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All Journal CESS (Journal of Computer Engineering, System and Science) SISFOTENIKA JOURNAL OF APPLIED INFORMATICS AND COMPUTING Angkasa: Jurnal Ilmiah Bidang Teknologi Compiler CSRID (Computer Science Research and Its Development Journal) Journal of Computer Networks, Architecture and High Performance Computing Jurnal Teknik Informatika (JUTIF) Jurnal Ilmiah IT CIDA : Diseminasi Teknologi Informasi
Dony Ariyus
Unknown Affiliation
Computer Science & IT Education

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Optimizing Email Spam Detection through Handling Class Imbalance with Class Weights and Hyperparameter Using GridSearchCV Nursyam, Muhammad Ridho; Koprawi, Muhammad; Ariyus, Dony
Journal of Applied Informatics and Computing Vol. 10 No. 1 (2026): February 2026
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v10i1.12060

Abstract

Email spam is a major problem in digital communication that can disrupt productivity, burden network resources, and pose a security threat. This research focuses on optimizing spam email detection using a machine learning approach by addressing class imbalance through class weighting and hyperparameter tuning using GridSearchCV. To improve model accuracy and sensitivity, a combination of diverse datasets is applied to provide a wider scope of training data. The models used in this study include Support Vector Machine (SVM), Random Forest, Multinomial Naive Bayes (MNB), and XGBoost. Evaluation is carried out based on metrics such as accuracy, precision, recall, and F1-score, before and after hyperparameter tuning. The experimental results show that SVM produces the highest accuracy after tuning, reaching 97.10%, compared to 96.73% before hyperparameter tuning. In addition, Random Forest, MNB, and XGBoost also show significant improvements, with each model achieving better performance after tuning. Overall, this study shows that dataset merging and class weight adjustment can significantly improve the model's ability to detect spam, as well as provide a basis for implementing the model in a more effective email spam detection system.
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 Ahmad Rifki Harir Amalina, Inna Nur Ashari, Wahid Miftahul Banu Santoso Deuis Nur Astrida Eka Yulia Sari I Wayan Pandu Swardiana Irma Darmayanti Julia Kurniasih Koprawi, Muhammad Muhammad Saifulloh Neno, Friden Elefri Norhikmah Norhikmah Nursyam, Muhammad Ridho Patmawati Hasan Prasetyo, Rafli Ilham Profesi, Dwinda Etika Saputra Aji, Dian Tetik, Yulius Nahak Yuliana Yuliana Yunita, Selviana