Klasifikasi Siswa Slow Learner dan Non Slow Learner Menggunakan Algoritma Naive Bayes (Studi Kasus: Sekolah Menengah Atas Tunas Luhur) - Abdul Harris Wicaksono; Ahmad Afif Supianto; Satrio Hadi Wijoyo (Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer, 2021)
Education is one of the important aspects for everyone who can be a provision in changing the attitudes and behaviors of individuals and groups in an effort to be aware through teaching and training. Children get intellectual education in school and in tutoring institutions. Children are required to understand the material about natural sciences or social sciences. but not all can capture material with the same ability because the level of intelligence and learning ability of each child is different. there are students whose learning ability is low so slow in understanding the material. These slow learners need special treatment in order to understand the material like other students. To predict these students, it is done by classifying slow learner students and non slow learners by using naive bayes algorithm using cross validation test method of 10 folds. The data used in this study amounted to 89 data obtained from questionnaires filled out by students in grades XI and XII of SMA Tunas Luhur. The test results using cross validation with 10 folds obtained an accuracy value of 0.92857, precision of 0.94736, recall of 0.97297, and F-measure of 0.96. After that, a dashboard was created for visualization, which resulted in a 7-page user interface with test results using a System Usability Scale questionnaire that resulted in a score of 71.75 or acceptable.