THE IMPLEMENTATION OF NAÏVE BAYES AND SUPPORT VECTOR MACHINE (SVM) ALGORITHM , IN DETERMINING ACHIEVING STUDENTS IN SMP NEGERI 8 CIMAHI

Abstract

Classification is a technique in data mining to classify data based on data attachment to sample data. In this study, we conducted a comparison of classification techniques to classify students who excel in the dataset of SMP Negeri 8 CIMAHI. Comparison of classification techniques is carried out to see which models in the classification technique are most effective for classifying targets in the dataset of SMP Negeri 8 CIMAHI. The classification technique used is the Support Vector Machine and Naïve Bayes. The classification process begins with preprocessing data to remove missing values ​​and select features in the dataset. After testing, it was found that the accurate classification results were obtained by the Support Vector Machine model with an accuracy value of 93%. Whereas for the Naïve Bayes model the accuracy results are 88%. for this case the Support Vector Machine was chosen as the model that has the best accuracy and the resulting visualization results are clearer to classify outstanding students in the dataset of SMP Negeri 8 CIMAHI.