Comparison of Machine Learning Algorithms for Predicting Stunting Prevalence in Indonesia
(1) Information Systems Study Program, Faculty of Engineering. Tadulako University, Palu
(2) Informatics Engineering Study Program, Faculty of Engineering. Tadulako University, Palu
(3) Information Systems Study Program, Faculty of Engineering. Tadulako University, Palu
(4) Department of Public Health, Faculty of Public Health. Tadulako University, Palu
(5) Informatics Engineering Study Program, Faculty of Engineering. Tadulako University, Palu
(6) Informatics Engineering Study Program, Faculty of Engineering. Tadulako University, Palu
(*) Corresponding Author
Abstract
Stunting is a serious public health problem, especially among under-fives, which can cause serious short- and long-term impacts. Efforts to tackle stunting in Indonesia involve national strategies and development priorities. Therefore, this study aims to compare the performance of machine learning regression algorithms in predicting stunting prevalence in Indonesia. The data collected is secondary data. The data collection was done carefully, taking explicit details regarding the source, scope, extent, and analysis of the dataset, and using a careful sampling methodology. The model evaluation results show that the Random Forest Regression algorithm has the best performance, with a success rate of 90.537%. The application of this model to the new dataset shows that East Nusa Tenggara province has the highest percentage of stunting at 31.85%, while Bali has the lowest percentage at 12.07%. Visualization of the dashboard using Tableau provides a clear picture of the distribution of stunting in Indonesia. In conclusion, this research contributes to the development of science, especially in the field of machine learning and public health, and provides policy recommendations for tackling stunting in Indonesia.
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A. M. K. Evi Nuryuliyani, "Getting to Know More about Stunting," https://yankes.kemkes.go.id/, 2023. https://yankes.kemkes.go.id/view_artikel/2657/mengenal-lebih-jauh-tentang-stunting#:~:text=Although declining%2C the rate is still, in the first 1,000 days of life. (accessed Feb. 06, 2024).
V. Ratriani, "Stunting According to WHO is a Chronic Malnutrition Problem, This is its Impact and Prevention," health.kontan.co.id, 2023. https://kesehatan.kontan.co.id/news/stunting-menurut-who-adalah-masalah-kurang-gizi-kronis-ini-dampak-dan-pencegahannya-1 (accessed Feb. 10, 2024).
stunting. Go.id, "What is stunting?" stunting.go.id, 2020. https://stunting.go.id/faq/apakah-yang-dimaksud-dengan-stunting/ (accessed Feb. 07, 2023).
Who, "Stunting prevalence among children under five years of age (%) (model-based estimates)," www.who.int, 2023.
N. O. Nirmalasari, "Child Stunting: Causes and Risk Factors of Stunting in Indonesia," Qawwam J. Gend. Mainstreaming, vol. 14, no. 1, pp. 19-28, 2020, doi: 10.20414/Qawwam.v14i1.2372.
Ministry of Health of the Republic of Indonesia, "Handbook on the Results of the Study of the Status of Nutrition of Indonesia (SSGI) at the National, Provincial, and District/City Levels in 2021," 2021.
Ministry of Health, "Nutrition Status Survey of Indonesia (SSGI) 2022 Results," Ministry of Health, pp. 1-150, 2022.
M. S. Haris, A. N. Khudori, and W. T. Kusuma, "Comparison of Supervised Machine Learning Methods for Prediction of Stunting Prevalence in East Java Province," J. Technol. Inf. and Comput. Science, vol. 9, no. 7, p. 1571, 2022, doi: 10.25126/jtiik.2022976744.
National Development Planning Agency (Bappenas), "Rpjmn 2020-2024," Natl. Mid-Term Dev. Plan 2020-2024, p. 313, 2020, [Online]. Available: https://www.bappenas.go.id/id/data-dan...dan.../rpjmn-2015-2019/
peraturan.bpk.go.id, "Acceleration of Stunting Reduction," peraturan.bpk.go.id, 2021. https://peraturan.bpk.go.id/Details/174964/perpres-no-72-tahun-2021#:~:text=This presidential decree regulates, among others, reporting%3B and 5) funding. (accessed Feb. 11, 2024).
stunting. Go.id, "Stunting and its Causes," stunting.go.id, 2021. https://dashboard.stunting.go.id/latar-belakang-home/ (accessed Feb. 06, 2024).
