Sistem Prediksi Risiko Stunting Menggunakan Bayesian Network Berbasis GIS
DOI:
https://doi.org/10.59039/ekstensi.v1i1.2Keywords:
Bayesian Network, GIS, Nutrition, Risk Prediction, StuntingAbstract
Stunting is one of the nutritional issues faced in the world. Stunting is a chronic nutritional problem caused by food intake that does not suit nutritional needs. Indonesia has a high commitment to stunting prevention efforts so that Indonesian children can grow and develop optimally and innovate and compete at the global level. The effort was demonstrated through the National Strategy of accelerating Stunting Prevention, known as Stranas Stunting, implemented in 2018 – 2024. The system was developed to be able to identify stunting supporting factors so that it can provide accurate information. And the system also displays data in the form of maps, to make it easier to perform analysis in a room. Spatial data is displayed using GIS (Geographic Information System). The study's result was that 75% of children grew up normally, while the other 25% predicted to suffer from stunting. The dominant factor affecting stunting is 57% of children with short anthropometry, 51% of children who do not get exclusive breast milk, 66% of mothers' knowledge of nutrition is not good, 69% of foster care patterns by mothers themselves, 80% of pregnant women with a diet less than three times, 99% of pregnant women not infected with the disease, and 54% of pregnant women with anthropometry less than mid-upper arm circumference (MUAC).
Downloads
References
S. C. Wang, R. Gao, and L. M. Wang, “Bayesian network classifiers based on Gaussian kernel density,” Expert Syst Appl, 2016, doi: 10.1016/j.eswa.2015.12.031.
P. Fuster-Parra, P. Tauler, M. Bennasar-Veny, A. Ligeza, A. A. López-González, and A. Aguiló, “Bayesian network modeling: A case study of an epidemiologic system analysis of cardiovascular risk,” Comput Methods Programs Biomed, 2016, doi: 10.1016/j.cmpb.2015.12.010.
Y. Rahayu, K. Muludi, and A. Hijriani, “Pemetaan Penyebaran dan Prediksi Jumlah Penduduk Menggunakan Model Geometrik di Wilayah Bandar Lampung Berbasis Web-GIS,” Journal of Information Systems Engineering and Business Intelligence, 2016, doi: 10.20473/jisebi.2.2.95-101.
E. Molina-Navarro, P. Segurado, P. Branco, C. Almeida, and H. E. Andersen, “Predicting the ecological status of rivers and streams under different climatic and socioeconomic scenarios using Bayesian Belief Networks,” Limnologica, vol. 80, Jan. 2020, doi: 10.1016/j.limno.2019.125742.
R. Darma Rusdiyan Yusron and I. Machfud, “Klasifikasi dan Monitoring Status Gizi Balita Melalui Penerapan Metode Naïve Bayes Classification Berbasis GIS Classification and Monitoring of Toddler Nutrition Status Through Application of GIS-Based Naïve Bayes Classification Method,” Jurnal Ilmiah Intech : Information Technology Journal of UMUS, vol. 4, no. 02, pp. 161–168, 2022.
A. Sutriyawan, R. Dian Kurniawati, P. S. Studi, K. Masyarakat, F. Ilmu Kesehatan, and U. Bhakti Kencana, “Proyeksi dan Pemetaan Sebaran Kasus Demam Berdarah Dengue (DBD) Berbasis Sistem Informasi Geografi (SIG) Projection and Mapping of Dengue Hemorrhagic Fever (DHF) Cases Based on Geographic Information System (SIG),” Jurnal Kesehatan Masyarakat, vol. 6, no. 2, pp. 71–81.
M. Tinambunan and S. Sintaro, “APLIKASI RESTFULL PADA SISTEM INFORMASI GEOGRAFIS PARIWISATA KOTA BANDAR LAMPUNG,” Jurnal Informatika dan Rekayasa Perangkat Lunak (JATIKA), vol. 2, no. 3, pp. 312–323, 2021, [Online]. Available: http://jim.teknokrat.ac.id/index.php/informatika
D. Alita, I. Tubagus, Y. Rahmanto, and A. Nurkholis, “SISTEM INFORMASI GEOGRAFIS PEMETAAN WILAYAH KELAYAKAN TANAM TANAMAN JAGUNG DAN SINGKONG PADA KABUPATEN LAMPUNG SELATAN,” Journal Sosial Science and Teknology for Community Service (JSSTSCS), vol. 1, no. 2, pp. 1–09, 2020, [Online]. Available: https://ejurnal.teknokrat.ac.id/index.php/teknoabdimas
A. Mittal and A. Kassim, Bayesian network technologies: Applications and graphical models. 2007. doi: 10.4018/978-1-59904-141-4.
J. Pearl, “A Personal Journey into Bayesian Networks,” Technical Report, 2018.
R. E. Neapolitan, “Learning Bayesian Network Structure,” in Probabilistic Methods for Bioinformatics, 2009. doi: 10.1016/b978-0-12-370476-4.50011-5.
J. Han, M. Kamber, J. Pei, J. Han, M. Kamber, and J. Pei, “9 – Classification: Advanced Methods,” in Data Mining, 2012.
D. Kurniasih, H. Hilmansyah, M. P. Astuti, and S. Imam, “Sehat dan bugar berkat gizi seimbang,” Jakarta: Kompas Gramedia, 2010.
World Health Organization (WHO), “Global Nutrition Targets 2025,” Can Pharm J, 2014.
F. Meilyasari and M. Isnawati, “FAKTOR RISIKO KEJADIAN STUNTING PADA BALITA USIA 12 BULAN DI DESA PURWOKERTO KECAMATAN PATEBON, KABUPATEN KENDAL,” Journal of Nutrition College, 2014, doi: 10.14710/jnc.v3i2.5437.
T. Fikadu, S. Assegid, and L. Dube, “Factors associated with stunting among children of age 24 to 59 months in Meskan district, Gurage Zone, South Ethiopia: A case-control study,” BMC Public Health, 2014, doi: 10.1186/1471-2458-14-800.
R. Bishwakarma, “Spatial inequality in child nutrition in Nepal: Implications of regional context and individual/household composition,” ProQuest Dissertations and Theses. 2011.
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2022 EKSPLORASI TEKNOLOGI ENTERPRISE DAN SISTEM INFORMASI (EKSTENSI)
This work is licensed under a Creative Commons Attribution 4.0 International License.
