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This Author published in this journals
All Journal International Journal of Informatics and Communication Technology (IJ-ICT) IJIIS: International Journal of Informatics and Information Systems International Journal for Applied Information Management Journal of Digital Market and Digital Currency Journal of Current Research in Blockchain International Journal Research on Metaverse
Sugianto, Dwi
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Humanities Computer Science & IT Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Environmental Science Social Sciences

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Journal : International Journal for Applied Information Management

 1 
Uncovering the Efficiency of Phishing Detection: An In-depth Comparative Examination of Classification Algorithms Sugianto, Dwi; Putawa, Rilliandi Arindra; Izumi, Calvina; Ghaffar, Soeltan Abdul
International Journal for Applied Information Management Vol. 4 No. 1 (2024): Regular Issue: April 2024
Publisher : Bright Institute

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/ijaim.v4i1.72

Abstract

This research aims to investigate the potential security risks associated with phishing email attacks and compare the performance of three main classification algorithms: random forest, SVM, and a combination of k-fold cross-validation with the xgboost model. The dataset consists of 18,634 emails, with 7,312 identified as phishing emails and 11,322 considered safe. Through experiments, the combination of k-fold cross-validation and xgboost demonstrated the best performance with the highest accuracy of 0.9712828770799785. The email classification graph provides a visual insight into the distribution of classification results, aiding in understanding patterns and trends in phishing attack detection. The analysis of the ROC curve results indicates that k-fold cross-validation and xgboost have a higher AUC compared to random forest and SVM, signifying a better ability to predict the correct class. The conclusion emphasizes the importance of the combination of k-fold cross-validation and xgboost in enhancing email security, with the potential for increased accuracy through parameter adjustments.
Classifying Vehicle Categories Based on Technical Specifications Using Random Forest and SMOTE for Data Augmentation Sugianto, Dwi; Wahyuningsih, Tri
International Journal for Applied Information Management Vol. 5 No. 4 (2025): Regular Issue: December 2025
Publisher : Bright Institute

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/ijaim.v5i4.113

Abstract

This study investigates the application of machine learning for classifying vehicles based on their technical specifications using the Random Forest algorithm. The objective was to create a robust classification model capable of categorizing vehicles into six distinct classes: Hybrid, SUV, Sedan, Sports, Truck, and Wagon. The analysis was conducted using a comprehensive dataset that included features such as engine size, horsepower, weight, and fuel efficiency, along with the target variable, vehicle class. To address the issue of class imbalance, the Synthetic Minority Over-sampling Technique (SMOTE) was applied to balance the training data. The results showed that the model performed particularly well in classifying Sedans, achieving a perfect recall and high F1-score, while struggling with underrepresented classes like Hybrid and Wagon. Despite applying SMOTE, the model’s performance for minority classes remained suboptimal, highlighting the challenges associated with highly imbalanced datasets. The study contributes to the field of vehicle classification by demonstrating the use of Random Forest for such tasks and providing insights into the challenges posed by imbalanced class distributions. The findings underscore the importance of feature selection, especially regarding numerical attributes such as horsepower and engine size, in improving classification accuracy. However, the study also identified limitations, including potential biases in the dataset and the difficulty in improving performance for minority vehicle classes. Future research should explore alternative algorithms like XGBoost or deep learning models, and consider expanding the dataset to include more diverse vehicle types. The practical implications of this work are significant for vehicle market segmentation, offering valuable insights for manufacturers, dealerships, and analysts seeking to optimize vehicle classification and improve market targeting strategies.
Co-Authors Ghaffar, Soeltan Abdul Hananto, Andhika Rafi Izumi, Calvina Javadi, Milad Pratama, Satrya Fajri Putawa, Rilliandi Arindra Sarmini Tri Wahyuningsih Yulianto, Koko Edy