Article Per Year (5 Year)
p-Index From 2021 - 2026
2.552
P-Index
This Author published in this journals
All Journal Jurnal Sains dan Seni ITS JOURNAL OF APPLIED INFORMATICS AND COMPUTING Jurnal Onoma: Pendidikan, Bahasa, dan Sastra bit-Tech Indonesian Journal of Data and Science Jurnal Lebesgue : Jurnal Ilmiah Pendidikan Matematika, Matematika dan Statistika Nusantara Science and Technology Proceedings Djtechno: Jurnal Teknologi Informasi Journal of Renewable Energy, Electrical, and Computer Engineering Journal of Artificial Intelligence and Digital Business SAINTEK Jurnal PETISI (Pendidikan Teknologi Informasi)
Aviolla Terza Damaliana
Data Science Department, Faculty Of Computer Science, Universitas Pembangunan Nasional “Veteran” Jawa Timur, Surabaya 60294, Indonesia
Agriculture, Biological Sciences & Forestry Arts Humanities Automotive Engineering Chemical Engineering, Chemistry & Bioengineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Materials Science & Nanotechnology Mathematics Medicine & Pharmacology

Published : 18 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 3 Documents
Search
Journal : journal of applied informatics and computing

 1 
Bahasa Inggris Nasywa Azzah Nabila; Aviolla Terza Damaliana; Shindi Shella May Wara
Journal of Applied Informatics and Computing Vol. 10 No. 3 (2026): June 2026
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v10i3.12734

Abstract

Floods are among the most frequent natural disasters in Indonesia, with thousands of events causing significant impacts on infrastructure damage and human lives. The substantial increase in the number of victims and flood-related damages in 2024 indicates that flood disaster mitigation efforts in Indonesia remain suboptimal. Consequently, a clustering-based analytical approach is required to understand patterns of flood impact across provinces. This study aims to cluster provinces in Indonesia based on flood-affected indicators using the Hierarchical Density-Based Spatial Clustering of Applications with Noise (HDBSCAN) method with Bayesian Optimization to obtain optimal hyperparameters. This study comprises several stages, including data collection, data standardization, statistical test, data reduction, hyperparameter optimization, HDBSCAN algorithm, model evaluation, and analysis of clustering results. The results show that HDBSCAN with Bayesian Optimization yields a well-separated cluster structure with a DBCV value of 0.515. The clustering results consist of three primary clusters and one noise cluster. Cluster 0 (High Displacement & Inundation) consisting of 5 provinces, cluster 1 (High Fatality & Structural Damage) consisting of 4 provinces, cluster 2 (Low Impact) consisting of 21 provinces, and the noise cluster consisting of 8 provinces. These findings are intended to provide a foundation for the government to formulate targeted flood mitigation strategies tailored to the flood impact characteristics of each province.
Indonesian Cyberbullying Detection Using IndoBERTweet-BiGRU Model on Class-Imbalanced X (Twitter) Data Fajria Ulumin Nafiah; Aviolla Terza Damaliana; Kartika Maulida Hindrayani
Journal of Applied Informatics and Computing Vol. 10 No. 4 (2026): August 2026
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v10i4.13686

