Sistem Peringatan Dini Penurunan Prestasi dan Minat Belajar pada Siswa Sekolah Menengah di Medan: Pendekatan Explainable Machine Learning
Penulis
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Arpan Arpan
Universitas Pembangunan Panca Budi
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Mohammad Yusup
Universitas Pembangunan Panca Budi
DOI:
https://doi.org/10.62712/juktisi.v4i2.689Kata Kunci:
Minat belajar, Prestasi akademik, Early warning system, explainable machine learning, SHAP, Medan, Sekolah menengahAbstrak
Latar Belakang: Sekolah menengah di Medan menghadapi penurunan minat belajar dan prestasi akademik pascapandemi. Deteksi dini yang akurat dan dapat dijelaskan dibutuhkan agar intervensi tidak terlambat. Tujuan: Mengembangkan dan mengevaluasi Early Warning System (EWS) berbasis Explainable Machine Learning untuk memprediksi (i) minat belajar rendah dan (ii) penurunan prestasi, sekaligus memaparkan pendorong risiko yang dapat ditindaklanjuti. Metode: Studi multi‑sekolah menggunakan data administrasi (kehadiran, keterlambatan, disiplin), jejak keterlibatan (proporsi tugas/PR tidak terkumpul, partisipasi kelas, log LMS bila tersedia), nilai rapor, serta survei minat & kesejahteraan 10–12 item. Dibandingkan tiga algoritma (Regresi Logistik, Random Forest, Gradient Boosting) dengan kalibrasi probabilitas; interpretabilitas menggunakan SHAP. Evaluasi pada set uji meliputi AUC, F1, recall-terkondisi (target ≥0,75), Brier, serta audit keadilan lintas gender dan status sosial ekonomi (SSE). Hasil (ilustratif): Gradient Boosting mencapai AUC 0,84 (minat) dan 0,82 (prestasi) dengan ECE ≈0,025 dan Brier ≈0,150. Fitur teratas: tugas/PR tidak terkumpul, skor minat intrinsik, presensi bulan sebelumnya, partisipasi kelas, dan nilai Matematika terkini. Perbedaan AUC antargender ≈0,02 dan F1 antar‑SSE ≈0,04. Kesimpulan: EWS yang akurat, terkalibrasi, dan dapat dijelaskan memungkinkan guru/BK memprioritaskan siswa berisiko dan memilih intervensi bertingkat (Tier 1–3) secara lebih tepat. Implementasi perlu disertai tata kelola data dan pemantauan berkala.
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