CLUSTERING DATA SISWA PUTUS SEKOLAH DENGAN ALGORITMA K-MEANS DAN DBSCAN
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Pembangunan sumber daya manusia dan kemajuan suatu negara bergantung pada pendidikan dasar. Meskipun partisipasi siswa di sekolah dasar telah meningkat pesat, fenomena putus sekolah di jenjang ini masih menjadi masalah besar di Indonesia. Penelitian ini menggunakan algoritma clustering K-Means dan DBScan untuk mengelompok data jumlah siswa putus sekolah setiap kota di Indonesia. Dataset yang digunakan berasal dari Kemendikbud yang diterbitkan pada tahun 2023, dataset ini memiliki variabel kota/kabupate, jumlah siswa putus sekolah SD, jumlah siswa putus sekolah SMP, jumlah siswa putus sekolah SMA, dan jumlah siswa putus sekolah SMK. Metode yang memiliki hasil terbaik yaitu algoritma K-Means dengan nilai K = 2 dengan nilai silhoutte 0.722. Secara umum hasil pengelompokkan menunjukkan daerah dengan jumlah siswa putus sekolah yang tinggi di angka 40 daerah, jumlah ini relatif rendah bila dibanding dengan kelompok daerah dengan jumlah siswa putus sekolah rendah yang bisa mencapai angka 474. Walaupun perbedaan yang cukup signifikan, hal ini bisa menandakan terdapat kesenjangan pendidikan antar daerah sehingga perbedaan nilai yang cukup jauh. Penelitian ini diharapkan bisa menjadi informasi penting bagi pemangku kepentingan.
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