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All Journal JOURNAL OF APPLIED INFORMATICS AND COMPUTING Adimas : Jurnal Pengabdian Kepada Masyarakat JE (Journal of Empowerment) International Journal of Advances in Data and Information Systems Abdimasku : Jurnal Pengabdian Masyarakat Journal of Computing Theories and Applications Journal of Multiscale Materials Informatics Journal of Future Artificial Intelligence and Technologies
Herowati, Wise
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Agriculture, Biological Sciences & Forestry Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Computer Science & IT Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Health Professions Industrial & Manufacturing Engineering Materials Science & Nanotechnology Social Sciences

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Journal : JOURNAL OF APPLIED INFORMATICS AND COMPUTING

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Utilization of Machine Learning for Predicting Corrosion Inhibition by Quinoxaline Compounds Fadil, Muhamad; Akrom, Muhamad; Herowati, Wise
Journal of Applied Informatics and Computing Vol. 9 No. 1 (2025): February 2025
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v9i1.8894

Abstract

Corrosion is a significant issue in both industrial and academic sectors, with widespread negative impacts on various aspects, including economics and safety. To address this problem, the use of corrosion inhibitors has proven effective. This study explores the application of Machine Learning (ML) methods based on Quantitative Structure-Properties Relationship (QSPR) to develop a predictive model for the efficiency of quinoxaline compounds as corrosion inhibitors. By conducting a comparative analysis among three algorithms: AdaBoost Regressor (ADB), Gradient Boosting Regressor (GBR), and Extreme Gradient Boosting Regressor (XGBR), and optimizing parameters through hyperparameter tuning using Grid Search and Random Search, this research demonstrates that the XGBR model yields the most superior prediction results. The XGBR optimized with hyperparameter tuning using Grid Search achieved the highest R² value of 0.970 and showed the lowest RMSE, MSE, MAD, and MAPE values of 0.368, 0.135, 0.119, and 0.273, respectively, indicating high predictive accuracy. These results are expected to contribute to the development of more effective methods for identifying corrosion inhibitor candidates.
Enhancing Aspect-Based Sentiment Analysis via Hugging Face Fine-Tuned IndoBERT Aprilah, Thania; Setiadi, De Rosal Ignatius Moses; Herowati, Wise
Journal of Applied Informatics and Computing Vol. 9 No. 6 (2025): December 2025
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v9i6.11409

Abstract

Aspect-Based Sentiment Analysis (ABSA) on hotel reviews faces significant challenges regarding semantic complexity and severe class imbalance, particularly in low-resource languages like Indonesian. This study evaluates the effectiveness of fine-tuning IndoBERT, a pre-trained Transformer model, to address these issues by benchmarking it against classical statistical methods (TF-IDF) and static embeddings (Sentence-BERT). Utilizing the HoASA dataset, the experiment implements a Random Oversampling strategy at the text level to mitigate data sparsity in minority classes. Empirical results demonstrate that the fine-tuned IndoBERT significantly outperforms baselines on the majority of aspects, achieving a global accuracy of 97% and macro F1-score of 0.92. Granular per-aspect analysis reveals that the model’s self-attention mechanism captures linguistic context robustly in tangible aspects (e.g., wifi, service), yet faces persistent challenges in highly ambiguous aspects such as smell (bau) and general. Statistical significance tests (Paired t-test and Wilcoxon) confirm that the performance gains over baselines are statistically significant (p < 0.05) and not due to random chance. The study concludes that leveraging contextual representations from IndoBERT, combined with data balancing strategies, offers a superior and statistically robust solution for handling linguistic variations and class bias in the Indonesian hospitality domain.
Co-Authors Achmad Wahid Kurniawan Akrom, Muhamad Aprilah, Thania Ayu Pertiwi Budi, Setyo De Rosal Ignatius Moses Setiadi Dutta, Pushan Kumar Hanny Haryanto Harisa, Ardiawan Bagus Harun Al Azies Hidayat, Novianto Nur Ignatius Moses Setiadi, De Rosal Indra Gamayanto Islam, Hussain Md Mehedul Kusuma, Muh Galuh Surya Putra Minh T. Nguyen, Minh T. Moch Anjas Aprihartha Muhamad Fadil Muljono Muljono Prabowo, Wahyu Aji Eko Prajanto Wahyu Adi, Prajanto Wahyu Safitri, Aprilyani Nur Sasono Wibowo Sendi Novianto Supriadi Rustad T. Sutojo Trisnapradika, Gustina Alfa Wibawa, Tangkas Surya Zami, Farrikh Al