Child Health Detection System Along with the Recommendations Using  K-Nearest Neighbor Artificial Intelligence

Khairulisni saniati; Krisdiana Wijayanti; Kurnianingsih; Khairulisni saniati; Krisdiana Wijayanti; Kurnianingsih

doi:10.61096/ijamscr.v11.iss3.2023.219-222

Child Health Detection System Along with the Recommendations Using K-Nearest Neighbor Artificial Intelligence

Issue

Vol. 11 No. 3 (2023): 2023 Volume -11 - Issue 3

Date Log

Published

July 3, 2023

This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Child Health Detection System Along with the Recommendations Using K-Nearest Neighbor Artificial Intelligence

https://doi.org/10.61096/ijamscr.v11.iss3.2023.219-222

Khairulisni saniati

Semarang Health Polytecnic, Semarang, , Indonesia

Krisdiana Wijayanti

Semarang Health Polytecnic, Semarang, , Indonesia

Kurnianingsih

Semarang State Polytechnic, Semarang, Indonesia

Corresponding Author(s) : Khairulisni saniati

khairulisni28@gmail.com

International Journal of Allied Medical Sciences and Clinical Research, Vol. 11 No. 3 (2023): 2023 Volume -11 - Issue 3
Article Published : July 3, 2023

Abstract

Background: Children under 5 years of age were very vulnerable to disease. Therefore, child health is one of the main focuses in the health sector. The main triggers for under-five mortality were preventable and treatable diseases. There were 5 diseases mostly experienced by under-five children, namely pneumonia, diarrhea, malaria, measles and malnutrition. In Indonesia, child health detection is still performed manually by midwives using the IMCI cards, but during the Covid-19 pandemic, child health detection is not actively performed. The K-Nearest Neighbor Artificial Intelligence System can be used for child health detection.
Objective: To develop a child health detection system along with the recommendations using k-nearest neighbor artificial intelligence.
Methods: Research and Development (RnD) and system accuracy test. The study subjects involved secondary data of 1000 IMCI register data. Data were tested using the intra-class correlation coefficient and the K-Nearest Neighbor algorithm.
Results: The results of the child health detection system using k-nearest neighbor artificial intelligence obtained a system accuracy rate of 100% and the sick child health recommendation system using k-nearest neighbor artificial intelligence obtained a system accuracy rate of 89.3%. It was proven that k-nearest neighbor artificial intelligence was able to develop a child health detection system along with the recommendations which can be used by parents.

Keywords

children health detection k-nearest neighbor

Khairulisni saniati, Krisdiana Wijayanti, Kurnianingsih. Child Health Detection System Along with the Recommendations Using K-Nearest Neighbor Artificial Intelligence. Int. J. of Allied Med. Sci. and Clin. Res. [Internet]. 2023 Jul. 3 [cited 2025 Sep. 20];11(3):219-22. Available from: https://ijamscr.com/ijamscr/article/view/1332

Download Citation

References

References

1. Notoatmodjo S. Public Health: Science and Art R, ed.).; 2011.
2. Indonesian Ministry of Health. The law protects children’s right to health care. Published online 2016. Available from: https://www.kemkes.go.id/article/print/16051800001/undang-undang-lindungi-hak-anak-untuk-dapatkan-pelayanan-kesehatan.html.
3. Ministry of Health. Head of planning and budget bureau, General Secretariat. Preparation Health Dev Strateg Plan. 2020-2024. Published online;2019.
4. Suparmi IBM, Rizkianti A, Sari K, et al. Integrated Management Services for Sick Toddlers (MTBS) at Puskesmas in Eastern Indonesia. 2018;(Imci):271-278.5.
5. Ministry of Health of the Republic of Indonesia. MTBS chart. Bk. 2019.
6. Ministry of Health of the Republic of Indonesia. Indonesian child health Profile 2018. Sci Educ. 2018;5(1):12-21.
7. Indonesian Ministry of Health. Profile of the Ministry of Health of The Republic of Indonesia 2019. Vol. 53; 2019.
8. Ministry of Health of the Republic of Indonesia. Key results of Riskesdas 2018. Published online 2018.
9. Central Java Provincial Health Office. Health profile of Central Java Province in 2019. Central Java provincial health office. 2019;24:273-275:3511351.
10. Semarang City Health Office. Health Profile 2018. Published online 2018.
11. World Health Organization. Coronavirus disease 2019-situation Report 182. Published online; 2020. doi: 10.1213/xaa.000000000000001218.
12. Price DL, Gwin JF. Pediatric nursing: an introductory text Elsevier, editor; 2014.
13. Nursanti A. Indonesian children positive for Covid-19. Ministry of Health: 1.6 percent Died, Activities Stay at Home. Published online 2020. Available from: https://www.pikiran-rakyat.com/nasional/pr-01616148/7008-anak-indonesia-positif-covid-19-kemenkes-16-persenya-meninggal-aktvitas-tetaplah-di-rumah.
14. Nurmawati I, Erawantini F. The need for designing a screening system for sick toddlers based on MTBS classification and reasoning. J-Kes. 2019;6(3):83-7. doi: 10.25047/j-kes.v6i3.18.
15. Pandemic M, Wahyuni I. Util WhatsApp Monit Baby Growth Dev Patterns. 2021;2(1):14-27.
16. Ministry of Health of the Republic of Indonesia. Toddler health during COVID-19 emergency response. Ministry of Health of the Republic of Indonesia; 2020. Published online 2020:1-30.
17. Ministry of Health of the Republic of Indonesia. Regulation of the Ministry of Health of the Republic of Indonesia Number 66 of. Vol. 2014; 2014. Published online 2014:2. Available from: http://kesmas.kemkes.go.id/perpu/konten/permenkes/pmk-no.-66-ttg-pemantauan-tumbuh-kembang-anak.
18. Indriani AF, Rachmawati EY, Fitriana JD. Utilization of the certainty factor method in the expert system for diagnosing disease in children. TechnoCom. 2017;17(1):12-22. doi: 10.33633/tc.v17i1.1576.
19. Susanti D, Wulandari H, Juaeriah R, Dewi SP. Application of interprofessional education (IPE) in the toddler Mother Class by health worker students to improve mothers’ attitudes towards toddler health in Cimahi city. By student health workers to improve attitude mother. 2017;3(243):51-7.
20. Aini F, Widyawati MN, Santoso B. Diagnosis of preeclampsia in pregnant women using a web-based information system. J Silampari Nurs. 2019;2. doi: 10.31539/jks.v2i2.508.
21. Darmayunata Y. Web-based expert system using backward chaining method. J Inf Technol Comput Sci. 2018;1(2):231-9.
22. Zuhriyah S, Wahyuningsih P. Application of certainty factor to expert system to diagnose measles rubella. Ilk J Ilm. 2019;11(2):159-66. doi: 10.33096/ilkom.v11i2.441.159-166.
23. Kurniawan V. Expert system for diagnosing human digestive disorders using the forward chaining method on Android-based mobile phones. 2019;53(9):1689-99.

Author biographies is not available.