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All Journal Building of Informatics, Technology and Science Jurnal Teknik Informatika (JUTIF) Journal of Applied Data Sciences Jurnal Pendidikan dan Teknologi Indonesia Decode: Jurnal Pendidikan Teknologi Informasi
Giat Karyono
STMIK AMIKOM Purwokerto
Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Education Engineering Languange, Linguistic, Communication & Media Other

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Journal : Jurnal Teknik Informatika (JUTIF)

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Enhancing Customer Purchase Behavior Prediction Using PSO-Tuned Ensemble Machine Learning Models Kafilla, Princess Iqlima; Utomo, Fandy Setyo; Karyono, Giat
Jurnal Teknik Informatika (Jutif) Vol. 6 No. 5 (2025): JUTIF Volume 6, Number 5, Oktober 2025
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jutif.2025.6.5.4952

Abstract

Predicting customer purchase behavior remains a significant challenge in e-commerce and marketing analytics due to its complex and nonlinear patterns. This study introduces a machine learning framework that integrates ensemble learning models with Particle Swarm Optimization (PSO) for hyperparameter tuning to improve classification accuracy and class discrimination. Several ensemble algorithms, including CatBoost, XGBoost, LightGBM, AdaBoost, and Gradient Boosting, were compared against a baseline Logistic Regression model, both with default and PSO-optimized configurations. Experiments on a real-world e-commerce dataset containing behavioral and demographic variables showed that ensemble methods substantially outperformed traditional models across accuracy, F1-score, and ROC AUC metrics. Notably, the PSO-tuned Gradient Boosting model achieved the highest ROC AUC of 0.9547, improving the AUC by approximately 0.0076 compared to its default configuration, while CatBoost obtained the highest overall accuracy and F1-score. PSO optimization was especially effective in enhancing simpler models such as Logistic Regression but showed marginal gains and some convergence instability in more complex ensemble models. Feature importance analyses consistently identified variables such as time spent on the website, discounts availed, age, and income as key drivers of purchase intent. These findings demonstrate the benefit of combining ensemble learning with metaheuristic optimization, offering actionable insights for developing robust, data-driven marketing strategies.
Comparative Analysis of Decision Tree, Random Forest, Svm, and Neural Network Models for Predicting Earthquake Magnitude Turino, Turino; Saputro, Rujianto Eko; Karyono, Giat
Jurnal Teknik Informatika (Jutif) Vol. 6 No. 2 (2025): JUTIF Volume 6, Number 2, April 2025
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jutif.2025.6.2.2378

Abstract

This study conducts a comparative analysis of four machine learning algorithms—Decision Tree, Random Forest, Support Vector Machine (SVM), and Neural Network—to predict earthquake magnitudes using the United States Geological Survey (USGS) earthquake dataset. The analysis evaluates each model's performance based on key metrics: Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), and the coefficient of determination (R²). The Random Forest model demonstrated superior performance, achieving the lowest MAE (0.217051), lowest RMSE (0.322398), and highest R² (0.574261), indicating its robustness in capturing complex, non-linear relationships in seismic data. SVM also showed strong performance, with competitive accuracy and robustness. Decision Tree and Neural Network models, while useful, had comparatively higher error rates and lower R² values. The study highlights the potential of ensemble learning and kernel methods in enhancing earthquake magnitude prediction accuracy. Practical implications of the findings include the integration of these models into early warning systems, urban planning, and the insurance industry for better risk assessment and management. Despite the promising results, the study acknowledges limitations such as reliance on historical data and the computational intensity of certain models. Future research is suggested to explore additional data sources, advanced machine learning techniques, and more efficient algorithms to further improve predictive capabilities. By providing a comprehensive evaluation of these models, this research contributes valuable insights into the effectiveness of various machine learning techniques for earthquake prediction, guiding future efforts to develop more accurate and reliable predictive models.
Co-Authors Aisy, Afra Rihadatul Andrianto Andrianto Berlilana Berlilana Fandy Setyo Utomo Hermanto, Aldy Agil Ibrahim, Farrel Imam Tahyudin Kafilla, Princess Iqlima Nurmalitasari, Gupita Pambudi, Rahmat Purwadi Purwadi Putri, Dewi Kartika Rujianto Eko Saputro Taqwa Hariguna Turino, Turino Wahid, Arif Mu'amar Yusmedi Nurfaizal