Profiling the Inequality of School Water, Sanitation, and Hygiene Facilities Among Indonesian Regions Using Cluster Analysis

Clustering Elementary school WASH

Authors

  • Didik Bani Unggul
    didikbaniunggul@gmail.com
    Department of Statistics, Faculty of Science and Data Analytics, Institut Teknologi Sepuluh Nopember, Surabaya 60111, Indonesia
  • Khomaria Nurul Ainy 1. Department of Statistics, Faculty of Science and Data Analytics, Institut Teknologi Sepuluh Nopember, Surabaya 60111, Indonesia; 2.Center for Data and Information Technology, Ministry of Education, Culture, Research and Technology of the Republic of Indonesia, Jakarta 10270, Indonesia
  • Roudlotul Jannah 1. Department of Statistics, Faculty of Science and Data Analytics, Institut Teknologi Sepuluh Nopember, Surabaya 60111, Indonesia; 2. Academic Directorate of Vocational Higher Education, Ministry of Education, Culture, Research and Technology of the Republic of Indonesia, Jakarta 10270, Indonesia
January 30, 2023
The results of clustering for public schools

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Introduction: Humans rely heavily on Water, Sanitation, and Hygiene (WASH) facilities. Goal 6 of the Sustainable Development Goals (SDGs) emphasizes ensuring communities possess universal access to clean water and sanitation. Because WASH is tremendously crucial in schools, the objective of this study is to provide a comprehensive profile of regional inequalities based on the availability of WASH indicators through cluster analysis. Methods: This study administered cross-sectional data from 514 regencies/cities in Indonesia with three variables, i.e. percentage of access to water, sanitation, and hygiene at public and private elementary schools. The profiling was performed by conducting K-means clustering method. Results and Discussion: Public and private schools were examined separately as the p-value in the difference test was less than 0.05. In accordance with the silhouette plot, the optimal number of clusters was two for each category. For the public-school category, the number of regencies/cities in Cluster 1 was 380 regencies/cities and 134 regencies/cities were in Cluster 2. For the private school category, Cluster 1 incorporated 418 regencies/cities and Cluster 2 merely encompassed 96 regencies/cities. Conclusion: Two clusters for each type of school had been established with Cluster 1 consisting of areas with high availability of WASH facilities while areas in Cluster 2 possessed a relatively low percentage in the three WASH indicators. There were 66 regencies/cities, generally located in eastern Indonesian provinces, grouped in Cluster 2 for both types of schools.