TINJAUAN SISTEMATIS TREN, METODE, DAN DATA PADA PREDIKSI KELULUSAN MAHASISWA
DOI:
https://doi.org/10.21927/ijubi.v8i2.6551Keywords:
Tinjauan Sistematis, Prediksi Kelulusan, Machine Learning, Random Forest, Dataset, Data Tidak SeimbangAbstract
Penelitian tentang prediksi kelulusan mahasiswa banyak dipublikasikan akan tetapi biasanya metode beserta data yang dihasilkan dikemas secara terpisah dan kompleks sehingga gambaran tentang topik prediksi kelulusan mahasiswa saat ini kurang komprehensif. Tinjauan literatur ini bertujuan untuk mengidentifikasi dan menganalisis tren penelitian, dataset, dan metode tentang prediksi kelulusan mahasiswa yang dipublikasikan antara tahun 2020-2025. Berdasarkan kriteria inklusi dan ekslusi, tercatat sebanyak 75 artikel dari 199 artikel yang bersumber pada jurnal kuartil 1-4. Tinjauan literatur sistematis dapat didefinisikan sebagai proses mengidentifikasi, menilai, dan menginterpretasikan semua bukti penelitian yang tersedia untuk memberikan jawaban atas pertanyaan penelitian yang spesifik. Hasil analisis dalam lima tahun terakhir mengungkapkan bahwa penelitian prediksi kelulusan mahasiswa terdapat empat topik yaitu prediksi/klasifikasi, analisis dataset, pengelompokan (clustering), dan estimasi. Selain itu, terdapat juga dua tren yang dibahas yaitu pemilihan fitur (feature selection) dan data tidak seimbang (imbalance data). Kategori data yang digunakan pada lima tahun terakhir lebih banyak menggunakan data private atau data real sebanyak 91% daripada data public. Metode yang paling sering digunakan pada topik-topik tersebut adalah Random Forest (RF), dan paling jarang yaitu metode Artificial Neural Network (ANN). Terdapat juga penggabungan metode untuk optimasi parameter di beberapa klasifikasi.
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