Komparasi Metode Decision Tree, KNN, dan SVM Untuk Menentukan Jurusan Di SMK

Novendra Adisaputra Sinaga (Politeknik Bisnis Indonesia, Pematangsiantar)
Ramadani Ramadani (Universitas Potensi Utama, Medan)
Khoirunsyah Dalimunthe (Universitas Potensi Utama, Medan)
Muhamad Sayid Amir Ali Lubis (Universitas Potensi Utama, Medan)
Rika Rosnelly (Universitas Potensi Utama, Medan)

Jurnal Sistem Komputer dan Informatika (JSON)
Vol 3, No 2 (2021): Desember 2021

The selection of majors for prospective vocational students is the first step in determining the next career. The determination of the major aims so that students can be directed in receiving lessons based on the ability and talent of students and of course when they have graduated have the skills to get a job if they do not continue their studies. Siti Banun Sigambal Private Vocational School is located in Labuhanbatu Rantau Prapat. In realizing one of the missions of SMK, namely Realizing quality learning in Vocational High School, SMK Siti Banun in determining the Department by conducting tests. In classifying data mining techniques can be used, among others Decision Tree, K-Nearest Neighbors (KNN) and Support Vector Machine (SVM). This research was conducted to compare the performance of Decision Tree, KNN and SVM algorithms in determining majors. Of the 245-test data used obtained SVM has an accuracy value of 89%, precision 87% while KNN has an accuracy value of 84%, precision 81% and Decision Tree has an accuracy value of 78% and precision of 75%.