Detecting Dehydration Based on Urine Color Using Fuzzy Logic Image Processing and Regulating Water Intake with an Automatic Water Pump According to Dehydration Level Using an IoT-Based

Authors

  • Denny Trias Utomo Politeknik Negeri Jember
  • Adi Heru Utomo Politeknik Negeri Jember
  • Zora Olivia Politeknik Negeri Jember
  • Nita Maria Politeknik Negeri Jember
  • Nilla Putri Rosidania Politeknik Negeri Jember

DOI:

https://doi.org/10.47134/ijhis.v1i3.32

Keywords:

dehydration, IoT system, image processing

Abstract

Dehydration is a condition where the body lacks the fluids it needs to carry out its functions optimally. Dehydration can cause various health problems, including decreased mental and physical performance, and can even cause death if not treated immediately. Therefore, it is important to be able to detect and treat dehydration early. One way to detect dehydration is through urine color analysis. Urine that is darker than normal can be a sign of dehydration. The classification of dehydration level according to urine color is as follows: 1-2: Hydrated, 3-4: Mildly dehydrated, 5-6: Dehydrated, 7-8: Very dehydrated. This research aims to develop an IoT-based dehydration detection system that can detect the level of dehydration in a person based on urine color and regulate water intake automatically using a water pump.  The novelty of this research is the method of integrating drinking water intake with dehydration detection based on real-time urine color based on IoT using the Fuzzy Logic method. The results of this research are used by the Jember State Polytechnic TeFa Nutrition Care Center (NCC) in serving patients. The methodology used in this research is Fuzzy Logic image processing to process urine color data and determine a person's level of dehydration. After carrying out this research, the following conclusions were obtained: Based on the literature study in this research, 8 levels of hydration status according to NSW Health were obtained, then from this literature a method was obtained to measure a person's hydration based on urine color using image processing using the Fuzzy Logic method.

References

P. W. Nastiti, N. Bintoro, J. N. W. Karyadi, and S. Rahayoe, ‘Kinetics Study of Chicken Breast Meat (Pectoralis major) Color Changes Measured Using the TCS 3200 Color Sensor during Storage at Room Temperature’, Key Eng. Mater., vol. 928, pp. 103–110, Sep. 2022, doi: 10.4028/p-w152c0.

J. Lacey, J. Corbett, L. Forni, L. Hooper, F. Hughes, and ..., ‘A multidisciplinary consensus on dehydration: definitions, diagnostic methods and clinical implications’, Ann. …, 2019, doi: 10.1080/07853890.2019.1628352.

K. B. Jayasekara et al., ‘Relevance of heat stress and dehydration to chronic kidney disease (CKDu) in Sri Lanka’, Prev. Med. Rep., vol. 15, p. 100928, 2019, doi: https://doi.org/10.1016/j.pmedr.2019.100928.

P. N. Utami, V. Arinal, and D. I. Mulyana, ‘Klasifikasi Dehidrasi Tubuh Manusia Berdasarkan Citra RGB Pada Warna Urine Menggunakan Metode K-Nearest Neighbor’, J. MEDIA Inform. BUDIDARMA, vol. 6, no. 1, pp. 18–26, 2022.

R. Maulana, M. R. Caesardi, and E. Setiawan, ‘Klasifikasi Tingkat Dehidrasi Berdasarkan Kondisi Urine, Denyut Jantung dan Laju Pernapasan’, J. Teknol. Inf. Dan Ilmu Komput. JTIIK, vol. 8, no. 2, 2021, Accessed: Sep. 30, 2023. [Online]. Available: https://scholar.archive.org/work/whewr474tvdazmzmu334c5sfsu/access/wayback/https://jtiik.ub.ac.id/index.php/jtiik/article/download/4379/pdf

Z. Zulfachmi, A. F. Syahputra, B. I. Prasetyo, and A. E. Shafira, ‘Klasifikasi Tingkat Dehidrasi Berdasarkan Warna Urin Menggunakan Metode KNN’, J. Bangkit Indones., vol. 12, no. 1, pp. 43–48, 2023.

D. K. Sutiari, L. S. Zulfadlih, and M. Ilham, ‘Sosialisasi dan Pengenalan Pendeteksi Dehidrasi Melalui Warna Urin di Puskesmas Andoolo Utama’, J. Pengabdi. Saintek Mandala Waluya, vol. 1, no. 2, pp. 74–79, 2021.

N.- Sutarna, ‘Sistem Pendeteksi Keasaman dan Warna Urine sebagai Indikasi Dini Dehidrasi’, ELECTRICES, vol. 2, no. 2, pp. 57–61, 2021, doi: 10.32722/ees.v2i2.3570.

A. Kazemian, X. Yuan, O. Davtalab, and B. Khoshnevis, ‘Computer vision for real-time extrusion quality monitoring and control in robotic construction’, Autom. Constr., vol. 101, pp. 92–98, 2019, doi: https://doi.org/10.1016/j.autcon.2019.01.022.

