Komparasi Algoritma Data Mining Dalam Ketuntasan Belajar Daring Siswa Pada Masa Pandemi Covid 19 - Muhammad Saiful; hariman bahtiar; Amri Muliawan Nur; Yupi Kuspandi Putra (Fakultas Teknik Universitas Hamzanwadi, 2023)
This research was conducted at SMA Negri 3 Selong and became the focus of students in class XI IPA and Social Studies. The sampling technique used purposive sampling method. This study aims to describe the extent to which the level of completeness of students during post-covid-19 pandemic learning with online media. This study uses a classification algorithm that functions to find a model that distinguishes data classes or data concepts, with the specific objective of determining the class of unknown object labels. The method used is the PSO-based Naïve Bayes and Naïve Bayes Comparison Algorithms. The results of this study indicate that the use of online media during online learning using the naïve Bayes algorithm is 83.91%, and the PSO-based naïve Bayes algorithm is 91.98%, from the experimental results and testing of the two algorithms, the results of the confusion matrix and AUC testing can be obtained which can be determined the best accuracy value is the PSO-based Naïve Bayes algorithm. As for the comparison of the results in the form of an accuracy value obtained by the Naïve Bayes Algorithm of 83.91% and the PSO-Based Naïve Bayes Algorithm of 91.98% and the difference in the level of accuracy of 8.07%, so it can be concluded that the algorithm that is suitable for classifying student learning completeness during the covid 19 pandemic is Naive Bayes based on particle swarm optimization.