Rancang Bangun Sistem Pakar Pemantau Kualitas Air Berbasis IoT Menggunakan Fuzzy Classifier

Muhammad Hisyamudin Ramadhan(1), Gunawan Dewantoro(2), Fransiscus Dalu Setiaji(3),

DOI: https://doi.org/10.15294/jte.v12i2.25351


(1) Universitas Kristen Satya Wacana
(2) Universitas Kristen Satya Wacana
(3) Universitas Kristen Satya Wacana

Abstract

The classification of water quality is vital to ensure that the water has been properly utilized. As of today, the water treatment plant employs a conventional method by taking water sample, measuring all of water quality parameters, and analyzing each sample. Besides, the conclusion-drawing processes have not been incorporated which might lead to water quality misclassification and prolonged efforts. In this study, an expert system was developed to monitor the water quality in real time fashion, therefore it could be accessed anytime and anywhere. The water quality analysis process was conducted by means of fuzzy classifier, and implemented on Arduino Mega 2560 board. The fuzzy inputs included pH value, total dissolved solid (TDS), and turbidity. A fuzzy inference system was employed to classify the water quality into three classes, namely good (meet the hygiene standards), fair, and poor (polluted). The expert system successfully yielded the inference results with a success rate of 100%. The water quality monitoring and classification could be accessed online through Internet of Things (IoT) platform Thingspeak.

Keywords

water quality; fuzzy classifier; pH; TDS; turbidity

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References

Peraturan Menteri Kesehatan Republik Indonesia No. 32 tahun 2017 tentang Standar Baku Mutu Kesehatan Lingkungan dan Persyaratan Kesehatan Air untuk Keperluan Higiene Sanitasi, Kolam Renang, Solus Per Aqua, dan Pemanian Umum., 2017.

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, doi: 10.1016/j.aquaeng.2018.01.004.

F. Amani and K. Prawiroredjo, “Alat ukur kualitas air minum dengan parameter pH, suhu, tingkat kekeruhan, dan jumlah padatan terlarut,†J. JETRi, vol. 14, no. 1, pp. 49–62, 2016, doi: 10.25105/jetri.v14i1.821.

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.

H. P. Luo, G. L. Li, W. F. Peng, J. Song, and Q. W. Bai, “Real-time remote monitoring system for aquaculture water quality,†Int. J. Agric. Biol. Eng., vol. 8, no. 6, pp. 136–143, 2015, doi: 10.3965/j.ijabe.20150806.1486.

N. A. Cloete, R. Malekian, and L. Nair, “Design of Smart Sensors for Real-Time Water Quality Monitoring,†IEEE Access, vol. 4, pp. 3975–3990, 2016, doi: 10.1109/ACCESS.2016.2592958.

Y. Xuecun, Z. Chuanqi, K. Linghong, and H. Zhixin, “Design of Fishpond Water Quality Monitoring and Control System Based on ZigBee,†J. Appl. Sci. Eng. Innov., vol. 2, no. 10, pp. 385–388, 2015.

K. S. Adu-Manu, C. Tapparello, W. Heinzelman, F. A. Katsriku, and J. D. Abdulai, “Water quality monitoring using wireless sensor networks: Current trends and future research directions,†ACM Trans. Sens. Networks, vol. 13, no. 1, 2017, doi: 10.1145/3005719.

D. S. Simbeye and S. F. Yang, “Water quality monitoring and control for aquaculture based on wireless sensor networks,†J. Networks, vol. 9, no. 4, pp. 840–849, 2014, doi: 10.4304/jnw.9.4.840-849.

M. Pule, A. Yahya, and J. Chuma, “Wireless sensor networks: A survey on monitoring water quality,†J. Appl. Res. Technol., vol. 15, no. 6, pp. 562–570, 2017, doi: 10.1016/j.jart.2017.07.004.

S. Bhattacharya, S. Chatterjee, and A. De, “GSM Based Water Quality Monitoring and Notification System,†Int. J. Emerg. Trends Electron. Commun. Eng., vol. 4, no. 1, pp. 1–7, 2020.

