Klasterisasi Profil Sekolah Penerima Bantuan Revitalisasi SMK 2025 berbasis K-Medoids dan Gower Distance
DOI:
https://doi.org/10.66599/wp.v1i1.40Keywords:
Gower distance, klaster, k-medoids fasterpam, revitalisasi smkAbstract
Purpose.Penelitian ini bertujuan memetakan profil sekolah penerima bantuan berdasarkan 12 variabel multidimensi serta merumuskan rekomendasi sebagai dasar kebijakan revitalisasi berikutnya. Method. Penelitian ini menggunakan metode klasterisasi dengan Gower Distance untuk menangani data campuran kategorik dan numerik, serta algoritma K-Medoids dengan implementasi FasterPAM. Jumlah klaster optimal ditentukan melalui kombinasi Hierarchical Clustering dengan Ward Linkage dan Silhouette Score yang kemudian divisualisasikan dengan Multidimensional Scaling. Finding. Hasil analisis mengidentifikasi bahwa terdapat tiga klaster sekolah penerima bantuan, yaitu Klaster 1 berisi 454 sekolah (31%) sebagai kelompok berpengalaman yang terkonsentrasi di Pulau Jawa, Klaster 2 berisi 600 sekolah (41%) dengan alokasi anggaran tertinggi yang menjangkau wilayah 3T dan menunjukkan proporsi pelaksanaan tahun tunggal yang relatif baik, serta Klaster 3 berisi 411 sekolah (28%) sebagai penerima pertama kali yang teridentifikasi sebagai kelompok prioritas untuk pendampingan intensif. Practical Implication.Temuan penelitian menunjukkan bahwa selain besaran anggaran, faktor pengalaman institusional sekolah dan distribusi penjadwalan operasional turut berperan penting dalam mendukung kelancaran pelaksanaan program. Originality. Hasil penelitian ini memberikan dasar empiris bagi pengembangan kebijakan revitalisasi SMK yang lebih terdiferensiasi dan adaptif ke depan.
Purpose. This study aims to map the profile of aid recipient schools based on 12 multidimensional variables and formulate recommendations as a basis for subsequent revitalization policies. Method. This study uses the clustering method with Gower Distance to handle mixed categorical and numeric data, as well as the K-Medoids algorithm with FasterPAM implementation. The optimal number of clusters is determined through a combination of Hierarchical Clustering with Ward Linkage and Silhouette Score which is then visualized with Multidimensional Scaling. Findings. The results of the analysis identified that there are three clusters of aid recipient schools, namely Cluster 1 containing 454 schools (31%) as an experienced group concentrated in Java Island, Cluster 2 containing 600 schools (41%) with the highest budget allocation reaching the 3T region and showing a relatively good proportion of single-year implementation, and Cluster 3 containing 411 schools (28%) as first-time recipients identified as a priority group for intensive mentoring. Practical Implications. The research findings show that in addition to the budget size, the institutional experience of the school and the distribution of operational scheduling also play an important role in supporting the smooth implementation of the program. Originality. The results of this study provide an empirical basis for the development of a more differentiated and adaptive vocational high school revitalization policy in the future.
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