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All Journal Jurnas Nasional Teknologi dan Sistem Informasi Annual Research Seminar Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) RABIT: Jurnal Teknologi dan Sistem Informasi Univrab PROCESSOR Jurnal Ilmiah Sistem Informasi, Teknologi Informasi dan Sistem Komputer Malcom: Indonesian Journal of Machine Learning and Computer Science Jurnal Informatika dan Rekayasa Komputer Jurnal Manajemen Teknologi dan Sistem Informasi Jurnal Manajemen Sistem Informasi PROCEEDING OF INTERNATIONAL CONFERENCE ON BUSINESS MANAGEMENT AND ACCOUNTING Jurnal Pengabdian Masyarakat Unama Prosiding Seminar Nasional Ilmu Teknik
Dodo Zaenal Abidin
Universitas Dinamika Bangsa
Humanities Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Social Sciences

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Analisis Sentimen Ulasan Aplikasi CapCut Berbasis IndoBERT dengan Validasi Silang Tasya Nurdin; Dodo Zaenal Abidin; Kurniabudi Kurniabudi
Prosiding Seminar Nasional Ilmu Teknik Vol. 2 No. 2 (2025): Desember: Prosiding Seminar Nasional Ilmu Teknik
Publisher : Asosiasi Riset Ilmu Teknik Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61132/prosemnasproit.v2i2.157

Abstract

This study conducts sentiment analysis of Indonesian user reviews of the CapCut application using IndoBERT and compares two evaluation schemes: a single 80/20 train–test split and stratified 5-fold cross-validation (k=5). A total of 1,048,575 reviews were collected from the Google Play Store through web scraping and labeled into three sentiment classes based on rating: negative (1–2), neutral (3), and positive (4–5). After preprocessing—cleaning, case folding, banned-word removal, normalization—and duplicate removal, 517,962 reviews were retained. IndoBERT Base P1 was fine-tuned using fixed hyperparameters (batch size 32, learning rate 2e-5, up to 4 epochs, early stopping patience 2), while undersampling was applied to the training set to address class imbalance. Performance was assessed using accuracy, precision, recall, F1-score, and ROC-AUC, supported by confusion matrix and ROC-curve visualizations. The single split achieved an accuracy of 0.756, whereas cross-validation produced a mean accuracy of 0.740. Across both schemes, the positive class achieved the best performance (F1-score 0.850; ROC-AUC 0.918–0.919), while the neutral class remained the most challenging (precision 0.198–0.206; F1-score 0.280–0.283). Overall, cross-validation is recommended for reporting because it reduces dependence on a single partition and provides a more representative estimate across multiple splits.
Model Prediksi Pelunasan Haji Berbasis XGBoost Dengan Interpretasi Shap: Studi Prediksi Pelunasan Haji dengan XGBoost dan SHAP di Provinsi Jambi Yan Apriadi; Dodo Zaenal Abidin; Jasmir Jasmir
Prosiding Seminar Nasional Ilmu Teknik Vol. 2 No. 2 (2025): Desember: Prosiding Seminar Nasional Ilmu Teknik
Publisher : Asosiasi Riset Ilmu Teknik Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61132/prosemnasproit.v2i2.178

Abstract

This study develops an interpretable machine learning model to predict the settlement status of Hajj fees in Jambi Province, Indonesia. Utilizing the XGBoost algorithm on a dataset of 4,332 prospective pilgrims from 2025, the research addresses the critical challenge of class imbalance where only 28.5% of samples are labeled "Unsettled". The baseline XGBoost model achieved a ROC-AUC of 0.7778, with a recall of 0.3482 for the minority class. SHAP (SHapley Additive exPlanations) analysis was employed to interpret model predictions, revealing that financial features specifically NILAI_VA (Virtual Account Value), JML_SETORAN (Deposit Amount), and JML_PELUNASAN (Settlement Amount) are the most significant factors influencing repayment risk, with negative SHAP values indicating increased default probability. The findings demonstrate that an interpretable XGBoost framework can provide both predictive accuracy and actionable insights for policymakers, enabling targeted interventions such as flexible payment schemes and enhanced financial monitoring for high-risk pilgrims..
Social Feature Integration for Entertainment Hoax Detection: Machine Learning and DistilBERT-Fusion: Dodo Zaenal Abidin; Agus Siswanto; Chindra Saputra; Bhetantio Bhetantio
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 10 No 4 (2026): August 2026 (in progress)
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/resti.v10i4.7208

Abstract

Detecting entertainment hoaxes remains a pressing challenge in the social media era, where short texts, provocative headlines, and rapid dissemination complicate verification. This study compares the effectiveness of classical machine learning models and the transformer DistilBERT, both with and without the integration of social features. The modified GossipCop dataset, consisting of 22,140 entries, includes news titles as the primary text representation and social features such as tweet count, tweet density, and viral indicators. Text was represented using TF-IDF for classical models and DistilBERT tokenization for the transformer, with performance evaluated under stratified 10-fold cross-validation. Results show that incorporating social features consistently improves classical models, with XGBoost + Social achieving the best performance (PR-AUC 0.84; F1-score 0.76), surpassing DistilBERT-Fusion (PR-AUC 0.79). McNemar’s test confirmed significant differences in error distributions, strengthening the reliability of these findings. To reduce reliance on a single empirical dataset, the social feature integration pipeline was additionally validated on the PolitiFact benchmark from a different domain, where incorporating minimal social signals yielded measurable and statistically significant gains over text-only models (Wilcoxon p = 0.0137), confirming the robustness of the social feature effect beyond the entertainment domain. Overall, the results highlight that for short-text entertainment news under the conditions examined in this study, boosting models enriched with social signals can outperform transformer-based approaches. While DistilBERT-Fusion provided competitive results, its improvement over text-only DistilBERT remained limited due to the short-text nature of the dataset, indicating opportunities for richer fusion strategies in future research.
Co-Authors Agus Siswanto Ami Wulandari, Cinthia An Nisa Ziah Putri Asep Sujarwadi Athallah, Ibni Faiq Athallah Az-zahra', Nur Amanah Beni Purnama Bhetantio Bhetantio Chindra Saputra Dona Primayu Ekarsih Effiyaldi Errissya Rasywir Feranika, Ayu Firmansyah, Azi Habibi Ul Akbar Hastuti, Meidya Hendryzal Jasmir Jasmir Jasmir, Jasmir Joni Devitra Kurniabudi, Kurniabudi Li Sensia Rahmawati M. Ade. Maulana M. Dimas Ramadhan M. Ichwan Athallah M. Zahran Yudha Meisak, Despita mikhail ibrahim Muhammad Hendrik Koto Muhammad Kevin Perdi Hasan Ngadimin NURHADI Nurhadi Nurhadi Nurhayati Oklatifa, Susi Pareza Alam Jusia Peri Sandiko Saputra Purnama, Benni Reza Firsandaya Malik Risky Radison Nasution ROBY SETIAWAN Rohaini, Eni ronal naibaho Rosita Dewi Setiawan Assegaf Sharipuddin Sharipuddin, Sharipuddin Siti Nurmaini Suci Ramadhani Tasya Nurdin Yan Apriadi Yossinomita Zelvia Krisna Senjani