PENGELOMPOKAN STUNTING MENGGUNAKAN METODE K-MEDOIDS DI DAERAH ISTIMEWA YOGYAKARTA (DIY)

Amalia Rizki Cahyani, Muhammad Muhajir, Ersa Riga Puspita, Lathifah Aliya Pratiwi

Abstract


Stunting, or the condition of short stature in toddlers, is a problem caused by prolonged insufficient nutrition intake. This issue arises from inadequate feeding practices that do not meet the nutritional needs of a toddler. Stunting can begin during fetal development and becomes apparent around the age of two. Several factors contribute to stunting, including high-risk levels, lack of adequate housing, lack of proper sanitation facilities, lack of access to safe drinking water, and inadequate family income. This study employs k-medoids cluster analysis to identify the grouping of sub-districts in the Yogyakarta Special Region (DIY Province) based on stunting risk factors. The research findings indicate that Cluster 1 has the highest rates of stunting and lack of family income, Cluster 4 has the highest instances of inadequate access to safe drinking water and housing, and Cluster 5 has the highest rates of inadequate sanitation facilities.


Keywords


Stunting; Clustering; K-Medoids

Full Text:

PDF


DOI: https://doi.org/10.15548/map.v6i1.8523
Abstract views : 6 times
PDF : 2 times

References


Gelman, A., Carlin, J. B., Stern, H. S., & Rubin, D. B. (2004). Bayesian Data Analysis (2nd ed.). New York: Chapman & Hall.

Kass, R. E., & Wasserman, L. (1996). The Selection of Prior Distribution by Formal Rules. Journal of the American Statistical Association, 91, 1343-1370.

Carlin, B. P., & Louis, T. A. (2009). Bayesian Methods for Data Analysis (3rd ed.). New York: Chapman & Hall.

Andrieu, C., de Freitas, N., Doucet, A., & Jordan, M. I. (2003). An Introduction to MCMC for Machine Learning. Machine Learning, 50, 5-43.

Bolstad, W. M. (2007). Introduction to Bayesian Statistics (2nd ed.). New Jersey: Wiley.

Gelman, A. (2007). Statistical Modeling, Causal Inference, and Social Science. Retrieved Februari 23, 2013, from http://andrewgelman.com/2007/07/18/informative_and/

Craiu, R. V., & Rosenthal, J. S. (2014). Bayesian Computation Via Markov Chain Monte Carlo. Annual Review of Statistics and Its Application, I, 179-201.

Neter, J., Wasserman, W., & Kutner, M. H. (1983). Applied Linear Regression. Illnois: Richard D. Irwin.

Box, G. E., & Tiao, G. C. (1973). Bayesian Inference in Statistical Analysis. Boston: Addison-Wesley Publishing Company.

Kementerian Desa, P. D. (2017). Buku Saku Desa dalam Penanganan Stunting. Jakarta: Kementerian Desa, Pembangunan Daerah Tertinggal, dan Transmigrasi Republik Indonesia.

DIY, D. K. (2020). Profil Kesehatan D.I. Yogyakarta. Yogyakarta: Dinas Kesehatan DIY.

Indrastuty, D., & Pujiyanto. (2014). Determinan Sosial Ekonomi Rumah Tangga dari Balita Stunting di Indonesia: Analisis Data

Indonesia Family Life Survey (IFLS) 2014. Jurnal Ekonomi Kesehatan Indonesia, 3(2), 68-75.

Satriawan, E. (2018). Strategi Nasional Percepatan Pencegahan Stunting 2018-2024. Jakarta: Tim Nasional Percepatan Penanggulangan Kemiskinan (TNP2K).

Prastuti, K., Alisa, S. R., Ni'mah, A. R., Rohmah, H., Rahmadya, S. R., Sumanto, R. P., & Mukminin, A. (2023). Sosialisasi Bahaya Stunting Pada Anak Usia Dini di TK Pertiwi Kertomulyo. Jurnal Cendekia Mengabdi Berinovasi dan Berkarya, 1(3), 90-95.

Bappenas. (2019). Kajian Sektor Kesehatan Pembangunan Gizi di Indonesia. Jakarta: Kementrian PPN/Bappenas.

Hutaminingtyas, N., Gono, J. N., & Pradekso, T. (2023). Pengaruh Terpaan Pemasaran Sosial Pencegahan Stunting dan Tingkat Pendidikan Masyarakat dan Tingkat Pendidikan Masyarakat Terhadap Perilaku Masyarakat Dalam Upaya Pencegahan Stunting. Interaksi Online, 11(4), 406-415.

Herman, E., Zsido, K. E., & Fenyves, V. (2022). Cluster Analysis with K-Means versus K-Medoid in Financial Performance Evaluation. Applied Sciences, 12(16), 7985.

Alkarkhi, F., & Alqaraghuli, W. A. (2019). Chapter 11: Cluster Analysis. In Easy Statistics for Food Science with R (pp. 177-186). USA: Academic Press.

Gujarati, D., & Porter, D. C. (2009). Basic Econometrics. New York: McGraw Hill.

Sihombing, R., Rachmatin, D., & Dahlan, J. A. (2019). Program Aplikasi Bahasa R untuk Pengelompokan Objek Menggunakan Metode K-Medoids Clustering. Jurnal EurekaMatika, 7(1), 58-79.

Sindi, S., Ningse, W. R., Sihombing, I. A., Zer, F. I., & Hartama, D. (2020). Analisis Algoritma K-Medoids Clustering dalam Pengelompokan Penyebaran Covid-19 di Indonesia. Jurnal Teknologi Informasi, 4(1), 166-173.

Marlina, D., Putri, N. F., Fernando, A., & Ramadhan, A. (2018). Implementasi Algoritma K-Medoids dan K-Means Untuk Pengelompokan Wilayah Sebaran Cacat pada Anak. Jurnal CoreIT, 4(2).

Shresta, N. (2021). Factor Analysis As a Tool for Survey Analysis. American Journal of Applied Mathematics and Statistics, 9(1), 4-11.

Usefi, H. (2022). Clustering, Multicollinearity, and Singular Vectors. Computational Statistics & Data Analysis, 173, 107523.


Refbacks

  • There are currently no refbacks.


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

 

Lisensi Creative Commonsis licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.