The Comparison of Logistic Regression Methods and Random Forest for Spotify Audio Mode Featurre Classification
Abstract
Studi ini membandingkan kemampuan dari metode regresi logistik dan random forest dalam melakukan klasifikasi fitur mode. Fitur mode ini merupakan fitur yang terdapat di dalam data fitur audio. Secara keseluruhan, data ini berisikan data dari musik atau lagu yang dirilis di platform Spotify yang di dalamnya terdapat berbagai fitur dari masing-masing musik. Dalam melakukan studi ini, metode regresi logistik dan metode random forest ini diterapkan dalam bahasa pemrograman Python. Setelah dilakukannya studi ini dapat disimpulkan bahwa metode random forest dapat melakukan klasifikasi yang lebih baik walaupun dengan selisih yang cukup dekat. Karena kedua metode ini adalah metode yang baik dalam melakukan klasifikasi. Fitur penting yang ditampilkan oleh random forest juga memberikan hasil yang lebih memuaskan, karena fitur yang dihasilkan memang fitur yang berkaitan dengan fitur mode dan sesuai dengan teori musik.
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