Abstract

Cyberbullying on social media platforms, particularly X (formerly Twitter), has become a serious issue that negatively affects users' mental health and well-being. Automatic cyberbullying detection in Indonesian remains challenging due to the widespread use of informal language, slang, abbreviations, and highly imbalanced class distributions. This study proposes a hybrid deep learning model that integrates IndoBERTweet with a Bidirectional Gated Recurrent Unit (BiGRU) to improve cyberbullying detection performance on Indonesian tweets. A dataset of Indonesian tweets was collected from X and annotated using a multi-stage dual large language model (LLM) labeling strategy to reduce the time and effort required for manual annotation while maintaining label consistency. To address class imbalance, this study investigates the effectiveness of Focal Loss and label distribution modification through multiple experimental scenarios. The proposed approach was evaluated using accuracy, precision, recall, and F1-score. The best performance was achieved by combining Focal Loss with a modified four-class label configuration consisting of Rude and Vulgar Words, Sexual Harassment, Body Shaming and Hate Speech, and Non-Cyberbullying. This configuration obtained an accuracy of 0.93, precision of 0.90, recall of 0.90, and F1-score of 0.90. These findings demonstrate that integrating contextual language representations with sequential modeling, supported by an efficient LLM-assisted labeling strategy and class imbalance handling, provides an effective approach for Indonesian cyberbullying detection and offers a practical solution for large-scale social media content moderation.
Implementation of a Hybrid TabNet–XGBoost Model Based on Radiosonde Data for Predicting Daily Rainfall Intensity in Surabaya Annabel Gracia Puryani; Aviolla Terza Damaliana; Alfan Rizaldy Pratama
Journal of Applied Informatics and Computing Vol. 10 No. 4 (2026): August 2026
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v10i4.13767

Abstract

Rainfall prediction plays an important role in supporting hydrometeorological disaster mitigation and weather-related decision-making. However, accurate rainfall prediction remains challenging because atmospheric processes are highly nonlinear and governed by complex interactions among multiple meteorological variables. This study proposes a Hybrid TabNet–XGBoost model for daily rainfall prediction using integrated radiosonde and surface meteorological observations collected at the BMKG Juanda Class I Meteorological Station. The dataset covers the period from 2019 to 2025 and consists of 2,551 daily observations. TabNet was employed to select the fifteen most informative atmospheric variables based on feature importance, while temporal dependencies were incorporated through lag features generated using Autocorrelation Function (ACF) and Partial Autocorrelation Function (PACF) analyses. Hyperparameter optimization was performed using Optuna with TimeSeriesSplit cross-validation prior to model training. Experimental results on the testing dataset achieved an RMSE of 19.1347 mm, an MAE of 11.9742 mm, a MAPE of 17.62%, and an R² of 0.0449. The proposed model was able to capture the general temporal pattern of daily rainfall and produced satisfactory predictions under the dominant rainfall conditions represented in the dataset. However, the model exhibited reduced sensitivity to high-intensity rainfall events, resulting in the underestimation of extreme rainfall and a relatively low R² value, primarily due to the imbalanced rainfall distribution and the complexity of rainfall processes. The optimized model was subsequently applied to generate daily rainfall projections for 2026 based on historical atmospheric observations. Since the corresponding observational data were unavailable at the time of this study, these projections should be interpreted as model-based forecasts rather than validated prediction results. Overall, the proposed Hybrid TabNet–XGBoost framework demonstrates the potential of integrating radiosonde and surface meteorological observations for daily rainfall prediction while highlighting the need for additional atmospheric and spatial information to improve the prediction of extreme rainfall events.
Co-Authors Abdul Rezha Efrat Najaf Adinda Rusdianti Maulani Putri Alfan Rizaldy Pratama Alyssa Amorita Azzah Anas Ahmadi Annabel Gracia Puryani Brescia Ayundina Yuniarossy Citra Amelia Intan Permadani Dea Putri Pascha Febriyanti Dina Magdalena Manurung Dwi Arman Prasetya Fajria Ulumin Nafiah Ilmatus Sa’diyah Irma Amanda Putri Kartika Maulida Hindrayani M Naswan Izzudin Akmal Mohamad Ibnu Fajar Maulana Mohammad Idhom Mohammad Idhom Muhammad Idhom Muhammad Muharrom Al Haromainy Nabila Rizky Amalia Putri Nasywa Azzah Nabila Permatasari, Reisa Reza Sadiya Purwadwika Rizki Amanda Sandria Amelia Putri Selena Nurmanina Afandy Setiawan Setiawan Shindi Shella May Wara Trimono Trimono Trimono, Trimono Wahyu Syaifullah J. S. Wahyu Syaifullah Jauharis Saputra