ANALISIS PREDIKSI SISWA PAKET C PKBM SILIWANGI PAMULANG DITERIMA MASUK PERGURUAN TINGGI NEGERI MENGGUNAKAN METODE ALGORITMA SUPPORT VECTOR MACHINE DAN K-NEAREST NEIGHBOUR (Studi Kasus : Sekolah PKBM Siliwangi): ANALISIS PREDIKSI SISWA PAKET C PKBM SILIWANGI PAMULANG DITERIMA MASUK PERGURUAN TINGGI NEGERI MENGGUNAKAN METODE ALGORITMA SUPPORT VECTOR MACHINE DAN K-NEAREST NEIGHBOUR (Studi Kasus : Sekolah PKBM Siliwangi)

Nazura, Is Anin Nazura

Jurnal Penelitian Ilmu komputer
Vol. 1 No. 4 (2023): Desember

Entering State Universities (PTN) for package C graduates is relatively difficult. Based on data from package C graduates at PKBM who were accepted into State Universities from 2020 to 2022 there were 42 students out of a total of 210 students who registered and out of a total of 1,132 students. In this regard, it is necessary to have a system or model that is used to predict Siliwangi PKBM PKBM Package C graduates who are thought to be accepted into State Universities (PTN) using the Support Vector Machine and K-Nearest Neighbor Algorithm methods. The results of the research are that the support vector machine method has a higher level of accuracy than the k-nearest neighbor algorithm with an accuracy value of 98.8%. The best precision value is the support vector machine algorithm with a value of 97.6% compared to the k-nearest neighbor algorithm. And the best recall value is the support vector machine algorithm with a value of 97.8%. Keywords: Education, Online Learning, Google Classroom, Research and Development, Student Competence, Score Results, K-Nearest Neighbor, Orange Data Mining.