Klasifikasi Popularitas Buku pada Platform Digital Menggunakan Random Forest Berbasis Fitur Perilaku Pembaca

Al Imran; Sandi Firmasyah; Siti Intan Nurfadilah; Muhammad Alamsyah; Farizi Ilham

doi:10.47065/bulletinds.v5i3.10449

Al Imran * Universitas Pamulang, Tangerang Selatan, Indonesia
Sandi Firmasyah Universitas Pamulang, Tangerang Selatan, Indonesia
Siti Intan Nurfadilah Universitas Pamulang, Tangerang Selatan, Indonesia
Muhammad Alamsyah Universitas Pamulang, Tangerang Selatan, Indonesia
Farizi Ilham Universitas Pamulang, Tangerang Selatan, Indonesia

(*) Corresponding Author

DOI: https://doi.org/10.47065/bulletinds.v5i3.10449

Keywords: Machine Learning; Digital Book; Popularity; Classification; Book-Crossing

Abstract

The assessment of book popularity on digital community platforms has long relied on single indicators such as average ratings, while popularity is a multidimensional construct involving broader community engagement. This study aims to: (1) analyze reader behavior patterns as a popularity indicator for digital books in the Book-Crossing dataset; (2) develop a multiclass popularity classification model using the Random Forest algorithm; and (3) generate strategic recommendations for authors, publishers, and digital platform managers. A quantitative approach was employed utilizing secondary data from the Book-Crossing dataset, which after preprocessing yielded 10,234 books eligible for analysis. A Random Forest Classifier was constructed using six reader behavior and book metadata features, with popularity labels (Popular, Moderately Popular, Less Popular) determined through quartile distribution analysis to address class imbalance. Results show the model achieved an accuracy of 91.2% with an F1-Score of 0.94 for the Popular category, while popularity distribution follows a power law pattern in which 10.0% of books fall into the Popular category and 58.2% reside in the Less Popular category. These findings demonstrate that combining multivariable reader behavior features can produce accurate and measurable popularity classification, while also confirming the popularity bias inherent in digital content ecosystems.

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