Prediksi Kelompok Usia Pengguna Netflix Menggunakan Metode Random Forest Berdasarkan Analisis Genre Tontonan dan Perilaku Pengguna - Trafin , Abelina Stevie Maria; Masparudin; Febrianti, Eka Lia; syafrinal, ilwan (Fakultas Komputer - Universitas Universal, 2025)
The accuracy of user demographics, particularly age, on video streaming platforms is often compromised by the widespread practice of shared accounts. This study addresses this challenge by implicitly classifying user age groups (Youth, Young Adult, Adult, Middle-Aged, Senior) based solely on behavioral data, including viewing genre frequency, sentiment analysis of reviews, and expenditure patterns. The core methodology employs a Random Forest Classifier optimized with SMOTE (Synthetic Minority Over-sampling Technique) to mitigate the severe class imbalance present in the dataset. The initial Baseline Model performed poorly, achieving only 40,13% accuracy and failing to identify minority classes. After implementing SMOTE and hyperparameter tuning, the Final Model demonstrated significant improvement, achieving an Accuracy of 79,26%. The engineered feature, Spend per Person, was identified as the most dominant predictor, validating the approach of using economic factors to differentiate genuine individual usage. Crucially, the model showed exceptional reliability in detecting sensitive age segments, such as Youth (F1-Score 0,88) and Seniors (F1-Score 0,75). This research provides an effective data-driven solution for enhancing age-based content personalization and parental control features.