Perbandingan Kinerja Metode Klasifikasi Untuk Memprediksi Putus Sekolah Dan Keberhasilan Akademik Siswa
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Abstract
Putus sekolah dan keberhasilan akademik siswa merupakan dua hal yang penting dalam pendidikan. Penelitian ini membandingkan kinerja metode klasifikasi untuk memprediksi putus sekolah dan keberhasilan akademik siswa. Metode klasifikasi yang digunakan adalah Random Forest Classifier, AdaBoost, Decision Tree, Logistic Regression, dan XGBoost. Dataset yang digunakan berasal dari perguruan tinggi yang memiliki 4424 sampel dengan 36 fitur dan 3 kelas. Hasil penelitian menunjukkan bahwa metode Random Forest Classifier memiliki kinerja terbaik dengan akurasi 76%, diikuti oleh XGBoost 76%, AdaBoost 74%, Logistic Regression 74%, dan Decision Tree 71%. Oleh karena itu, metode Random Forest Classifier dapat digunakan untuk memprediksi putus sekolah dan keberhasilan akademik siswa dengan lebih akurat. Namun, perlu dicatat bahwa meskipun semua metode klasifikasi yang digunakan dalam penelitian ini telah mengalami perbaikan kinerja melalui penggunaan teknik ADASYN dan penyetelan parameter, mereka masih menghadapi tantangan dalam mengidentifikasi dengan akurat kasus-kasus dalam salah satu kelas minoritas. Oleh karena itu, langkah selanjutnya yang perlu diambil adalah melakukan penelitian lebih lanjut untuk mengoptimalkan parameter dengan lebih cermat dan juga mempertimbangkan pendekatan lain yang dapat lebih lanjut meningkatkan kinerja model, seperti mempertimbangkan penambahan informasi tambahan yang mungkin ada dalam dataset.
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