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 Epidemiology and Society Health Review| ESHR 
Vol. 4, No. 2, 2022, pp. 89-90 ISSN 2656-6052 (online) | 2656-1107 (print) 
 
 

      10.26555/eshr.v4i2.6369  

 
89 

 
 

  

Viewpoint 

Technological Innovation is Needed to Accelerate Stunting 
Reduction in Indonesia 

 
Herman Yuliansyah1*, Sulistyawati Sulistyawati2, Surahma Asti Mulasari2 
1 Laboratory of Artificial Intelligence, Informatics Department, Universitas Ahmad Dahlan, 

Yogyakarta, Indonesia 
2 Faculty of Public Health, Universitas Ahmad Dahlan, Yogyakarta, Indonesia 
 
* Correspondence: herman.yuliansyah@tif.uad.ac.id. Phone: +6281328557057 

Received 29 July 2022; Accepted 3 August 2022; Published 6 August 2022 

 

The WHO defines stunting as a low height for age condition in which toddlers have short height 
due to suboptimal health quality due to inappropriate quantity and quality of food intake (1). In 
2019, Indonesia's number of children with stunting is still relatively high at 27.67% (2). This 
situation is still far from the expectation that the national stunting rate will be below 14% by 
2024. Geographically, in Indonesia - stunting in children under five is spread across the 
province, although the number among the area varies.  

The Indonesian government has implemented programs to reduce stunting by targeting 
several groups, including: pregnant and maternity mothers, toddlers, school-age children, 
adolescents, and young adults (3). The actions include interventions and efforts to increase 
knowledge among the related subject – mostly among women. These efforts must still have 
been carried out until recently. However, along with the development of the digital era, stunting 
prevention needs to involve technology as an innovation to predict the possibility of a toddler 
becoming stunted in the future when their intake is insufficient. 

In Indonesia – through the Integrated Service Post (Posyandu) – toddlers receive regular 
monthly check-ups, including their height and weight since birth. This program collects cohort 
data at the individual level, which is essential to see trends and for developing predictions. 
Considering the magnitude of the benefits of this data, technological innovation is needed to 
utilize this data further and encourage the sustainability of data input. This innovation should 
provide alerts for early stunting detection so that program targets can be more accurate. The 
digitized cohort data can be used as capital to estimate the possibility of a child becoming 
stunted in their development. Thus, prevention efforts can be carried out early if it is known 
that a toddler is indicated to be suffering from Stunting. Of course, this innovation cannot be 
run alone but supports the existing prevention program. 

The increasing trend of artificial intelligence technology (4) and the Internet of Things (IoT) (5) 
usage is an opportunity to support stunting prevention. Conventional measuring tools 
commonly used to record the condition of toddlers are transformed into digital instruments as 
data feeders to be stored in cloud storage by utilizing IoT. Machine Learning learns this data 



 
 

 

Vol. 4, No. 2, 2022, pp. 89-90    10.26555/eshr.v4i2.6369 
  

90 

to make predictions as early detection of Stunting (6–9). In addition, data in cloud storage can 
also be processed to become a decision support system in making public policies. 

Keywords:  Stunting; Internet of things; Innovation; Cohort data, Artificial intelligence 

REFERENCES  

1.  World Health Organization. Malnutrition. Web. 2022 [cited 2022 Jun 5]. Available from: 
https://www.who.int/health-topics/malnutrition#tab=tab_1 

2.  Kementerian Kesehatan RI. Buletin Jendela Data dan Informasi Kesehatan: Situasi Balita 
Pendek (Stunting) di Indonesia. Kementeri Kesehat RI. 2018;20.  

3.  Kemenkes RI. Buletin Stunting. Kementeri Kesehat RI. 2018;301(5):1163–78.  
4.  Kugler L. Artificial intelligence, machine learning, and the fight against world hunger. 

Commun ACM. 2022 Feb;65(2):17–9. Available from: 
https://dl.acm.org/doi/10.1145/3503779 

5.  Yang Y, Wang H, Jiang R, Guo X, Cheng J, Chen Y. A Review of IoT-Enabled Mobile 
Healthcare: Technologies, Challenges, and Future Trends. IEEE Internet Things J. 2022 
Jun 15;9(12):9478–502. Available from: https://ieeexplore.ieee.org/document/9686065/ 

6.  Chilyabanyama ON, Chilengi R, Simuyandi M, Chisenga CC, Chirwa M, Hamusonde K, 
et al. Performance of Machine Learning Classifiers in Classifying Stunting among Under-
Five Children in Zambia. Children. 2022 Jul 20;9(7):1082. Available from: 
https://www.mdpi.com/2227-9067/9/7/1082 

7.  Bitew FH, Sparks CS, Nyarko SH. Machine learning algorithms for predicting 
undernutrition among under-five children in Ethiopia. Public Health Nutr. 2021 Oct 8;1–
12. Available from: 
https://www.cambridge.org/core/product/identifier/S1368980021004262/type/journal_arti
cle 

8.  Islam MM, Rahman MJ, Islam MM, Roy DC, Ahmed NAMF, Hussain S, et al. Application 
of machine learning based algorithm for prediction of malnutrition among women in 
Bangladesh. Int J Cogn Comput Eng. 2022 Jun;3:46–57. Available from: 
https://linkinghub.elsevier.com/retrieve/pii/S2666307422000067 

9.  Khan W, Zaki N, Masud MM, Ahmad A, Ali L, Ali N, et al. Infant birth weight estimation 
and low birth weight classification in United Arab Emirates using machine learning 
algorithms. Sci Rep. 2022 Dec 15;12(1):12110. Available from: 
https://www.nature.com/articles/s41598-022-14393-6 

 


