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All Journal Bulletin of Electrical Engineering and Informatics Jurnal Ilmiah Teknik Elektro Komputer dan Informatika (JITEKI) Jurnal Teknologi Informasi dan Ilmu Komputer Telematika Bianglala Informatika : Jurnal Komputer dan Informatika Akademi Bina Sarana Informatika Yogyakarta Jutisi: Jurnal Ilmiah Teknik Informatika dan Sistem Informasi Journal of Electronics, Electromedical Engineering, and Medical Informatics International Journal of Advances in Data and Information Systems Jurnal Teknik Informatika (JUTIF) International Journal of Electronics and Communications Systems Journal of Computing Theories and Applications Jurnal Informatika Polinema (JIP) Scientific Journal of Informatics Indonesian Journal of Electronics, Electromedical Engineering, and Medical Informatics
Setyo Wahyu Saputro
Lambung Mangkurat University
Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Education Electrical & Electronics Engineering Engineering Health Professions Library & Information Science Materials Science & Nanotechnology

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Quantifying the Impact of Text Preprocessing on IndoBERT Fine-Tuning for Indonesian Informal Culinary Sentiment Analysis Rahmat Budianoor; Setyo Wahyu Saputro; Friska Abadi; Radityo Adi Nugroho; Andi Farmadi
Journal of Computing Theories and Applications Vol. 3 No. 4 (2026): JCTA 3(4) 2026
Publisher : Universitas Dian Nuswantoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62411/jcta.15980

Abstract

Indonesian culinary comments on social media platforms such as Instagram are characterized by informal spelling, regional language mixing, slang expressions, and emojis, posing substantial challenges for automated sentiment classification. While IndoBERT has demonstrated strong performance across Indonesian natural language processing tasks, the contribution of individual preprocessing components to fine-tuning performance on informal text remains underexplored, particularly in the culinary domain. This study addresses this gap by conducting a systematic preprocessing ablation study on IndoBERT-Base fine-tuning for Indonesian culinary sentiment classification, accompanied by a comparative evaluation against Naive Bayes with TF-IDF, SVM with TF-IDF, and BiLSTM as representative baselines. A dataset of 3,500 manually labeled Instagram culinary comments across three sentiment classes was used, with a stratified 80/10/10 split. Six preprocessing variants were evaluated under identical experimental conditions to isolate the contribution of each component. The results show that slang normalization is the most impactful single preprocessing step, yielding a macro F1-score gain of +0.0609 over the no-preprocessing baseline, while the full pipeline achieves an accuracy of 0.8800 and a macro F1-score of 0.8465. IndoBERT-Base with the full pipeline outperforms all baselines across all evaluation metrics. Per-class analysis reveals that the negative class achieves the lowest F1-score of 0.7600, with sarcastic expressions and Banjar regional vocabulary identified as primary sources of misclassification. These findings indicate that preprocessing decisions have a measurable and non-uniform effect on IndoBERT fine-tuning performance. In this study, slang normalization provides the most substantial individual contribution in bridging the vocabulary gap between informal user-generated text and the model’s pre-training distribution.
Metaheuristic-Based Hyperparameter Optimization Analysis of Deep Neural Network for Cross-Project Defect Prediction in Mobile Applications Abdul Rahman, Maulana; Herteno, Rudy; Adi Nugroho, Radityo; Abadi, Friska; Wahyu Saputro, Setyo
Indonesian Journal of Electronics, Electromedical Engineering, and Medical Informatics Vol. 8 No. 2 (2026): May
Publisher : Jurusan Teknik Elektromedik, Politeknik Kesehatan Kemenkes Surabaya, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35882/ijeeemi.v8i2.340

Abstract

Software Defect Prediction (SDP) plays a strategic role in identifying software defects during the early stages of development, thereby enabling more efficient allocation of testing resources, particularly in the rapidly evolving mobile application domain characterized by fast release cycles. The commonly used Within-Project Defect Prediction (WPDP) approach is often constrained by the limited availability of historical data, especially in projects at early stages of development. As an alternative, Cross-Project Defect Prediction (CPDP) leverages historical data from other projects as training sources. Moreover, the performance of the Deep Neural Network (DNN) used in SDP is highly dependent on accurate hyperparameter configurations, where manual tuning requires substantial time and computational resources without guaranteeing optimal results. To address this issue, this study analyzes and compares the effectiveness of three metaheuristic algorithms, namely Particle Swarm Optimization (PSO), Genetic Algorithm (GA), and Grey Wolf Optimizer (GWO), in optimizing DNN hyperparameters within a CPDP framework. This study utilizes 14 open-source Android mobile application projects and employs the Leave-One-Out Cross-Validation technique. The performance of each combination is evaluated using ROC-AUC as the primary metric. The Wilcoxon Signed-Rank Test with a Bonferroni correction is used to assess the statistical significance of the observed performance differences. The experimental results demonstrate that GWO-DNN achieves the best performance, with an average ROC-AUC of 0.721, and is the only combination that remains statistically significant after Bonferroni correction, with a small effect size based on Cliff’s delta. Overall, the findings of this study indicate that metaheuristic-based hyperparameter tuning is a sufficiently effective approach for improving the capability of DNN in cross-project software defect prediction within the mobile application domain, although the observed improvements remain moderate.
Co-Authors Abadi, Friska Adi, Puput Dani Prasetyo Adini, Muhammad Hifdzi Aflaha, Rahmina Ulfah Akhmad Zakirin Akhtar, Zarif Bin Andi Farmadi Andi Farmadi Angga Maulana Akbar Anshory, Muhammad Naufal As’ary Ramadhan Badali, Rahmat Amin Deni Sutaji Dodon Turianto Nugrahadi Dwi Kartini, Dwi Erick Kurniawan Fadhillah, Muhammad Alif Faisal, Mohammad Reza Fatma Indriani Febrian, Muhamad Michael Friska Abadi Hana, Elvina Nur Hariyady, Hariyady Herteno, Rudy Herteno, Rudy Hertono, Rudy Irwan Budiman Irwan Budiman Kamil, Hawariul Martalisa, Asri Maulana Abdul Rahman Maulidha, Khusnul Rahmi Muhammad Fikri Muhammad Ikhwanul Hakim Muhammad Itqan Mazdadi Muhammad Khalid Maulana Muhammad Nazar Gunawan Muhammad Noor Muhammad Reza Faisal, Muhammad Reza Muliadi Nabella, Putri Nur Hidayatullah, Wildan Putri Nabella Radityo Adi Nugroho Rahmat Budianoor Rahmayanti Rahmayanti Raidra Zeniananto Ramadhan, Muhammad Rizky Aulia Reza Faisal, Mohammad Rezeki, Abdillah Rozaq, Hasri Akbar Awal Rudy Herteno Saragih, Triando Hamonangan Satou, Kenji Siti Aisyah Solechah Suryadi, Mulia Kevin Syahputra, Muhammad Reza Ujang Ruyandi Umiatin, Umiatin Wahyudi Ariannor Wardana, Muhammad Difha YILDIZ, Oktay Yulia Yudihartanti