Algoritma K-Medoids untuk Mengelompokkan Desa yang Memiliki Fasilitas Sekolah di Indonesia

Abstract

School facilities are learning facilities and infrastructure. Study rooms, study rooms, sports halls, prayer rooms, arts rooms and sports equipment. Means of learning to read textbooks, reading books, school laboratory tools and facilities and various other learning media. This study discusses the application of the K-Medoids method in grouping villages that have school facilities based on the province and education level. Data sources used from the National Statistics Agency (BPS). This study uses data mining techniques in data processing using the k-medoids clustering method. The k-medoid method is part of a fairly efficient grouping of partitions in small datasets and looks for the most representative points. The advantages of this method can overcome the shortcomings of the k-means method that is sensitive to outliers. Another advantage of this method is that the results of the grouping process do not match the entry sequence of the dataset. Grouping k-medoid method can be applied to the percentage of facilities based on the province, so that provincial grouping can be determined based on the data. From the grouping data, 3 clusters were obtained, namely a low cluster of 15 provinces, a moderate cluster of 16 provinces and a high cluster of 3 provinces from the percentage of school facilities in each province. It is hoped that this research can provide information to the government about data collection of school facilities in Indonesia which discusses examiners in the provision of school facilities in Indonesia.