Article Per Year (5 Year)
p-Index From 2019 - 2024
0.408
P-Index
This Author published in this journals
All Journal Urecol Journal. Part E: Engineering
Nurhayat Nurhayat
Jakarta Technical University of Fisheries, Indonesia
Chemical Engineering, Chemistry & Bioengineering Civil Engineering, Building, Construction & Architecture Control & Systems Engineering Electrical & Electronics Engineering Engineering Industrial & Manufacturing Engineering Mechanical Engineering

Published : 2 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 2 Documents
Search

 1 
Automatic Water Quality Monitoring System With Real-Time Data Type Based on Internet of Things (IOT) for Vannamei Shrimp Farming Sobri Sobri; Pungkas Prayitno; Basino Basino; Nurhayat Nurhayat
Urecol Journal. Part E: Engineering Vol. 1 No. 2 (2021): August-Dec
Publisher : Konsorsium LPPM Perguruan Tinggi Muhammadiyah 'Aisyiyah (PTMA) Koordinator Wilayah Jawa Tengah - DIY

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (664.311 KB) | DOI: 10.53017/uje.64

Abstract

Water quality is an important factor in supporting the success of vanname shrimp farming. The automatic water quality monitoring system is designed using two hardware, namely the DS18B290 temperature sensor to measure water temperature, the E-201 BNC pH meter kit sensor to measure water pH, and the TDS meter sensor. gravity Dfrobot to measure the salinity of the water, and the RED Turbidity MJKDZ sensor to measure the turbidity of the water, as the data sending hardware (transmitter). Arduino Mega2560 microcontroller, NodeMCU esp8266 as receiver and data processing hardware (reciver). The automatic water quality monitoring system is also designed using three software, namely Arduino IDE software as the main hardware program, ThingSpeak software as a database and MIT APP Inventor software as application development software. The test results for the level of accuracy and error of sensor measurements on water quality monitoring tools show that the sensor has a very good percentage of performance with the temperature sensor accuracy reaching 98.67%, the sensor reaching pH 99.35%, the salinity sensor reaching 99.28%. Meanwhile, the temperature sensor measurement error rate is only 1.32%, the pH sensor is only 0.64% and the salinity sensor is only 0.71%.
Automatic Water Quality Monitoring System With Real-Time Data Type Based on Internet of Things (IOT) for Vannamei Shrimp Farming Sobri Sobri; Pungkas Prayitno; Basino Basino; Nurhayat Nurhayat
Urecol Journal. Part E: Engineering Vol. 1 No. 2 (2021): August-Dec
Publisher : Konsorsium LPPM Perguruan Tinggi Muhammadiyah 'Aisyiyah (PTMA) Koordinator Wilayah Jawa Tengah - DIY

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.53017/uje.64

Abstract

Water quality is an important factor in supporting the success of vanname shrimp farming. The automatic water quality monitoring system is designed using two hardware, namely the DS18B290 temperature sensor to measure water temperature, the E-201 BNC pH meter kit sensor to measure water pH, and the TDS meter sensor. gravity Dfrobot to measure the salinity of the water, and the RED Turbidity MJKDZ sensor to measure the turbidity of the water, as the data sending hardware (transmitter). Arduino Mega2560 microcontroller, NodeMCU esp8266 as receiver and data processing hardware (reciver). The automatic water quality monitoring system is also designed using three software, namely Arduino IDE software as the main hardware program, ThingSpeak software as a database and MIT APP Inventor software as application development software. The test results for the level of accuracy and error of sensor measurements on water quality monitoring tools show that the sensor has a very good percentage of performance with the temperature sensor accuracy reaching 98.67%, the sensor reaching pH 99.35%, the salinity sensor reaching 99.28%. Meanwhile, the temperature sensor measurement error rate is only 1.32%, the pH sensor is only 0.64% and the salinity sensor is only 0.71%.
Co-Authors Basino, Basino Pungkas Prayitno Sobri Sobri