Water Quality Monitoring and Control System in Koi Fish Cultivation Based on Internet-of-Things (IoT)

This research develops an Internet-of-Things (IoT)-based system for real-time monitoring and automatic control of water quality in koi fish farming, addressing the lack of knowledge regarding optimal water conditions. The system integrates sensors for pH, ammonia, temperature, total dissolved solids (TDS), and turbidity, along with controllers such as filters, coolers, and heaters, all managed through a mobile application called “AquaKoi.” System testing is divided into IoT device testing and mobile application testing. IoT device testing ensures proper sensor and controller functionality, with sensor data verified against water quality standards. Application testing includes black box testing, quality-of-service (QoS) measurement, user acceptance test (UAT), and notification warning testing, showing a user satisfaction rate of 92%. The test results indicate that the system functions well and meets specifications, despite challenges like overheating of the ESP32 microcontroller, which was mitigated with a temporary fan solution. Overall, the AquaKoi system demonstrates significant potential in enhancing the efficiency of koi fish farming. However, further development is recommended to address technical constraints, improve the user interface, and expand the system’s capabilities to meet more diverse user needs.

Authors

Publication Details

Journal
Jurnal Rekayasa Elektrika
Published
2025-03-29
DOI
https://doi.org/10.17529/jre.v21i1.40760
Citations
1
Primary Topic
Water Quality Monitoring Technologies
Type
article
Field-Weighted Citation Impact
0.82
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Water Quality Monitoring and Control System in Koi Fish Cultivation Based on Internet-of-Things (IoT)

M. Ferry Irawan, Nurwulan Fitriyanti, Alaa Ahmad, Rendy Munadi et al.
1 citations
Jurnal Rekayasa Elektrika
Water Quality Monitoring Technologies
0.82
article

Water Quality Monitoring and Control System in Koi Fish Cultivation Based on Internet-of-Things (IoT)

M. Ferry Irawan, Nurwulan Fitriyanti, Alaa Ahmad, Rendy Munadi, Naili Ikrimah M.Yanis
article en
1 citations

Abstract

This research develops an Internet-of-Things (IoT)-based system for real-time monitoring and automatic control of water quality in koi fish farming, addressing the lack of knowledge regarding optimal water conditions. The system integrates sensors for pH, ammonia, temperature, total dissolved solids (TDS), and turbidity, along with controllers such as filters, coolers, and heaters, all managed through a mobile application called “AquaKoi.” System testing is divided into IoT device testing and mobile application testing. IoT device testing ensures proper sensor and controller functionality, with sensor data verified against water quality standards. Application testing includes black box testing, quality-of-service (QoS) measurement, user acceptance test (UAT), and notification warning testing, showing a user satisfaction rate of 92%. The test results indicate that the system functions well and meets specifications, despite challenges like overheating of the ESP32 microcontroller, which was mitigated with a temporary fan solution. Overall, the AquaKoi system demonstrates significant potential in enhancing the efficiency of koi fish farming. However, further development is recommended to address technical constraints, improve the user interface, and expand the system’s capabilities to meet more diverse user needs.

Jurnal Rekayasa ElektrikaVol. 21(1)
Openalex Percentile: Top 27%
Water Quality Monitoring Technologies
0.82
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.