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, 2026)
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.