Optimalisasi Pembagian Kelas Peminatan Berdasarkan Minat dan Bakat Siswa Menggunakan K-Means dan Algoritma Genetika

Oktaviansyah, Muhammad Andika (2025) Optimalisasi Pembagian Kelas Peminatan Berdasarkan Minat dan Bakat Siswa Menggunakan K-Means dan Algoritma Genetika. Undergraduate thesis, Universitas 17 Agustus 1945 Surabaya.

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Abstract

Proses pembagian kelas peminatan di sekolah menengah sering kali menghadapi kendala dalam menentukan kelompok siswa secara adil dan tepat, terutama ketika mempertimbangkan aspek minat dan bakat. Penelitian ini bertujuan untuk mengembangkan sistem pengelompokan kelas peminatan secara optimal dengan memanfaatkan metode K-Means Clustering dan Algoritma Genetika. Metode K-Means digunakan untuk melakukan klasterisasi awal berdasarkan data minat siswa menggunakan indikator RIASEC, yang kemudian dimaknai sebagai empat kelompok peminatan: IPA, IPA Campuran, IPS, dan IPS Campuran. Selanjutnya, Algoritma Genetika diimplementasikan untuk mengoptimalkan distribusi siswa dalam tiap kelas dengan mempertimbangkan kecocokan siswa terhadap peminatan, jumlah siswa per kelas, tingkat perpindahan dari peminatan awal, serta nilai entropy sebagai indikator homogenitas kelas. Hasil penelitian menunjukkan bahwa sistem yang dibangun mampu menghasilkan pembagian kelas yang seimbang dan relevan, dengan distribusi jumlah siswa dalam rentang ideal (30–35 siswa per kelas) serta rata-rata kecocokan peminatan yang tinggi. Nilai akurasi sebesar 73,68% menunjukkan bahwa mayoritas siswa tetap berada di peminatan awal mereka. Meskipun terjadi penurunan nilai silhouette score dari 0,287 menjadi 0,080 setelah optimasi, hal ini merupakan hasil dari penyesuaian terhadap batasan kelas yang realistis. Selain itu, nilai entropy akhir sebesar 0,4821 menunjukkan bahwa tingkat keberagaman latar belakang peminatan dalam setiap kelas berada pada tingkat menengah, yang berarti distribusi kelas cenderung lebih homogen dibandingkan kondisi awal. Hal ini diharapkan mampu meningkatkan efektivitas pembelajaran dalam kelas yang lebih seragam. Penelitian ini membuktikan bahwa integrasi metode data mining dan optimasi dapat digunakan sebagai pendekatan berbasis data dalam pembentukan kelas peminatan secara objektif dan efisien.

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