Implementasi Data Mining Untuk Mengklasifikasi Hasil Belajar Siswa/i Dengan Metode Naïve Bayes - Riyanto, Agus; Elvis Sastra Ompusunggu (Fakultas Teknologi dan Ilmu Komputer Universitas Prima Indonesia, 2024)
This study aims to classify student learning outcomes and determine the accuracy of the research methods. The research was conducted at MAS Amaliyah Sunggal in 2024. This research applies data mining using the Naïve Bayes algorithm method and testing the accuracy of the Naïve Bayes method with RapidMiner. The data used consists of secondary data obtained directly from the school and data from distributing questionnaires. The population is all third-grade high school students T.A. 2023/2024, which amounted to 151 people. The sampling technique used is simple random sampling with a sample size of 60 people, which will be used as a dataset. The attributes used amounted to 10 and 1 class attribute for classification. Data analysis is done with the Bayes theorem equation with 60 training data and 1 testing data. The analysis results show that the highest probability value is in the P (H = Very Good) class of 0.4061516, which can be concluded by the classification of learning outcomes on report cards T.A. 2023/2024, categorized as very good. Based on the results of testing the level of accuracy of the Naïve Bayes method with RapidMiner using the provisions of 70% training data and 30% testing data, it shows an accuracy value of 94.44%, which means that the Naïve Bayes method is good enough to be used to classify student learning outcomes.