Unsupervised Data Mining Technique for Clustering Library in Indonesia

Authors

Robbi Rahim, Universiti Malaysia PerlisFollow
Joseph Teguh Santoso, Universitas STEKOMFollow
Sri Jumini, Universitas Sains Al-Qur'anFollow
Gita Widi Bhawika, Institut Teknologi Sepuluh NopemberFollow
Daniel Susilo, Universitas Multimedia NusantaraFollow
Danny Wibowo, Sekolah Tinggi Ilmu Ekonomi Indonesia SurabayaFollow

Abstract

Organizing school libraries not only keeps library materials, but helps students and teachers in completing tasks in the teaching process so that national development goals are in order to improve community welfare by producing quality and competitive human resources. The purpose of this study is to analyze the Unsupervised Learning technique in conducting cluster mapping of the number of libraries at education levels in Indonesia. The data source was obtained from the Ministry of Education and Culture which was processed by the Central Statistics Agency (abbreviated as BPS) with url: bps.go.id/. The data consisted of 34 records where the attribute used was the number of libraries at each level of education starting from Elementary School, Junior High School, Senior High School and Vocational High School. The Unsupervised Learning Technique used is the k-medoids method which is part of data mining. The mapping label used is the high cluster (K1) for the number of libraries and the low cluster (K2) for the number of libraries at each level of education. The analysis process uses the help of Rapid Miner software. The results of the study indicated that 3 provinces in the high cluster (K1) and 31 provinces in the low cluster (K2) were for elementary schools; 4 provinces in the high cluster (K1) and 30 provinces in the low cluster (K2) for Junior High Schools; 13 provinces in the high cluster (K1) and 21 provinces in the low cluster (K2) for Senior High Schools; 8 provinces in the high cluster (K1) and 26 provinces in the low cluster (K2) for Vocational High Schools. The cluster formed is the optimal school cluster tested with the parameters of the Davies Bouldin Index (DBI) with results of 0.176 (Elementary School), 0.284 (Junior High School), 0.780 (Senior High School) and 0.662 (Vocational High School). The results of the research are expected to provide information in increasing the number of libraries at each level of education so that students and teachers can take advantage of existing library learning resources at school.