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Jati Sumarah
Politeknik Dharma Patria, Kebumen, Jawa Tengah
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Journal : JURNAL MEDIA INFORMATIKA BUDIDARMA

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Pemanfaatan Algoritma K-Means untuk Pengelompokkan Angka Partisipasi Sekolah di Jawa Tengah Jati Sumarah; Ajeng Tiara Wulandari
JURNAL MEDIA INFORMATIKA BUDIDARMA Vol 5, No 4 (2021): Oktober 2021
Publisher : STMIK Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/mib.v5i4.3277

Abstract

In Indonesia, the School Participation Rate (APS) is recognized as one of the indicators of the success of developing education services in regions, whether Province, Regency, or City. The higher the rate of school enrollment, the more successful an area is at providing access to educational services. The dataset was obtained from the Central Statistics Agency (BPS) of Central Java Province's website. The object studied is the percentage of APS in the Central Java region from 2017 to 2019 for ages 7 to 12, 13 to 15, and 16 to 18. The study's goal was to conduct an analysis in the form of mapping the School Participation Rate in the districts and cities of Central Java, the third most populous province after West and East Java. RapidMiner software is used in the analysis process. The research output is a map of clusters of areas in the Regency and City areas. The k-means method, which is part of clustering data mining, is the solution method offered. The number of mapping clusters in this study was divided into two categories: high (C1) and low (C2) clusters. According to the study's findings, the mapping of the 7-12 year old cluster was 24 provinces in the high cluster (cluster 0) and 11 provinces in the low cluster (cluster 1); the mapping of the 13-15 year old cluster is 23 provinces in the high cluster (cluster 0) and 12 provinces in the low cluster (cluster 1); and the mapping of the 16-18 year old cluster is 15 provinces in the low cluster (cluster 1). Cluster determination is based on the final centroid value, with the final centroid value of the 7-12 year old cluster being high (cluster 0) 99.81, 99.87, 99.75; low (cluster 1) 99.73, 99.43, 99.25; and the centroid value of the 13-15 year old cluster being high (cluster 0) 97.52, 97.12, 96.93; low (cluster 1) 93.78, 93.58 Overall, the mapping results show a high percentage for all age groups, which is greater than 50% in the high cluster. In detail, 24 provinces (57 percent) are in the low cluster for the 16-18 year age group. The research findings can provide a macro picture of the level of development of the School Enrollment Rate over the last few years
Kluster Rata-Rata Lama Sekolah (RLS) Menurut Jenis Kelamin di Provinsi Jawa Tengah dengan K-Means Ajeng Tiara Wulandari; Jati Sumarah
JURNAL MEDIA INFORMATIKA BUDIDARMA Vol 5, No 4 (2021): Oktober 2021
Publisher : STMIK Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/mib.v5i4.3279

Abstract

The average length of school (RLS) describes the level of achievement in school activities of each citizen in a given area. The higher the number of years of schooling, the higher the population's level of education, hence this indicator is critical since it can reveal the quality of a region's human resources. Furthermore, numerous studies have found that the average length of schooling has a major impact on economic growth. This indicates that when the average length of schooling rises, the number of unemployed and poor people in a given area declines, resulting in a positive and considerable impact on economic growth. The goal of the study was to use artificial intelligence to undertake an analysis in the form of a cluster mapping of the Average Length of Schooling in Regencies and Cities in Central Java (AI). This is necessary in order to gain a macro picture of the average years of schooling's progress over the last few years through regional mapping. The data was taken from the Central Java Statistics Agency (BPS) website, and it was based on the subject Average Length of School (RLS) by gender from 2017 to 2019. The k-means method, which is part of clustering data mining, was employed as the solution method. There were two types of clusters used in this study: high and low clusters. RapidMiner software is used to aid the analyzing process. Preprocessing is done before the k-means approach by taking the average value of the number of RLS based on gender from 2017 to 2019. K-means will be used to process the results of the average value obtained. According to the findings, eight provinces (23 percent) were in the high cluster (cluster 1) while 27 provinces (77 percent) were in the low cluster (cluster 0). According to the findings, RLS levels are still low in over 70% of Central Java's locations.
Co-Authors Ajeng Tiara Wulandari