P. D. Kusuma, "Machine Learning Theories, Programs, and Case Studies," https://books.google.co.id/, 2020. https://books.google.co.id/books/about/Machine_Learning_Teori_Program_Dan_Studi.html?id=4k3sDwAAQBAJ&redir_esc=y (accessed Feb. 13, 2024).
E. Retnoningsih and R. Pramudita, "Getting to Know Machine Learning with Supervised and Unsupervised Learning Techniques Using Python," vol. 7, no. 2, pp. 156-165, 2020.
Farah Imaniar R, "Linear Regression in Machine Learning," medium.com, 2019. https://medium.com/group-3-machine-learning/regresi-linear-pada-machine-learning-97f34ce18633 (accessed Feb. 13, 2024).
D. Store, "7 Types of Research Methodology and Full Explanation," deepublishstore.com, 2023. https://deepublishstore.com/blog/jenis-metodologi-penelitian/ (accessed Feb. 13, 2024).
R. Pratama, "Research Methods: Definition, Types, and Examples (+PDF)," bocahkampus.com, 2024. https://bocahkampus.com/metode-penelitian (accessed Feb. 13, 2024).
B. Central Bureau of Statistics, "Health Statistics Profile 2023," 2023.
B. Statistics Indonesia, "Settlement and Housing," bps.go.id, 2023. https://www.bps.go.id/id/statistics-table?subject=525 (accessed Feb. 14, 2024).
B. Statistics Indonesia, "Consumption and Income," bps.go.id, 2023. https://www.bps.go.id/id/statistics-table?subject=523 (accessed Feb. 14, 2024).
B. Central Bureau of Statistics, Front cover of CHILDHOOD PROFILE 2021 i. 2021.
R. Agustina, "Front cover of EDUCATION STATISTICS 2021 i," Pus. Stat., 2021.
BPS, "Indonesian Education Statistics 2022," Badan Pus. Stat., no. February, pp. 1-353, 2022.
BPS 2022, "Statisti Pendidikan 2023," Badan Pus. Stat., vol. 1101001, p. 790, 2023.
B. Statistics Indonesia, "Maternal and Child Health Profile 2022," pp. 7823-7830.
National Bureau of Statistics, "Poverty Profile in Indonesia," Badan Pus. Stat., vol. 01, no. 05, pp. 1-8, 2022.
Central Bureau of Statistics of Indonesia, "Poverty Profile in Indonesia September 2022," Ber. Official Stat., vol. 1, no. 5, p. 8, 2023.
"Poverty Profiles in Indonesia March 2023," no. 47, 2023.
A. Pragota, "Scatterplot: Visualization for Regression," learn.nural.id, 2021. https://learn.nural.id/course/statistics/regresi-linier/scatterplot (accessed Feb. 15, 2024).
Trivusi, "Support Vector Regression (SVR) Algorithm:) A Type of SVM for Regression," trivusi. Web. Id, 2022. https://www.trivusi.web.id/2022/08/algoritma-svr.html (accessed Feb. 15, 2024).
Herlambang, "Machine Learning: Decision Tree Regression," megabagus. Id, 2019. https://www.megabagus.id/machine-learning-decision-tree-regression/ (accessed Feb. 15, 2024).
R. Mulywan, "Random Forests," rifqimulyawan.com, 2024. https://rifqimulyawan.com/kamus/random-forests/ (accessed Feb. 15, 2024).
Binus, "Generative Additve Model (GAM)," binus.ac.id, 2021. https://mti.binus.ac.id/2021/12/31/generative-additve-model-gam/ (accessed Feb. 15, 2024).
Trivusi, "Difference between MAE, MSE, RMSE, and MAPE in Data Science," trivusi. Web. Id, 2023. https://www.trivusi.web.id/2023/03/perbedaan-mae-mse-rmse-dan-mape.html (accessed Feb. 15, 2024).
Datatechnotes, "Regression Accuracy Check in Python (MAE, MSE, RMSE, R-Squared)," datatechnotes.com, 2019. datatechnotes (accessed Feb. 15, 2024).
Tableau, "What is Tableau?" tableau.com, 2021. https://www.tableau.com/why-tableau/what-is-tableau (accessed Feb. 15, 2024)
DOI: https://doi.org/10.32736/sisfokom.v13i2.2097
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