Article Per Year (5 Year)
p-Index From 2021 - 2026
1.235
P-Index
This Author published in this journals
All Journal Indonesian Journal of Electrical Engineering and Computer Science Jurnal Teknik Informatika (JUTIF) Journal of Applied Business and Technology Jurnal Informatika dan Rekayasa Komputer
Abidin, Dodo Zaenal
Unknown Affiliation
Computer Science & IT Decision Sciences, Operations Research & Management Economics, Econometrics & Finance

Published : 7 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 3 Documents
Search
Journal : Jurnal Teknik Informatika (JUTIF)

 1 
Improving Term Deposit Customer Prediction Using Support Vector Machine with SMOTE and Hyperparameter Tuning in Bank Marketing Campaigns Abidin, Dodo Zaenal; Rosario , Maria; Sadikin , Ali; Nurhadi, Nurhadi; Jasmir, Jasmir
Jurnal Teknik Informatika (Jutif) Vol. 6 No. 3 (2025): JUTIF Volume 6, Number 3, Juni 2025
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jutif.2025.6.3.4585

Abstract

Identifying potential customers for term deposit products remains a challenge in the banking industry due to class imbalance in marketing datasets. This study proposes an integrated approach that combines Support Vector Machine (SVM) with the Synthetic Minority Oversampling Technique (SMOTE) and hyperparameter tuning via GridSearchCV to enhance prediction performance. The dataset comprises 45,211 records containing demographic and campaign-related features. Preprocessing steps include categorical encoding, feature scaling, and SMOTE-based resampling. The optimized SVM model achieves an accuracy of 91% and an AUC of 0.96, outperforming the baseline model and demonstrating strong discriminatory ability, particularly for the minority class. This method improves the balance between precision and recall while reducing bias toward the majority class. The findings confirm the effectiveness of combining SMOTE and SVM for imbalanced classification tasks in the financial domain. These results contribute to the advancement of applied machine learning in informatics, particularly in developing robust decision support systems for data-driven banking strategies. Future work may extend this approach to diverse datasets and explore advanced resampling or ensemble techniques to improve model generalization.
A Comprehensive Benchmarking Pipeline for Transformer-Based Sentiment Analysis using Cross-Validated Metrics Abidin, Dodo Zaenal; Afuan, Lasmedi; Toscany, Afrizal Nehemia; Nurhadi, Nurhadi
Jurnal Teknik Informatika (Jutif) Vol. 6 No. 4 (2025): JUTIF Volume 6, Number 4, Agustus 2025
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jutif.2025.6.4.4894

Abstract

Transformer-based models have significantly advanced sentiment analysis in natural language processing. However, many existing studies still lack robust, cross-validated evaluations and comprehensive performance reporting. This study proposes an integrated benchmarking pipeline for sentiment classification on the IMDb dataset using BERT, RoBERTa, and DistilBERT. The methodology includes systematic preprocessing, stratified 5-fold cross-validation, and aggregate evaluation through confusion matrices, ROC and precision-recall (PR) curves, and multi-metric classification reports. Experimental results demonstrate that all models achieve high accuracy, precision, recall, and F1-score, with RoBERTa leading overall (94.1% mean accuracy and F1), followed by BERT (92.8%) and DistilBERT (92.1%). All models exceed 0.97 in ROC-AUC and PR-AUC, confirming strong discriminative capability. Compared to prior approaches, this pipeline enhances result robustness, interpretability, and reproducibility. The provided results and open-source code offer a reliable reference for future research and practical deployment. This study is limited to the IMDb dataset in English, suggesting future work on multilingual, cross-domain, and explainable AI integration.
Enhancing Fake News Detection on Imbalanced Data Using Resampling Techniques and Classical Machine Learning Models Abidin, Dodo Zaenal; Siswanto, Agus; Saputra, Chindra; Betantiyo , Betantiyo; Nehemia Toscany, Afrizal
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.2025.6.5.5177

Abstract

Class imbalance remains a critical challenge in fake news detection, particularly in domains such as entertainment media where class distributions are highly skewed. This study evaluates seven resampling techniques—Random Oversampling, SMOTE, ADASYN, Random Undersampling, Tomek Links, NearMiss, and No Resampling—applied to three classical machine learning models: Logistic Regression, Support Vector Machine (SVM), and Random Forest. Using the imbalanced GossipCop dataset comprising 24,102 news headlines, the proposed pipeline integrates TF-IDF vectorization, stratified 3-fold cross-validation, and five evaluation metrics: F1-score, precision, recall, ROC AUC, and PR AUC. Experimental results show that oversampling methods, particularly SMOTE and Random Oversampling, substantially improve minority class (fake news) detection. Among all model–resampling combinations, SVM with SMOTE achieved the highest performance (F1-score = 0.67, PR AUC = 0.74), demonstrating its robustness in handling imbalanced short-text classification. Conversely, undersampling methods frequently reduced recall, especially with ensemble models like Random Forest. This approach enhances model robustness in fake news detection on skewed datasets and contributes a reproducible, domain-specific framework for developing more reliable misinformation classifiers.
Co-Authors Afrizal Nehemia Toscany Agus Siswanto Betantiyo , Betantiyo Chindra Saputra Errissya Rasywir Fachruddin Fachruddin Jasmir, Jasmir Lasmedi Afuan Maulana Hidayat Nasutioni, Wahyudi Nurhadi Pajri Rasywir, Errisya Rosario , Maria Sadikin , Ali Sujatmiko, Tri Aji WILLY RIYADI