Implementation of K-Means Clustering in Mapping Teacher Distribution Using Geographic Information System

Khairul Muttaqin, Universitas Samudra, Indonesia
Rif’ah Nurhidayah, Universitas Samudra, Indonesia
Novianda Novianda, Universitas Samudra, Indonesia
Ahmad Ihsan, Universitas Samudra, Indonesia
Jumriani Sultan, Universitas Negeri Yogyakarta, Indonesia
Fina Rifqiyah, Universitas Negeri Yogyakarta, Indonesia

 10.21831/elinvo.v9i1.76884

Abstract


The placement of teachers in Indonesia has not been evenly distributed across several regions due to inaccurate recruitment and placement processes. The quality of education, particularly in rural areas, is negatively impacted by this uneven distribution. Teachers play a crucial role in enhancing education, making it essential to address this issue. This study seeks to equilibrate the allocation of teachers in Langsa City using the K-Means Clustering method based on the number of teachers, students, and study groups at the Madrasah Ibtidaiyah, Madrasah Tsanawiyah, and Madrasah Aliyah levels. The clustering results are then mapped using the Quantum Geographic Information System. The study identifies 20 schools with a shortage of teachers, 7 schools with sufficient teachers, and 3 schools with a surplus. The utilization of the K-Means Clustering method demonstrated a high accuracy rate of 92.8%. The implications of these findings suggest that educational authorities can use the clustering results to strategically address teacher shortages by reallocating teaching resources more effectively, thus potentially improving educational outcomes in underserved areas. Moreover, the GIS mapping offers a practical tool for ongoing monitoring and decision-making regarding teacher distribution.

Keywords


Teachers’ distribution; clustering; K-Means; GIS; QGIS

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References


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DOI: https://doi.org/10.21831/elinvo.v9i1.76884

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