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All Journal Sistemasi: Jurnal Sistem Informasi Jurnal Teknik Informatika (JUTIF)
Amalina, Inna Nur
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Computer Science & IT Electrical & Electronics Engineering

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Analysis of the Performance Comparison between Random Forest and SVM RBF in Detecting Cyberbullying on Imbalanced Data with the SMOTE Approach Amalina, Inna Nur; Norhikmah, Norhikmah; Ashari, Wahid Miftahul
Sistemasi: Jurnal Sistem Informasi Vol 14, No 6 (2025): Sistemasi: Jurnal Sistem Informasi
Publisher : Program Studi Sistem Informasi Fakultas Teknik dan Ilmu Komputer

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32520/stmsi.v14i6.5574

Abstract

Cyberbullying has emerged as a growing threat with the widespread adoption of social media, creating significant risks to online safety. Automatic detection of such behavior remains challenging, particularly when the training dataset is highly imbalanced. This study presents a comparative analysis of Random Forest and Support Vector Machine with Radial Basis Function kernel (SVM RBF) for cyberbullying detection, incorporating the Synthetic Minority Over-sampling Technique (SMOTE) to address class imbalance. The experiments utilized a publicly available, manually annotated dataset containing 47,693 English-language tweets from global users, labeled as cyberbullying or non-cyberbullying. Performance was evaluated using accuracy, precision, recall, and F1-score. Results indicate that Random Forest achieved the highest performance before SMOTE (accuracy = 88.52%, precision = 89.07%, recall = 94.00%, F1-score = 91.49%), while SMOTE improved recall for both algorithms but reduced accuracy and precision. These findings highlight that the choice of algorithm and effective handling of class imbalance are critical for enhancing the reliability of automated cyberbullying detection systems, thereby enabling more effective content moderation and safer online environments.
Regression Based Prediction of Roblox Game Popularity Using Extreme Gradient Boosting with Hyperparameter Optimization Amalina, Inna Nur; Norhikmah, Norhikmah; Ariyus, Dony; Koprawi, Muhammad; Prasetyo, Rafli Ilham
Jurnal Teknik Informatika (Jutif) Vol. 7 No. 1 (2026): JUTIF Volume 7, Number 1, February 2026
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jutif.2026.7.1.5648

Abstract

The rapid growth of the digital gaming industry has increased the importance of predicting game popularity on user-generated content platforms such as Roblox, where diverse games and highly variable user engagement patterns create challenges in modeling long-term popularity trends. This study aims to develop a regression-based popularity prediction model using the Extreme Gradient Boosting (XGBoost) algorithm based on user interaction indicators, including visits, likes, dislikes, favorites, and active players. To investigate the effect of model optimization, hyperparameter tuning is performed using GridSearchCV. Model performance is evaluated using Mean Absolute Error (MAE), Mean Squared Error (MSE), Root Mean Squared Error (RMSE), and the Coefficient of Determination (R²). Experimental results show that the baseline XGBoost model achieves an R² value of 80.74%, indicating strong capability in capturing non-linear popularity patterns. However, the optimized model yields a lower R² value of 77.71%, accompanied by slight increases in prediction error metrics, revealing that hyperparameter optimization does not always improve performance for highly skewed popularity data. Feature importance analysis further indicates that interaction-based attributes, particularly likes and dislikes, are the most influential predictors. These findings provide an important contribution to Informatics research by demonstrating the effectiveness of ensemble regression models for digital entertainment analytics while highlighting the need for critical evaluation of optimization strategies rather than assuming universal performance gains.
Co-Authors Ashari, Wahid Miftahul Dony Ariyus Koprawi, Muhammad Norhikmah Norhikmah Prasetyo, Rafli Ilham