PENERAPAN K-MEANS CLUSTERING UNTUK REKOMENDASI PENENTUAN JURUSAN PERGURUAN TINGGI PADA SISWA SMA N 2 JAMBI
Published on Dec 21, 2019
The students of SMA Negeri 2 city of Jambi tend to choose majors based on interest, and desire because of parents. Some of them already take into account the existing potential in them, then commitment to learning in the field of it won't go smoothly, even though the Department he chooses it doesn't match his ability. Therefore, the author does analysis of data mining using value data class XII students from one to four semesters and kuisoner the authors share. In doing the analysis the author using tools tools WEKA and RapidMiner. The method used is the method of k-means clustering with 24 attributes and 5 clusters. The number of clusters on a manual calculation is, there are 62 C1, C2 data there are 28 data, data, there are 30 C3 C4 C5 there are 30 data, there are 60 data. The number of clusters in the calculation of RapidMiner is there are 35, C1, C2 data there are 55 data, there are 58 C3 data, there are 35 C4 C5, there are data 27 data. The number of clusters on a calculation of the WEKA is a, C1, C2 data there are 30 there are 49 data, there are 41 data, the C3 C4 C5 32 there are data, there are 58 data.