PENERAPAN DATA MINING UNTUK REKOMENDASI PEMILIHAN PROGRAM STUDI PERGURUAN TINGGI PADA SISWA-SISWI SMA NEGERI 1 LAKBOK DENGAN METODE K-MEANS CLUSTERING
DOI:
https://doi.org/10.25157/jmsig.v2i1.5408Keywords:
Data Mining, K-Means Clustering, Recommendations, Study Programs, Universities, Micosoft Excel, SPSSAbstract
Many students at SMA Negeri 1 Lakbok often choose study programs in college because of the influence of friends or others, without considering in depth whether the study program is in accordance with their interests and talents. As a result, many students drop out of school midway because they feel they are taking an unsuitable study program. Therefore, the author conducted a data mining analysis using class data of class XII MIPA and IPS students. In the analysis, the author used Microsoft Excel and SPSS tools. The method used is the K-Means Clustering method with a total of 242 MIPA student data and 92 IPS student data, there are 19 attributes and 10 MIPA clusters and 10 IPS clusters. The number of MIPA Microsoft Excel calculations is 2 large, namely cluster 3 there are 43 data recommended to enter mathematics education, cluster 7 there are 53 data recommended to enter chemical engineering. The number of IPS Microsoft Excel calculations is 2 large, namely cluster 3 there are 16 data recommended to enter law science, cluster 5 there are 12 data recommended to enter geography education. And the number of SPSS MIPA calculations is 2 large, namely cluster 1 contains 39 data recommended for informatics engineering, cluster 5 contains 40 data recommended for Indonesian language education. The number of SPSS IPS calculations is 2 large, namely cluster 2 contains 14 data recommended for accounting, and cluster 7 contains 17 data recommended for Indonesian language education.
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