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All Journal JOURNAL OF APPLIED INFORMATICS AND COMPUTING
Rendi Rendi
Department of Informatics Engineering, Politeknik Negeri Indramayu
Computer Science & IT

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AQUARA: Development of an AI-Assisted Integrated Web and Mobile Digital Platform for Fisheries Information Services and Aquaculture Business Analysis Nur Budi Nugraha; Rendi Rendi; Fauzan Ishlakhuddin
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.v10i4.13695

Abstract

Fish farmers in Indramayu Regency face difficulties in accessing fisheries information, evaluating aquaculture business feasibility, and communicating with the local fisheries authority because existing digital services are fragmented across multiple platforms. This study aims to develop AQUARA, an integrated web and mobile based platform that combines fisheries information, government services, aquaculture business analysis, discussion forums, a fish farmer directory, and AI assisted digital consultation. The platform was developed using a Research and Development (R&D) approach consisting of requirement analysis, system design, platform development, testing, and evaluation. System functionality was verified through Black Box Testing, non functional aspects were examined through performance, security, and compatibility testing, and usability was evaluated using the System Usability Scale (SUS). The testing results confirmed that all implemented features operated according to the specified requirements, the platform demonstrated acceptable response time, role based access control, and encrypted authentication, and it achieved a SUS score of 82.7 (Grade A), indicating excellent usability and high user acceptance. The integration of automated business feasibility analysis and the Dr. Fisho AI computer vision based preliminary fish disease detection feature within a single platform provides a practical solution for supporting the digital transformation of the aquaculture sector. The proposed platform improves information accessibility, supports business decision making, and strengthens collaboration among fish farmers and fisheries stakeholders.
Benchmarking Random Forest, Support Vector Machine, and XGBoost for Flood Risk Classification Using a Synthetic Dataset: A Case Study of Indramayu Regency Nur Budi Nugraha; Rendi Rendi; Yaqutina Marjani Santosa
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.v10i4.13708

Abstract

Flood risk assessment plays a critical role in disaster mitigation planning, yet flood classification studies in Indonesia have largely relied on a single machine learning algorithm without systematically evaluating alternative classifiers under identical experimental conditions. This study benchmarks Random Forest (RF), Support Vector Machine (SVM), and XGBoost for three-class flood risk classification (Low, Medium, and High) in Indramayu Regency, Indonesia, using a literature-informed synthetic dataset of 4,500 samples generated from seven flood-related features. To ensure a fair comparison, all models were trained and evaluated under an identical preprocessing, hyperparameter optimization, and validation framework, with Logistic Regression and Decision Tree included as baseline classifiers. Experimental results show that SVM achieved the highest predictive performance with an accuracy of 88.56% and a macro F1-score of 86.83%, followed closely by XGBoost (88.44% accuracy, 86.76% macro F1), while RF obtained 85.00% accuracy and an 83.57% macro F1-score. Statistical significance testing confirmed that SVM and XGBoost significantly outperformed RF, whereas no significant difference was observed between SVM and XGBoost. Feature importance analysis consistently identified rainfall and river distance as the two most influential predictors across all models. Although SVM provided the strongest overall classification performance, RF demonstrated competitive predictive capability with better generalization characteristics than XGBoost, supporting its suitability for operational flood mitigation decision-support systems where model interpretability and robustness are important. Because the benchmark is based on a synthetic dataset, further validation using real observational flood data is recommended before operational deployment.
Co-Authors Fauzan Ishlakhuddin Nur Budi Nugraha Yaqutina Marjani Santosa