• Nadine Swastika (1) Indonesia International Institute of Life Sciences
  • Winda Hasuki (2) Indonesia International Institute of Life Sciences
  • Sava Savero (3) Indonesia International Institute of Life Sciences
  • Putri Satya (4) Indonesia International Institute of Life Sciences
  • Arli Aditya Parikesit (5*) Indonesia International Institute for Life Sciences https://orcid.org/0000-0001-8716-3926

  • (*) Corresponding Author

Keywords: Water Intake, Dehydration, Color, Human Excrement


In order to function properly the human body requires adequate hydration due to 70% of the human body being built up by water with lots of chemical reactions involved in order to work optimally throughout the day. This paper presents an idea to make a water intake app based on human excrement surveillance. It might be a solution to intensify people's ability to become self-conscious when drinking less water by surveying the excreted substance. Currently, available software that measures one indicator which is between urine and feces detector on how much water should the user drink. But actually, both excrement indicators are needed to detect users' drinking amount. If one of these indicators shows a bad result, it could lead to water intoxication or hydration. The application was created using Python to give feedback regarding the user's water intake based on the condition of their excreted substance.


Download data is not yet available.

Author Biography

Arli Aditya Parikesit, Indonesia International Institute for Life Sciences

Department of Bioinformatics
School of Life Sciences
Indonesia International Institute for Life Sciences
Jl. Pulomas Barat Kav.88 Jakarta 13210


Archie, M. (2019). Creating a Cliché Library for Social Applications (Massachusetts Institute of Technology). Massachusetts Institute of Technology, Cambridge. Retrieved from https://dspace.mit.edu/handle/1721.1/123001

Aversa, R., Petrescu, V., Apicella, A., & Petrescu, I. T. (2016). The basic elements of life’s. American Journal of Engineering and Applied Sciences, 9(4), 1189–1197. https://doi.org/10.3844/ajeassp.2016.1189.1197

Bernard, S., & Parikesit, A. A. (2020). Artificial Intelligence in Colonoscopy: Improving Medical Diagnostic of Colorectal Cancer. Frontiers in Health Informatics, 9(1), 33. https://doi.org/10.30699/fhi.v9i1.209

Bhat, N., Wijaya, E. B., & Parikesit, A. A. (2019). Use of the “DNAChecker” Algorithm for Improving Bioinformatics Research. Makara Journal of Technology, 23(2), 72. https://doi.org/10.7454/mst.v23i2.3488

Bičanić, I., Hladnik, A., Džaja, D., & Petanjek, Z. (2019). The anatomy of orofacial innervation. Acta Clinica Croatica, 58(Supplement 1), 35–42. https://doi.org/10.20471/acc.2019.58.s1.05

Broom, A. (2005). Virtually healthy: The impact of internet use on disease experience and the doctor-patient relationship. Qualitative Health Research, 15(3), 325–345. https://doi.org/10.1177/1049732304272916

Carmichael, A. (2011). Initial treatment of dehydration for severe acute malnutrition.

Cheuvront, S. N., Muñoz, C. X., & Kenefick, R. W. (2016). The void in using urine concentration to assess population fluid intake adequacy or hydration status 1,2. The American Journal of Clinical Nutrition, 104(3), 553–556. https://doi.org/10.3945/ajcn.115.129858

Everyone. (2020). Water Reminder - Remind Drink Water. Hanoi: Smart Apps OGS Studio. Retrieved from https://play.google.com/store/apps/details?id=com.remind.drink.water.hourly&hl=en_US&gl=US

Fogel, A. L., & Kvedar, J. C. (2018). Artificial intelligence powers digital medicine. Npj Digital Medicine, 1(5), 1–4. https://doi.org/10.1038/s41746-017-0012-2

Ford, B. A., & McElvania, E. (2020). Machine learning takes laboratory automation to the next level. Journal of Clinical Microbiology, 58(4), 1683–1702. https://doi.org/10.1128/JCM.00012-20

Fujimoto, T., Hashimoto, T., Sakaki, H., Higashi, Y., Tamura, T., & Tsuji, T. (1998). Automated handling system for excretion. Annual International Conference of the IEEE Engineering in Medicine and Biology - Proceedings, 4, 1973–1976. Hong Kong: IEEE. https://doi.org/10.1109/iembs.1998.746989

Funn Media. (2020). WaterMinder - Water Tracker and Drink Reminder App. Cicero Ave: Funn Media. Retrieved from https://play.google.com/store/apps/details?id=com.funnmedia.waterminder

Ghasemi, E., Khorvash, M., Ghorbani, G. R., & Elmamouz, F. (2014). Effects of straw treatment and nitrogen supplementation on digestibility, intake and physiological responses of water intake as well as urine and faecal characteristics. Journal of Animal Physiology and Animal Nutrition, 98(1), 100–106. https://doi.org/10.1111/jpn.12052

Grandjean, A. (2014). Water Requirements, Impinging Factors, and Recommended Intakes. Geneva.

