PREDIKSI PILIHAN PROGRAM STUDI MAHASISWA BARU MENGGUNAKAN RULES AS FEATURES
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Abstract
Penerimaan mahasiswa baru (PMB) merupakan proses strategis yang berperan penting dalam keberlanjutan institusi pendidikan tinggi. Penelitian ini bertujuan untuk menganalisis dan memprediksi pilihan program studi calon mahasiswa menggunakan pendekatan Rules-as-Features (RAF). Metode ini dipilih karena mampu mengubah hasil Association Rule Mining (ARM) menjadi fitur prediktif yang meningkatkan akurasi dan interpretabilitas model pembelajaran mesin. Algoritma Apriori digunakan untuk menemukan pola hubungan antar atribut seperti asal wilayah dan jenis sekolah, yang kemudian dikonversi menjadi fitur biner RULE_1 hingga RULE_27 dan digabungkan ke dataset utama. Model Random Forest dipilih karena kemampuannya mengelola data berdimensi tinggi serta menghasilkan estimasi yang stabil dibanding Logistic Regression. Hasil penelitian menunjukkan bahwa integrasi RAF meningkatkan akurasi dari 0,82 menjadi 0,89, F1-score (macro) dari 0,80 menjadi 0,87, dan AUC mikro dari 0,86 menjadi 0,92. Temuan ini menunjukkan bahwa kombinasi ARM–RAF efektif dalam menangkap hubungan nonlinier antar atribut, menghasilkan model yang akurat sekaligus dapat dijelaskan. Pendekatan ini dapat dimanfaatkan untuk mendukung strategi promosi, penentuan kuota, dan rekomendasi program studi berbasis data.
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