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All Journal ILKOMNIKA: Journal of Computer Science and Applied Informatics Journal of Advanced in Information and Industrial Technology (JAIIT)
Agvenia, Keisya
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Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Electrical & Electronics Engineering Industrial & Manufacturing Engineering

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IoT-Based Water Quality Monitoring System to Enhance Sustainability and Business Performance in Koi Fish Cultivation Sugiarto; Nugraha, Isna; Fahrudin, Tresna Maulana; Rizqina, Azza; Agvenia, Keisya
Journal of Advances in Information and Industrial Technology Vol. 7 No. 2 (2025): Nov
Publisher : LPPM Telkom University Surabaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52435/jaiit.v7i2.730

Abstract

Water quality is a critical factor that determines the survival and productivity of koi fish cultivation. Fluctuations in key parameters, such as pH, dissolved oxygen (DO), total dissolved solids (TDS), and turbidity, can induce stress and lead to mass fish mortality, resulting in substantial financial losses for farmers. This study proposes an IoT-based water quality monitoring system designed to enhance both environmental sustainability and business performance in koi aquaculture. The system integrates four sensors (pH, DO, TDS, and turbidity) connected to an ESP32 microcontroller, which transmits real-time data via Wi-Fi to cloud platforms (Firebase and Blynk). A dedicated dashboard provides continuous monitoring, historical trend visualization, and real-time alerts when parameter thresholds are exceeded. The prototype was validated in an operational koi pond and achieved an average accuracy of 96.5%. User testing involving 10 koi farmers showed an 89% satisfaction rate, demonstrating the system's practicality and usability. Economically, the solution reduced manual monitoring costs by 40%, water replacement volume by 25%, and increased fish survival rates by 12%. These results indicate that IoT implementation in aquaculture not only improves environmental control but also increases operational efficiency and overall profitability, contributing to sustainable, data-driven aquaculture practices.
Integrating IoT Data and Consumer Behavior Analytics to Enhance Decision-Making in Sustainable Koi Fish Cultivation Sugiarto, Sugiarto; Wahyuni, A; Nugraha, Isna; Rizqina, Azza; Agvenia, Keisya
ILKOMNIKA Vol 8 No 1 (2026): Volume 8, Number 1, April 2026
Publisher : Lembaga Penelitian dan Pengabdian Masyarakat

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28926/ilkomnika.v8i1.832

Abstract

This study presents a data-driven decision-support framework that integrates Internet of Things (IoT)–based water quality monitoring with consumer behavior analytics to support sustainable koi fish cultivation. An IoT monitoring system was implemented using an ESP32 microcontroller equipped with pH, dissolved oxygen (DO), temperature, and turbidity sensors to continuously record water quality conditions over a 30-hour observation period. Time-series sensor data were processed through noise filtering, timestamp synchronization, and descriptive statistical analysis to characterize environmental stability patterns. In parallel, consumer behavior data were collected from 50 respondents using an online questionnaire addressing color preference, purchase considerations, maintenance awareness, and price sensitivity. The integrated analysis combined correlation analysis and K-Means clustering to explore relationships between water quality stability indicators and consumer segmentation. The results indicate that relatively stable pH (6.66–7.20) and DO (6.0–7.1 mg/L) conditions align with the preferences of quality-focused and maintenance-oriented consumer groups, while automated IoT-based monitoring supports operational efficiency relevant to budget-conscious buyers. Overall, the findings demonstrate that integrating environmental sensing data with consumer behavior analytics can enhance operational decision-making, improve market alignment, and support sustainability in koi aquaculture systems.
Co-Authors Fahrudin, Tresna Maulana Nugraha, Isna Rizqina, Azza Sugiarto Sugiarto S Wahyuni, A