I. Kurniastuti, E. N. I. Yuliati, F. Yudianto, and T. D. Wulan, ‘Determination of Hue Saturation Value (HSV) color feature in kidney histology image’, J. Phys. Conf. Ser., vol. 2157, no. 1, p. 012020, Jan. 2022, doi: 10.1088/1742-6596/2157/1/012020.

Y. T. J. Samodra, ‘Pengaruh dehidrasi (kehilangan) cairan 2.8% terhadap prestasi lari 400 meter’, J. Sport. J. Penelit. Pembelajaran, vol. 6, no. 2, pp. 526–540, 2020, doi: 10.29407/js_unpgri.v6i2.14484.

A. Di Mauro, A. Cominola, A. Castelletti, and A. Di Nardo, ‘Urban Water Consumption at Multiple Spatial and Temporal Scales. A Review of Existing Datasets’, Water, vol. 13, no. 1, p. 36, Dec. 2020, doi: 10.3390/w13010036.

B. Büyükkaragöz and S. A. Bakkaloğlu, ‘Serum osmolality and hyperosmolar states’, Pediatr. Nephrol., vol. 38, no. 4, pp. 1013–1025, Apr. 2023, doi: 10.1007/s00467-022-05668-1.

A. Tootee, E. N. Esfahani, and B. Larijani, ‘Diabetes management during Ramadan amid Covid-19 pandemic’, DARU J. Pharm. Sci., vol. 28, no. 2, pp. 795–798, Dec. 2020, doi: 10.1007/s40199-020-00357-6.

R. KEMENKES, ‘Berapa takaran normal air agar tidak kekurangan cairan dalam tubuh ?’ Sep. 2018. Accessed: May 02, 2023. [Online]. Available: https://p2ptm.kemkes.go.id/preview/infografhic/berapa-takaran-normal-air-agar-tidak-kekurangan-cairan-dalam-tubuh

F. Salehi, H. R. Kamran, and K. Goharpour, ‘Effects of ultrasound time, xanthan gum, and sucrose levels on the osmosis dehydration and appearance characteristics of grapefruit slices: Process optimization using response surface methodology’, Ultrason. Sonochem., vol. 98, p. 106505, 2023, doi: https://doi.org/10.1016/j.ultsonch.2023.106505.

V. Ojha, A. Abraham, and V. Snášel, ‘Heuristic design of fuzzy inference systems: A review of three decades of research’, Eng. Appl. Artif. Intell., vol. 85, pp. 845–864, Oct. 2019, doi: 10.1016/j.engappai.2019.08.010.

M. M. P. Aditya, S. Sumardi, and S. B. Utomo, ‘Perancangan Sistem Pendeteksi Dehidrasi Dan Gangguan Hati Berbasis Fuzzy’, MALCOM Indones. J. Mach. Learn. Comput. Sci., vol. 1, no. 1, pp. 65–72, 2021, doi: 10.57152/malcom.v1i1.80.

‘Urine colour chart - Beat the heat’. Accessed: Oct. 11, 2023. [Online]. Available: https://www.health.nsw.gov.au/environment/beattheheat/Pages/urine-colour-chart.aspx

Z. Yan et al., ‘From Newtonian to non-Newtonian fluid: Insight into the impact of rheological characteristics on mineral deposition in urine collection and transportation’, Sci. Total Environ., vol. 823, p. 153532, 2022, doi: https://doi.org/10.1016/j.scitotenv.2022.153532.

A. Putra and M. Andriani, ‘Systematic Literature Review: Media Video Blog (Vlog) pada Pembelajaran Matematika’, Alauddin Journal of Mathematics Education, vol. 3, no. 1. Universitas Islam Negeri Alauddin Makassar, pp. 111–111, 2021. doi: 10.24252/ajme.v3i1.17528.

V. Santi, I. A. Bangsa, and L. Nurpulaela, ‘Implementasi Kendali Logika Fuzzy sebagai Penentu Tingkat Dehidrasi pada Sistem Monitoring Pencegahan Risiko Kadar Glukosa Darah’, Power Elektron. J. Orang Elektro, vol. 11, no. 1, pp. 123–126, 2022.

A. S. De Oliveira Góes and R. C. L. De Oliveira, ‘A Process for Human Resource Performance Evaluation Using Computational Intelligence: An Approach Using a Combination of Rule-Based Classifiers and Supervised Learning Algorithms’, IEEE Access, vol. 8, pp. 39403–39419, 2020, doi: 10.1109/ACCESS.2020.2975485.

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Published

2024-01-16

How to Cite

Utomo, D. T., Utomo, A. H., Olivia, Z., Maria, N., & Rosidania, N. P. (2024). Detecting Dehydration Based on Urine Color Using Fuzzy Logic Image Processing and Regulating Water Intake with an Automatic Water Pump According to Dehydration Level Using an IoT-Based. International Journal of Health and Information System, 1(3), 152–164. https://doi.org/10.47134/ijhis.v1i3.32

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