Prediksi Ketuntasan Belajar Siswa Menggunakan Naive Bayes dengan Integrasi Data Akademik, Absensi, dan Partisipasi Ekstrakurikuler
DOI:
https://doi.org/10.52436/1.jpti.752Kata Kunci:
Data Akademik, Data Non-Akademik, Machine Learning, Naïve Bayes Classifier, Prediksi Ketuntasan SiswaAbstrak
Peningkatan kualitas pendidikan memerlukan identifikasi dini siswa berisiko tidak tuntas. Penelitian ini bertujuan mengembangkan sistem prediksi ketuntasan siswa menggunakan metode Naive Bayes dengan memanfaatkan data akademik (nilai ujian, absensi) dan non-akademik (partisipasi ekstrakurikuler). Data historis dari 150 siswa SMA dianalisis melalui pra-pemrosesan (normalisasi, penanganan data hilang) dan validasi menggunakan 10-fold cross-validation. Hasil menunjukkan akurasi sistem mencapai 85,2%, presisi 83%, dan recall 87%, dengan nilai ujian sebagai faktor dominan (feature importance = 0,45). Sistem ini memberikan prediksi objektif untuk membantu guru melakukan intervensi tepat waktu, meningkatkan efektivitas pembelajaran. Temuan ini menegaskan potensi Naive Bayes dalam analisis data pendidikan multidimensi.
Unduhan
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