IMPLEMENTATION OF WEB-BASED NAIVE BAYES ALGORITHM FOR DETERMINING DEPARTMENTS AT SMK 10 MUHAMMADIYAH KISARAN - Nurlaili Sabila; Herman Saputra; Muthia Dewi (Informatika, Universitas Jenderal Soedirman, 2022)
Determination of majors is very important for the convenience of prospective students in the process and continuation of education so that they do not experience difficulties in the teaching and learning process in the future. SMK 10 Muhammadiyah Kisaran is one of the private vocational schools in Asahan that provides 3 majors including Audio Video Engineering (TAV), Computer and Network Engineering (TKJ), and Motorcycle Engineering and Business (TBSM). SMK 10 Muhammadiyah Kisaran does not yet have a special system for selecting majors so that prospective students are welcome to choose majors according to their own wishes, not a few students find it difficult because the students themselves do not understand their abilities.so that it’s not uncommon for students to choose majors in a random way or follow their friends' choices. Therefore we need a system that can help prospective students in selecting majors that match their interests and talents and reduce mistakes in choosing majors. The technique used for the classification data mining model in this study is the Naïve Bayes Algorithm. The dataset that will be used as training data and test data is data for new students for the 2021/2022 school year, to be precise, for class X SMK 10 Muhammadiyah Kisaran obtained from the results of documentation and questionnaires. The criteria used were school origin, gender, interests, major, influence of friends, parental suggestions, math scores, English grades, and science grades. The results of the classification modeling with the Naïve Bayes Algorithm produce an accuracy value of 89%.