Penerapan Data Mining Dalam Prediksi Kinerja Akademik Mahasiswa Menggunakan Algoritma Machine Learning

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Prediksi kinerja akademik mahasiswa menjadi topik penting dalam pendidikan tinggi karena dapat membantu institusi mengidentifikasi mahasiswa yang berisiko mengalami penurunan prestasi dan merancang intervensi yang tepat. Berbagai penelitian telah memanfaatkan teknik data mining untuk tujuan ini, namun sejauh ini belum ada pemetaan komprehensif mengenai tren, metode, dan efektivitas pendekatan yang digunakan. Penelitian ini bertujuan melakukan Systematic Literature Review (SLR) dengan panduan Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guna mengkaji penerapan data mining dalam prediksi kinerja akademik mahasiswa. Pencarian literatur dilakukan pada basis data Scopus, IEEE Xplore, ScienceDirect, SpringerLink, dan Google Scholar dengan kriteria publikasi tahun 2016–2025. Dari 527 artikel awal, proses seleksi menghasilkan 24 artikel yang memenuhi kriteria inklusi. Hasil analisis menunjukkan bahwa algoritma seperti Decision Tree, Support Vector Machine, Artificial Neural Network, dan Graph Convolutional Networks digunakan secara luas dengan tingkat akurasi bervariasi. Faktor penentu performa model meliputi kualitas dataset, teknik feature selection, dan metode evaluasi. Penelitian ini menegaskan bahwa meskipun akurasi model terus meningkat, interpretabilitas hasil dan generalisasi model masih menjadi tantangan. Temuan ini memberikan landasan bagi pengembangan metode prediksi yang lebih efektif dan adaptif di masa depan.


Kata Kunci


Data mining, prediksi kinerja akademik, mahasiswa, machine learning

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Referensi


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DOI: http://dx.doi.org/10.30811/jim.v10i2.7643

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