Guo, Q., Wang, B., Cao, S., Jia, C., Zhao, L., Zhang, Q., … Duan, X. (2020). Patterns and sociodemographic determinants of water intake by children in China: results from the first national population-based survey. European Journal of Nutrition, 59(2), 529–538. https://doi.org/10.1007/s00394-019-01921-w

Ivan, J., Nurdiansyah, R., & Parikesit, A. A. (2019). Mathematical Problem Solving: One Way to Prevent Dementia. Frontiers in Health Informatics, 8(1), 14. https://doi.org/10.30699/fhi.v8i1.179

Jéquier, E., & Constant, F. (2010). Water as an essential nutrient: the physiological basis of hydration. European Journal of Clinical Nutrition, 64, 115–123. https://doi.org/10.1038/ejcn.2009.111

Kenefick, R. W., & Sawka, M. N. (2007). Hydration at the Work Site. Journal of the American College of Nutrition, 26, 597S-603S. https://doi.org/10.1080/07315724.2007.10719665

Kobrinskii, B. A. (2020). Fuzzy and Reflection in the Construction of a Medical Expert System. Journal of Software Engineering and Applications, 13(02), 15–23. https://doi.org/10.4236/jsea.2020.132002

Liao, J. C., & Churchill, B. M. (2001). Pediatric urine testing. Pediatric Clinics of North America, 48(6), 1425–1440. https://doi.org/10.1016/S0031-3955(05)70384-9

Liska, D., Mah, E., Brisbois, T., Barrios, P. L., Baker, L. B., & Spriet, L. L. (2019). Narrative Review of Hydration and Selected Health Outcomes in the General Population. Nutrients, 11(1), 70–99. https://doi.org/10.3390/nu11010070

Malisova, O., Bountziouka, V., Panagiotakos, D. Β., Zampelas, A., & Kapsokefalou, M. (2013). Evaluation of seasonality on total water intake, water loss and water balance in the general population in Greece. Journal of Human Nutrition and Dietetics, 26(SUPPL.1), 90–96. https://doi.org/10.1111/jhn.12077

Nguyen, V. P. (2014). An open source program to generate zero-thickness cohesive interface elements. Advances in Engineering Software, 74(August), 27–39. https://doi.org/10.1016/j.advengsoft.2014.04.002

Park, S. min, Won, D. D., Lee, B. J., Escobedo, D., Esteva, A., Aalipour, A., … Gambhir, S. S. (2020). A mountable toilet system for personalized health monitoring via the analysis of excreta. Nature Biomedical Engineering, 4(6), 624–635. https://doi.org/10.1038/s41551-020-0534-9

Perrier, E. T., Armstrong, L. E., Bottin, J. H., Clark, W. F., Dolci, A., Guelinckx, I., … Péronnet, F. (2020, July). Hydration for health hypothesis: a narrative review of supporting evidence. European Journal of Nutrition, pp. 1–14. Springer. https://doi.org/10.1007/s00394-020-02296-z

Popkin, B. M., D’Anci, K. E., & Rosenberg, I. H. (2010). Water, hydration, and health. Nutrition Reviews, Vol. 68, pp. 439–458. Blackwell Publishing Inc. https://doi.org/10.1111/j.1753-4887.2010.00304.x

Riebl, S. K., & Davy, B. M. (2013). The Hydration Equation. ACSM’S Health & Fitness Journal, 17(6), 21–28. https://doi.org/10.1249/FIT.0b013e3182a9570f

Rose, C., Parker, A., Jefferson, B., Cartmell, E., & Rose, © C. (2015). The Characterization of Feces and Urine: A Review of the Literature to Inform Advanced Treatment Technology. Critical Reviews in Environmental Science and Technology, 45, 1827–1879. https://doi.org/10.1080/10643389.2014.1000761

Sawka, M. N., Cheuvront, S. N., & Carter, R. (2005). Human Water Needs. Nutrition Reviews, 63(suppl_1), S30–S39. https://doi.org/10.1111/j.1753-4887.2005.tb00152.x

Shu, Z., Liu, G., Xie, Q., & Ren, Z. (2017). A method of urine detection based on front vision and image recognition. Proceedings - 2016 9th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics, CISP-BMEI 2016, 402–405. Datong: Institute of Electrical and Electronics Engineers Inc. https://doi.org/10.1109/CISP-BMEI.2016.7852744

Stachenfeld, N. S., Leone, C. A., Mitchell, E. S., Freese, E., & Harkness, L. (2018). Water intake reverses dehydration associated impaired executive function in healthy young women. Physiology and Behavior, 185(1), 103–111. https://doi.org/10.1016/j.physbeh.2017.12.028

Stookey, J. D. (2019). Analysis of 2009-2012 Nutrition Health and Examination Survey (NHANES) Data to Estimate the Median Water Intake Associated with Meeting Hydration Criteria for Individuals Aged 12-80 Years in the US Population. https://doi.org/10.3390/nu11030657

How to Cite
Swastika, N., Hasuki, W., Savero, S., Satya, P., & Parikesit, A. (2021). WATER INTAKE APPLET BASED ON HUMAN EXCREMENT. Jurnal Riset Informatika, 3(2), 109-118. https://doi.org/10.34288/jri.v3i2.189
Article Metrics

Abstract viewed = 161 times
PDF downloaded = 39 times