Microcontroller-Based Water Quality Monitoring System Implementation
DOI:
10.47709/brilliance.v2i2.1544Keywords:
Internet of things, microcontroller, turbidityDimension Badge Record
Abstract
So far, Regional Drinking Water Companies (PDAMs) have used conventional methods by taking water samples, measuring all water quality parameters, and analyzing them one by one. In addition, the process of making conclusions on water quality has not been integrated so that it can cause misclassification of water quality and prolong the work. In this study, an expert system was designed to monitor water quality that works in real time so that it can be accessed anytime and anywhere. The water quality analysis process is carried out with a fuzzy classifier realized using Arduino Mega 2560. The fuzzy input variables include the pH value, total dissolved solids (TDS), and turbidity or turbidity. A fuzzy inference system is used to classify water quality into three classes, namely good (meets quality standards), ordinary, and bad (polluted). The expert system of success provides inference results with a 100% success percentage. The results of monitoring and water quality classification can be accessed online using the Internet of Things (IoT) ThingSpeak platform
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References
Mohhamad H.H., Zaini, & Haditya.F., “Sistem Monitoring Kunci Pintu Ruangan Menggunakan Modul Wifi”, Jurnal Universitas Muhammadiyah Jakarta, 2017.
Agistian.N, Arif.S.,& Herfia.R, “Aplikasi Pendeteksi Kualitas Air Menggunakan Turbidity Sensor dan Arduino Berbasis Web Mobile”, Jurnal Core IT, 5(1), hlm. 13-18 , 2019.
Wadu,R.A, Ada,Y.S.B., & Panggalo.I.U, “Rancang Bangun Sistem Sirkulasi Air Pada Akuarium/Bak Ikan Air Tawar Berdasarkan Kekeruhan Air Secara Otomatis”, Jurnal Ilmiah Flash, 3(1), hlm.1-10, 2017.
Muammarul.I., Esa.Apriaskar, Djuaniadi, “Pengendalian Suhu Air Menggunakan Sensor Suhu DS18B20”, Journal of Engineering and Sustainable Technology, 6(1), hlm.347-352, 2019.
Candra, R. A., Suherman, & Ilham, D. N. (2021). Amplitude range data validation in Internet of Things (IoT) sensor. IOP Conference Series: Earth and Environmental Science, 644(1). https://doi.org/10.1088/1755-1315/644/1/012063
Ilham, D. N., Satria, E., Anugreni, F., Candra, R. A., & Kusumo, H. N. R. A. (2021). Rain Monitoring System for Nutmeg Drying Based on Internet of Things. Journal of Computer Networks, Architecture, and High-Performance Computing, 3(1), 52–57. https://doi.org/10.47709/cnahpc.v3i1.933
Nursila, N., Ilham, D. N., Yunan, A., Harahap, M. K., & Candra, R. A. (2021). Prototype of IoT-Based Fruit Alcohol Level Measurement Tool. Brilliance: Research of Artificial Intelligence, 1(1), 8–13. https://doi.org/10.47709/brilliance.v1i1.1078
Purba, Dina Stefani., Pangaribuan, Porman., & Wibowo, Agung Surya.”Pengendalian Suhu Air Berdasarkan Durasi Pemanasan Menggunakan Fuzzy Logic Dan Pi Kontroler”. eProceedings of Engineering 5.3, pp. 4011-4017, 2018.
Iqbal, Muhamad., Pangaribuan, Porman., & Wibowo, Agung Surya. “Perancangan dan Implementasi Alat Pengendali Suhu Air Berbasis Mikrokontroler”. eProceedings of Engineering 4.1, hlm. 53-59, 2017.
R. Pramana, “Perancangan Sistem Kontrol dan Monitoring Kualitas Air dan Suhu Air Pada Kolam Budidaya Ikan,” J. Sustain. J. Has. Penelit. dan Ind. Terap., vol. 7, no. 1, pp. 13–23, 2018, doi: 10.31629/sustainable.v7i1.435.
L. Parra, J. Rocher, J. Escrivá, and J. Lloret, “Design and development of low cost smart turbidity sensor for water quality monitoring in fish farms,” Aquac. Eng., vol. 81, pp. 10–18, 2018.
M. J. Villaseñor-Aguilar et al., “Fuzzy Classification of the Maturity of the Tomato Using a Vision System,” J. Sensors, vol. 2019, pp. 1-12, 2019, doi: 10.1155/2019/3175848.
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Accepted Date: 2022-06-06
Published Date: 2022-06-07
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