Fire game. The KNN model with a k value of 6 produced an accuracy of 94.73% and a very small prediction error, as seen in the Confusion Matrix and Classification Report. These results indicate that the model is capable of working well in classifying students' learning interest into medium or high categories. This prediction model is expected to be used as a tool to understand student learning interest patterns in the school environment. The contribution of this research is the application of KNN-based machine learning methods in analyzing student learning interests, which were previously dominated by conventional approaches such as regression and questionnaires.