Metode Peningkatan Akurasi pada Sensor TDS Berbasis Arduino untuk Nutrisi Air Menggunakan Regresi Linier
Abstract
Water quality has an important role in the field of aquaculture. One of the factors that determine water quality is the level of TDS (Total Disolved Solid). Therefore, a TDS meter that has precise accuracy is needed to be able to accurately measure the quality of various types of water. In this study developed a prototype capable of measuring TDS levels in water. This prototype consists of a TDS sensor device, Arduino UNO and an LCD to display the results of the measured water quality readings. So that the accuracy read by the prototype is able to match the commercial TDS meter, the researchers used a linear regression algorithm to be included in the Arduino TDS program. The results of the experiment show that the accuracy of the TDS prototype which was originally 77% increased to 98.3%, is almost close to precision with commercial TDS meters in general.
References
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[3] M. K. Ganeshan, “New Agriculture Technology in Modern Farming,” no. September, 2021, doi: 10.30726/ijmrss/v8.i3.2021.83016.
[4] R. S. R. Mayavan, R. Jeganath, and V. Chamundeeswari, “Automated Hydroponic System for Deep Water Culture To Grow Tomato Using Atmega328,” no. 8, pp. 27–32, 2017.
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[6] Z. Shareef, “Design and wireless sensor Network Analysis of Water Quality Monitoring System for Aquaculture,” 2019 3rd Int. Conf. Comput. Methodol. Commun., no. Iccmc, pp. 405–408, 2019.
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[8] R. A. Koestoer, Y. A. Saleh, I. Roihan, and Harinaldi, “A simple method for calibration of temperature sensor DS18B20 waterproof in oil bath based on Arduino data acquisition system,” AIP Conf. Proc., vol. 2062, 2019, doi: 10.1063/1.5086553.
[9] S. K. Verma, M. Rajesh, and R. Vincent, “Smart-farming using internet of things,” J. Comput. Theor. Nanosci., vol. 17, no. 1, pp. 172–176, 2020, doi: 10.1166/jctn.2020.8646.
[10] D. Pant, A. Bhatt, M. Khan, O. P. Nautiyal, and P. Adhikari, “Automated IoT based Smart Water Quality Assessment System,” Proc. 2019 8th Int. Conf. Syst. Model. Adv. Res. Trends, SMART 2019, pp. 98–104, 2020, doi: 10.1109/SMART46866.2019.9117271.
[11] A. G. Menon and P. Menon, “Automated Water Quality Monitoring IOT System for Small-scale Aquaculture Farms,” J. Comput. Eng. Inf. Technol., vol. 9, no. 5, pp. 3–8, 2020, doi: 10.37532/jceit.2020.9(3).239.
[12] M. Badura, P. Batog, A. Drzeniecka-Osiadacz, and P. Modzel, “Regression methods in the calibration of low-cost sensors for ambient particulate matter measurements,” SN Appl. Sci., vol. 1, no. 6, pp. 1–11, 2019, doi: 10.1007/s42452-019-0630-1.
[13] S. Hayder and B. H. Khudair, “Water Quality Assessment and Total Dissolved Solids Prediction,” no. December, 2019.
[14] T. Maharani, M. A. Zainuddin, and S. Sukaridhoto, “Brightness on LEDs for Light Fidelity Applications and Measurements,” 2019 Int. Electron. Symp., pp. 53–57, 2019.
[15] M. Zainuddin, L. Politeknik, and N. Batam, “Desain dan Uji Coba Sederhana Pada Obstacle Avoiding Robot Menggunakan Mikrokontroler Arduino,” Jagi, vol. 2, no. 1, p. 1, 2018, [Online]. Available: http://jurnal.polibatam.ac.id/index.php/JAGI/announcement/view/14Viewproject.
[16] L. Hakim and B. Manurung, “Design and analytical simulation of heart rate measurement and human body temperature with linear regression approach,” AIP Conf. Proc., vol. 2221, no. March, 2020, doi: 10.1063/5.0003189.
Published
2022-04-14
How to Cite
TISNA, Dhodit Rengga et al.
Metode Peningkatan Akurasi pada Sensor TDS Berbasis Arduino untuk Nutrisi Air Menggunakan Regresi Linier.
JURNAL INTEGRASI, [S.l.], v. 14, n. 1, p. 61-68, apr. 2022.
ISSN 2548-9828.
Available at: <http://704209.wb34atkl.asia/index.php/JI/article/view/3906>. Date accessed: 28 nov. 2024.
doi: https://doi.org/10.30871/ji.v14i1.3906.
Section
Articles