E. Ragavan, C. Hariharan, N. Aravindraj, and S. S. Manivannan, “Real time water quality monitoring system,†Int. J. Pharm. Technol., vol. 8, no. 4, pp. 26199–26205, 2016, doi: 10.15680/ijircce.2015.0306016.

B. Das and P. C. Jain, “Real-time water quality monitoring system using Internet of Things,†2017 Int. Conf. Comput. Commun. Electron. COMPTELIX 2017, pp. 78–82, 2017, doi: 10.1109/COMPTELIX.2017.8003942.

F. Wang, L. Hu, J. Zhou, and K. Zhao, “A survey from the perspective of evolutionary process in the internet of things,†Int. J. Distrib. Sens. Networks, vol. 11, no. 3, pp. 1-9, 2015, doi: 10.1155/2015/462752.

M. S. Bennet Praba, N. Rengaswamy, Vishal, and O. Deepak, “IoT Based Smart Water System,†Proc. 3rd Int. Conf. Commun. Electron. Syst. ICCES 2018, pp. 1041–1045, 2018, doi: 10.1109/CESYS.2018.8723969.

M. Mukta, S. Islam, S. Das Barman, A. W. Reza, and M. S. H. Khan, “Iot based Smart Water Quality Monitoring System,†2019 IEEE 4th Int. Conf. Comput. Commun. Syst., pp. 669–673, 2019, doi: 10.1109/CCOMS.2019.8821742.

R. Martínez, N. Vela, A. el Aatik, E. Murray, P. Roche, and J. M. Navarro, “On the use of an IoT integrated system for water quality monitoring and management in wastewater treatment plants,†Water (Switzerland), vol. 12, no. 4, pp. 1-21, 2020, doi: 10.3390/W12041096.

M. Parameswari and M. B. Moses, “Efficient analysis of water quality measurement reporting system using IOT based system in WSN,†Cluster Comput., vol. 22, pp. 12193-12201, 2019, doi: 10.1007/s10586-017-1581-1.

L. García, L. Parra, J. M. Jimenez, J. Lloret, and P. Lorenz, “IoT-based smart irrigation systems: An overview on the recent trends on sensors and IoT systems for irrigation in precision agriculture,†Sensors (Switzerland), vol. 20, no. 4, pp. 1-48, 2020, doi: 10.3390/s20041042.

G. Gao, K. Xiao, and M. Chen, “An intelligent IoT-based control and traceability system to forecast and maintain water quality in freshwater fish farms,†Comput. Electron. Agric., vol. 166, pp. 1-10, 2019, doi: 10.1016/j.compag.2019.105013.

P. Damor and K. J. Sharma, “IoT based Water Monitoring System: A Review,†Int. J. Adv. Eng. Res. Dev., vol. 4, no. 6, 2017.

J. Wang, H. Yue, and Z. Zhou, “An improved traceability system for food quality assurance and evaluation based on fuzzy classification and neural network,†Food Control, vol. 79, pp. 363-370, 2017, doi: 10.1016/j.foodcont.2017.04.013.

O. El Aissaoui, Y. El Alami El Madani, L. Oughdir, and Y. El Allioui, “A fuzzy classification approach for learning style prediction based on web mining technique in e-learning environments,†Educ. Inf. Technol., vol. 24, no. 3, pp. 1943-1959, 2019, doi: 10.1007/s10639-018-9820-5.

K. Liu, J. Wang, X. Tao, X. Zeng, P. Bruniaux, and E. Kamalha, “Fuzzy classification of young women’s lower body based on anthropometric measurement,†Int. J. Ind. Ergon., vol. 55, pp. 60–68, 2016, doi: 10.1016/j.ergon.2016.07.008.

S. A. Mokeddem, “A fuzzy classification model for myocardial infarction risk assessment,†Appl. Intell., vol. 4, pp. 1233-1250, 2018, doi: 10.1007/s10489-017-1102-1.

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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