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Peringatan Dini Dropout Mahasiswa: Tinjauan Tiga Jurnal dan Panduan Analisis Data dengan Python

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Tulisan ini membahas bagaimana data akademik kampus, seperti nilai, kehadiran, dan status pembayaran, dapat dimanfaatkan untuk mendeteksi mahasiswa yang berisiko putus kuliah sejak dini. Penulis meninjau tiga artikel jurnal terbaru tentang prediksi dropout dan menyoroti kendala seperti keberagaman tipe data, kualitas data yang tidak konsisten, serta ketimpangan jumlah mahasiswa yang dropout. Salah satu temuan yang dibahas, dengan kombinasi teknik penyeimbang kelas dan CatBoost, menunjukkan performa sangat tinggi, namun penulis mengingatkan perlunya memeriksa potensi kebocoran data saat resampling. Bagian kedua berisi tutorial langkah demi langkah menggunakan Python untuk mengeksplorasi, memvisualisasikan, dan memodelkan data dropout. Penulis juga menekankan bahwa alur kerja pada dataset kecil dapat diterapkan pada data berskala besar.

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