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All Journal JoDENS: Journal of Digital Ecosystem for Natural Sustainability JUTEKOM - Jurnal Teknologi dan Ilmu Komputer
Masparudin
Unknown Affiliation
Computer Science & IT Decision Sciences, Operations Research & Management

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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
Journal of Digital Ecosystem for Natural Sustainability Vol 5 No 2 (2025): Desember 2025
Publisher : Fakultas Komputer - Universitas Universal

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.63643/jodens.v5i2.326

Abstract

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.
Evaluating Resampling Methods for Imbalanced Necrosis Classification on CT Scans Purnajaya, Akhmad Rezki; Masparudin
Jurnal Teknologi informasi dan Ilmu Komputer Vol. 2 No. 2 (2026): April 2026
Publisher : Nolsatu Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.65258/jutekom.v2.i2.56

Abstract

Necrosis, or body tissue death, occurs when there is insufficient blood flow to the tissue, which can be caused by injury, radiation, or chemicals. One of the main challenges in the automated diagnosis of necrosis is data imbalance in medical datasets, where the number of pathological cases is far less than normal cases. To address this issue, this study implements and evaluates various data sampling techniques, including Random Undersampling (RUS), Random Oversampling (ROS), Combination of Over-Undersampling (COUS), Synthetic Minority Over-sampling Technique (SMOTE), and Tomek Link, then using a Support Vector Machine (SVM) as the classifier. The test results show that the best sampling technique is the Synthetic Minority Over-sampling Technique (SMOTE), which successfully achieved an accuracy of 100% and an Area Under Curve (AUC) of 100%, indicating its significant potential in improving the accuracy of necrosis diagnosis from CT scans.
Co-Authors Febrianti, Eka Lia Ilwan Syafrinal Purnajaya, Akhmad Rezki Trafin , Abelina Stevie Maria