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All Journal JURNAL MEDIA INFORMATIKA BUDIDARMA PROCESSOR Jurnal Ilmiah Sistem Informasi, Teknologi Informasi dan Sistem Komputer Jurnal Ilmiah Media Sisfo Building of Informatics, Technology and Science SKANIKA: Sistem Komputer dan Teknik Informatika Jurnal Teknik Informatika (JUTIF) Innovative: Journal Of Social Science Research Jurnal Informatika dan Rekayasa Komputer Jurnal Manajemen Teknologi dan Sistem Informasi Jurnal Manajemen Sistem Informasi The Indonesian Journal of Computer Science Journal of Law and Legal Reform Jurnal Pengabdian Masyarakat Unama Jurnal Asosiasi Pengajar Hukum Tata Negara Hukum Administrasi Negara
Afrizal Nehemia Toscany
Universitas Dinamika Bangsa
Humanities Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Law, Crime, Criminology & Criminal Justice Public Health Other

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Journal : Jurnal Teknik Informatika (JUTIF)

 1 
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.
Optimized RoBERTa–DeBERTa Ensemble for Multi-Class Sentiment Analysis on Highly Imbalanced Data Sika, Xaverius; Kisbianty, Desi; Istoningtyas, Marrylinteri; Abidin, Dodo Zaenal; Toscany, Afrizal Nehemia
Jurnal Teknik Informatika (Jutif) Vol. 7 No. 2 (2026): JUTIF Volume 7, Number 2, April 2026
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jutif.2026.7.2.5350

Abstract

Multi-class sentiment analysis on highly imbalanced datasets poses substantial challenges for achieving accurate and equitable classification, particularly when neutral sentiments are considerably underrepresented. This study evaluates four fine-tuned transformer models—Bidirectional Encoder Representations from Transformers (BERT), DistilBERT, RoBERTa, and DeBERTa—using a real-world Amazon review dataset comprising over 20,000 user-generated texts. Sentiment labels were derived from star ratings through a standardized mapping scheme. Experimental results show that while BERT achieved the highest overall accuracy (93%), its performance on the minority Neutral class remained limited (F1-score: 0.36). DeBERTa improved Neutral recall to 0.59 but with a slightly lower overall accuracy of 91%. To address this imbalance, two ensemble strategies were explored: a fixed-weight soft voting scheme and an optimized-weight ensemble combining RoBERTa and DeBERTa. The optimized RoBERTa–DeBERTa ensemble yielded the most balanced performance, achieving a Neutral-class F1-score of 0.57 while maintaining 91% overall accuracy. ROC and PR curve analyses further indicate superior sensitivity–precision balance for this optimized ensemble. The findings indicate that adaptive ensemble weighting can substantially enhance minority-class detection under severe imbalance. This study provides a clear methodological contribution by demonstrating the effectiveness of targeted ensemble optimization and offers practical guidance for developing more balanced and reliable sentiment classification systems.
Co-Authors Abd. Rasyid Syamsuri Abdul Rahim Abdul Rahim abdurrohman maulana Abidin, Dodo Zaenal Agus Nugroho Agus Siswanto Ali Sadikin Ali Sadikin Almustaqim, Andry Amir, Diana Arahmad Taupiq Arjuna Panji Arjuna Panji Prakarsa Beni Irawan Betantiyo , Betantiyo Bustasmi, M.Irwan Cahyana Putra Pratama Chindra Saputra Desi Kisbianty, Desi Dodo Zainal Abidin Eko Nuriyatman Elita Rahmi Fitria Fitria Husaein, Ahmad Irawan Irawan, Beni Istoningtyas , Marrylinteri Istoningtyas, Marrylinteri Janu Hadi Susilo Jasmir Jumersyah Pratama, Raka KARMAN, ZULFI Kisbianty , Desi Kurniabudi Lasmedi Afuan M Irwan Bustami M. Irwan Bustasmi M.Irwan M.Irwan Bustasmi Mhd Ridho Saputra Muhammad Ramadhan Saputra MUHAMMAD SURYA Nugroho, M. Restu Nurhadi Nurmala Viani Dwi Rahayu Pareza Alam Jusia Pratama Putra, Rifqi Renaldi Yulvianda ROBY SETIAWAN Rts. Fanny Inayah Rusdianto Roestam Setiawan Assegaf SIKA, XAVERIUS Silvi Febrianti Sutoyo, Mochammad Arief Hermawan taupiq, Arahmad Teguh Yuwono Wahyu Nugraha Xaverius Sika Xaverius Sika YOSE, IMELDA Yovi Pratama