Pengelompokan Mahasiswa Berdasarkan Data Akademik Sebelum Kuliah dan Masa Studi Menggunakan K-Medoids - Herri Kurnia; Lisna Zahrotun; Utaminingsih Linarti (InfoTekJar : Jurnal Nasional Informatika dan Teknologi Jaringan, 2021)
The research objective is to obtain information about the results of clustering that are useful for the campus, especially study programs, to be used as consideration for future admissions by grouping, causing a mismatch between the number of students and the existing campus facilities. This research was conducted at university X which has several faculties, one of which is faculty Y which consists of the R study program, S study program, T study program and U study program. This research uses the K-Medoids method. The stages of this research started with load dataset, data cleaning, data selection, data transformation with one hot encoding, euclidean distance, and k-medoids to produce clusters. Testing the quality of the clusters in this study using the silhouette coefficient. The research resulted in recommended student data and all of them came from Java Island. In the dataset of study programs R, S, and U, the recommended data are obtained with a total number of 9, 57, and 64, respectively, which have an average math score of at least 82. Meanwhile, for the T study program dataset, there are 35 data with an average mathematical value. amounted to 73.89. The test results for the dataset of study programs R, S, T and U are 0.52, 0.67, 0.35, and 0.65 respectively, so the results are quite good.