Perbandingan K-Nearest Neighbor dan Random Forest dengan Seleksi Fitur Information Gain untuk Klasifikasi Lama Studi Mahasiswa

Isran K Hasan (Universitas Negeri Gorontalo)
Resmawan Resmawan (Universitas Negeri Gorontalo)
Jefriyanto Ibrahim (Universitas Negeri Gorontalo)

Indonesian Journal of Applied Statistics
Vol 5, No 1 (2022)

Accreditation is a quality and feasibility assessment form in carrying out higher education. One of the factors that affect accreditation is the length of student study. In this study, the length of student study is classified by using the best attributes resulting from selecting information gain features. In optimizing the classification algorithm, we process the data by converting the original data into data that is ready to be mined. The next step is dividing the data into training and testing data so that the classification algorithm can be applied. This study gives the best four attributes, with K-nearest neighbor (K-NN) classification of 86.67% and random forest classification of 100%.Keywords: length of study; information gain; K-nearest neighbor; random forest