OPTIMALISASI ALGORITMA RANDOM FOREST FEATURE SELECTION DAN HYPERPARAMETER TUNING KLASIFIKASI GENRE MUSIK
DOI:
https://doi.org/10.31000/jika.v9i1.12216Abstrak
Mendengarkan musik merupakan aspek penting dari kehidupan manusia, namun pengenalan genre musik secara subjektif menambah kompleksitas dalam proses klasifikasinya. Oleh karena itu, diperlukan pendekatan yang teliti dan andal untuk menganalisis serta mengelompokkan data musik. Metode Random Forest banyak digunakan dalam klasifikasi genre musik, memerlukan optimalisasi algoritma yang presisi melalui Feature Selection dan Hyperparameter Tuning. Manfaat penelitian ini yaitu untuk memberikan pemahaman mengenai peran teknik Feature Selection dan Hyperparameter Tuning dalam mengoptimalkan performa algoritma Random Forest. Dengan memanfaatkan algoritma secara maksimal, akurasi klasifikasi genre musik dapat ditingkatkan, yang berperan penting dalam menciptakan sistem rekomendasi musik yang lebih tepat dan akurat. Penelitian ini diawali dengan pengumpulan data yang diolah dalam proses preprocessing untuk mendapatkan data yang bersih. Fitur-fitur dalam dataset dipilih melalui Feature Selection untuk mendapatkan fitur yang mampu merepresentasikan kelas genre musik. Metode Random Forest digunakan untuk klasifikasi, diikuti dengan Hyperparameter Tuning untuk mendapatkan parameter yang optimal. Hasil pengujian menunjukkan bahwa metode Random Forest memiliki nilai ROC AUC sebesar 0.909. Optimalisasi meningkatkan kinerja dengan nilai ROC AUC menjadi 0.913, menunjukkan peningkatan kinerja model sebesar 0.004 dan masuk kategori evaluasi yang excellentReferensi
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