Tinjauan Literatur Sistematis: Prediksi Dini Risiko Penurunan Prestasi Akademik Berbasis Machine Learning
DOI:
https://doi.org/10.52436/1.jpti.2187Kata Kunci:
academic early warning system, educational data mining, machine learning, penurunan performa akademik, student performance predictionAbstrak
Penurunan performa akademik merupakan permasalahan penting dalam pendidikan karena dapat berdampak pada keberhasilan belajar, motivasi, dan keberlanjutan studi peserta didik. Perkembangan Educational Data Mining (EDM), Machine Learning (ML), dan Learning Analytics mendorong pemanfaatan sistem prediktif untuk mendeteksi risiko akademik sejak dini. Meskipun demikian, sebagian besar penelitian masih menitikberatkan pada prediksi hasil pembelajaran akhir dibandingkan identifikasi dini yang mendukung intervensi preventif. Penelitian ini bertujuan mensintesis perkembangan penelitian terkait prediksi dini risiko penurunan performa akademik serta mengidentifikasi faktor prediktif, pendekatan yang digunakan, dan arah pengembangan sistem academic early warning. Kajian dilakukan melalui metode Systematic Literature Review (SLR) dengan mengikuti pedoman PRISMA 2020. Proses seleksi menghasilkan 60 studi primer yang dipublikasikan pada periode 2020–2026 dan dianalisis menggunakan pendekatan deskriptif serta tematik. Hasil penelitian menunjukkan bahwa pendekatan ML, khususnya Random Forest, XGBoost, Support Vector Machine, dan Logistic Regression, merupakan metode yang paling banyak digunakan, dengan Random Forest muncul pada 30 dari 60 studi primer. Sebagian besar penelitian melaporkan akurasi prediksi berada pada kisaran 80%–99%, sementara beberapa model deep learning dan hybrid learning mampu mencapai akurasi di atas 98%, sehingga menunjukkan peningkatan performa prediksi dibandingkan pendekatan konvensional. Faktor akademik masih menjadi prediktor utama, namun penelitian terkini semakin mengintegrasikan faktor perilaku, keterlibatan belajar, aktivitas digital, pola temporal, dan faktor psikososial. Selain itu, ditemukan bahwa aspek interpretabilitas, adaptivitas, generalisasi lintas institusi, dan dukungan intervensi akademik masih menjadi tantangan utama. Penelitian ini menegaskan pentingnya pengembangan sistem early warning yang lebih interpretatif, adaptif, dan berorientasi pada intervensi untuk mendukung pengambilan keputusan pendidikan secara preventif.
Unduhan
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