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All Journal JOIV : International Journal on Informatics Visualization GUYUB: Journal of Community Engagement
Suasono, Zaikhul Sulthon
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Optimalisasi Kualitas Air pada Tambak Udang Vannamei Menggunakan Modul IoT Gunawan, Agus Indra; Setiawardhana, Setiawardhana; Gunawan, M Wisnu; Alam, Daffa Syah; Suasono, Zaikhul Sulthon; Hamida, Silfiana Nur
GUYUB: Journal of Community Engagement Vol 6, No 1 (2025): Maret
Publisher : Universitas Nurul Jadid

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33650/guyub.v6i1.10581

Abstract

Indonesian has great potential in the fisheries sector, with vaname shrimp as a leading commodity due to its competitive price and efficient cultivation. However, many shrimp farmers in Keputih Village, Surabaya City still lack an understanding of the importance of monitoring and managing pond water quality. In response to this, the Master of Applied Electrical Engineering and Master of Applied Informatics and Computer Engineering teams at Politeknik Elektronika Negeri Surabaya (PENS) introduced an IoT-based Water Quality Meter module. This program not only provides real-time water quality monitoring technology that can be accessed via smartphone or laptop, but also provides training and assistance to pond farmers in adopting this technology. Evaluation results show that pond farmers can operate the module well to monitor water quality parameters, making it easier to monitor ponds accurately and practically. The community service program is expected to increase yields, strengthen collaboration between academics and communities, and encourage the adoption of modern technology in shrimp farming.
Cloud Computing-based Shrimp Pond Water Quality Prediction Intelligent Service System Suasono, Zaikhul Sulthon; Setiawardhana, Setiawardhana; Winarno, Idris; Gunawan, Agus Indra
JOIV : International Journal on Informatics Visualization Vol 9, No 4 (2025)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62527/joiv.9.4.2862

Abstract

Maintaining water quality is an essential factor in the success of shrimp farming, particularly in conventional and semi-intensive methods in Indonesian. Poor water quality will affect shrimp's survival, reproduction, development, and harvest yield. In order to furnish data regarding future water quality conditions, This research aims to create an intelligent cloud-based water quality prediction system for shrimp ponds that can provide accurate predictions regarding future water quality conditions. The system utilizes the WQI dataset gathered from four different shrimp farming sites, totaling 408 samples, each location exhibiting a different set of values. The model will be trained using four parameters: pH, DO, salinity, and temperature. The WQI dataset will be pre-processed to address missing data, outliers, and standardization. The water quality prediction model uses three machine learning algorithms: SVM, ANN, and MLR. The model's performance results are evaluated using MAE, RMSE, and R². The results indicate that the ANN model is the most effective, achieving an MAE: 0.4023, RMSE: 0.5336, and R²: 0.7178 for temperature predictions, and an MAE: 0.4080, RMSE: 0.5942, and R²: 0.5997 for salinity. The SVM model had mixed results for temperature, with an MAE: 0.3645 and RMSE: 0.4823, but it performed poorly for DO, as evidenced by a negative R² of -0.2428. The MLR model provided reasonable temperature predictions MAE: 0.4953, RMSE: 0.6370, R²: 0.5602. Subsequent research endeavors should prioritize the augmentation of the dataset size and the incorporation of temporal dimensions in order to enhance the precision of predictive outcomes.
Co-Authors Alam, Daffa Syah Gunawan, Agus Indra Gunawan, M Wisnu Hamida, Silfiana Nur Idris Winarno Setiawardhana, Setiawardhana