Perbandingan Algoritma Random Forest, Logistic Regression, dan Decision Tree Untuk Prediksi High-Score Anime pada Dataset Myanimelist

Abstract

The MyAnimeList platform provides rich structured data encompassing content metadata, user interactions, and community scores. This study aims to compare the performance of three classification algorithms—Random Forest, Logistic Regression, and Decision Tree in predicting whether an anime qualifies as high-score (score ≥ 7.0) based on attributes such as member count, episodes, favorites, popularity rank, type, status, source, and genre. The dataset used is the MyAnimeList Dataset published by azathoth42 on Kaggle, downloaded via KaggleHub. Research stages include exploratory data analysis, feature engineering (label encoding for categorical variables, one-hot encoding for genres, and derived features fav_ratio and is_long_series), numerical feature normalization for Logistic Regression, and stratified 80:20 data splitting. Evaluation uses Accuracy, Precision, Recall, F1-Score, and ROC-AUC metrics, supplemented by 5-fold stratified cross-validation. Results show that Random Forest achieves the best performance, followed by Logistic Regression and Decision Tree. Feature importance analysis reveals that members, rank, and favorites are the most dominant predictors. This study demonstrates that Random Forest ensemble approaches consistently outperform single models for anime content quality classification tasks.