Analisis K-Medoids Dalam Pengelompokkan Rasio Murid dengan Guru, Murid dengan Rombel, dan Rasio Rombel dengan Kelas Jenjang Pendidikan SD dan SMP Menurut Provinsi

Dewinta Marthadinata Sinaga(1*), Agus Perdana Windarto(2), Dedy Hartama(3),

(1) STIKOM Tunas Bangsa, Pematangsiantar
(2) STIKOM Tunas Bangsa, Pematangsiantar
(3) STIKOM Tunas Bangsa, Pematangsiantar
(*) Corresponding Author

Abstract


Study groups are a meeting place between students and teachers in a class in the education unit. So that, a study group can be said to be valid if it has at least 20 students and there are teachers who teach. The number of students and the number of study groups varies in each school that is adjusted to the number of students registered at the school. The various conditions and conditions of schools in Indonesia that allow rules regarding the number of learners and the number of study groups cannot be applied in their entirety. This can cause the low quality of education in Indonesia. Sources of data used from the Indonesian Central Statistics Agency website include data on student to teacher ratio, student with class, and class to class ratio for elementary and junior high school levels in 2016-2017. The purpose of this study is to create a grouping model using the k-medoids algorithm. The k-medoid method has similarities with the k-means method, which includes the partitioning method. Partitioning method is a method of grouping data into a number of clusters without a hierarchical structure between one another. From the results of the study above it can be concluded that the grouping of student to teacher ratios, students with rombel, and rombel ratios with elementary and junior high school education classes by province can apply the k-medoidkmeans method. The data is processed into 2 clusters, namely high and low clusters. From the results of calculations it can be concluded from 34 provinces in Indonesia that a high cluster (C1) obtained 5 provinces namely Kep. Bangka Belitung, DKI Jakarta, West Java, Banten, West Papua and 29 provinces as low clusters (C2) namely Aceh, North Sumatra, Riau, Jambi, South Indonesia, Bengkulu, Lampung, Kep. Riau, Central Java, DI Yogyakarta, East Java, Bali, West Nusa Tenggara, East Nusa Tenggara, West Kalimantan, Central Kalimantan, South Kalimantan, East Kalimantan, North Kalimantan, North Sulawesi, Central Sulawasi, South Sulawesi, Southeast Sulawesi, Gorontalo, West Sulawesi, Maluku, North Maluku, and Papua. The implementation process using the RapidMiner 5.3 application is used to help find accurate values. It is hoped that this research will provide input to the government to take policies in improving the quality of education in Indonesia in the future

Keywords


Datamining; Clustering; K-Medoid; Study Group; RapidMiner 5.3

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