AI APPROACH TO PREDICT STUDENT PERFORMANCE (CASE STUDY: BATTUTA UNIVERSITY)

M. Rhifky Wayahdi, Fahmi Ruziq, Subhan Hafiz Nanda Ginting

Abstract


Penelitian yang dilakukan menggunakan pendekatan kecerdasan buatan (AI) pada proses prediksi kinerja mahasiswa Universitas Battuta. Model kecerdasan buatan yang digunakan adalah model Random Forest. Penulis menggunakan tiga dataset berbeda dengan 300 pohon keputusan untuk proses pelatihan dan pengujian dengan model Random Forest dan melakukan uji coba dengan tiga variasi model. Model pertama (RF-1) menunjukkan akurasi yang tinggi yaitu sebesar 90%, sedangkan model kedua (RF-2) dan ketiga (RF-3) masing-masing memperoleh akurasi sebesar 89%. Matriks konfusi dan laporan klasifikasi (presisi, perolehan, dan skor f1) digunakan untuk mengevaluasi kinerja model kecerdasan buatan yang digunakan. Pada kategori “lulus”, ketiga model memiliki performa yang baik dengan presisi dan perolehan 90–95%. Pada kategori “distinction”, model pertama (RF-1) dan ketiga (RF-3) memiliki presisi dan recall yang lebih baik dibandingkan model kedua (RF-2). Sedangkan pada kategori “gagal”, model kedua (RF-2) menunjukkan performa yang sedikit lebih unggul dibandingkan model lainnya. Hasil penelitian ini menunjukkan bahwa model Random Forest mampu menghasilkan akurasi yang cukup tinggi dalam memprediksi kinerja siswa, yaitu berkisar 80–90%. Dengan demikian, model Random Forest merupakan metode yang cukup efektif untuk memprediksi kinerja siswa. Hasil ini diharapkan dapat digunakan oleh universitas untuk mengidentifikasi mahasiswa yang memerlukan intervensi dini dan meningkatkan strategi pembelajaran yang lebih efektif.


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DOI: https://doi.org/10.54314/jssr.v7i4.2332

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