Prediksi Tingkat Prevalensi Stunting Kabupaten Lima Puluh Kota Menggunakan Metode Monte Carlo
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Abstract
Stunting is a condition of failure to thrive in children under five years old (infants under five years old) due to chronic malnutrition so that children are too short for their age. According to available data, the stunting prevalence rate in Lima Puluh Kota Regency in 2020 is quite high, at 8.28%. This has become the attention of the central government by establishing Lima Puluh Kota Regency as one of the Regencies/Cities Locations for the National Integrated Stunting Reduction Intervention Focus. The results of this study aim to assist the District Government of Lima Puluh Kota in planning the convergence of programs/interventions as an effort to accelerate stunting prevention and reduce the percentage of stunting under five in Lima Puluh Kota Regency. This research data uses the stunting prevalence rate from 2018 to 2020 which comes from data on the number of toddlers and the number of stunting toddlers from 22 health centers in Lima Puluh Kota Regency. Furthermore, the data was processed using the Monte Carlo method to predict the stunting prevalence rate in 2021. Based on the tests conducted using the Monte Carlo method, the highest stunting prediction rates were found at the Pakan Rabaa Public Health Center and the Suliki Public Health Center with a stunting prevalence rate of 11.70%. The level of accuracy obtained is 93.73%. The Monte Carlo method is suitable for predicting the prevalence of stunting in Lima Puluh Kota Regency, seen from the high level of accuracy from the results of data processing.
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[2] Meri Anggryni, Wiwi Mardiah, Yanti Hermayanti, Windy Rakhmawati, Gusgus Graha Ramdhanie, Henny Suzana Mediani, 2021, Faktor Pemberian Nutrisi Masa Golden Age dengan Kejadian Stunting pada Balita di Negara Berkembang, Jurnal Obsesi: Jurnal Pendidikan Anak Usia Dini, Volume 5 Issue 2 Pages 1764-1776, DOI: 10.31004/obsesi.v5i2.967
[3] Traverso L., Mazzoli E , Miller C, Pulighe G. , Perelli, Morese M. M and Branca, (2021), Cost Benefit and Risk Analysis of Low iLUC Bioenergy Production in Europe Using Monte Carlo Simulation, Energies 2021, 14, 1650, https://doi.org/10.3390/en14061650
[4] Ziad Francis, Sebastien Incerti,, Sara A. Zein, Nathanael Lampe,Carlos A. Guzmanc and Marco Durante, (2021), Monte Carlo Simulation of SARS-CoV-2 Radiation-Induced Inactivation for Vaccine Development, Radiation Research 195, 221–229, DOI: https://doi.org/10.1667/RADE-20-00241.1
[5] Abrar Hussain, Lihao Yang, Shifeng Mao, Bo Da, Karoly, Tok Esi, Z.J. Ding, (2021), Determination of electron backscattering coefficient of beryllium by a high-precision Monte Carlo simulation, Elsevier Nuclear Materials and Energy 26, 100862, https://doi.org/10.1016/j.nme.2020.100862
[6] I-Kai Chen, Matthew D.Klimek, and Maxim Perelstein,(2021),Improved neural network Monte Carlo simulation, SciPost, 10 (023) https://doi.org/10.21468/SciPostPhys.10.1.023
[7] Magdalena Kruza a , David Shaw a , Jacob Shaw b , Nicola Carslaw, (2021), Towards improved models for indoor air chemistry: A Monte Carlo simulation study, Elsevier Atmospheric Environment, 1352-2310, https://doi.org/10.1016/j.atmosenv.2021.118625
[8] Paper C., Colucci L., and Albino R. D., (2017),Predicting the Unpredictable : Using Monte Carlo Simulation to Predict Project Completion Date Predicting the Unpredictable : Using Monte Carlo Simulation to Predict Project Completion Date, no. October, pp. 0–6, https://doi.org/10.1007/978-3-319-55907-0
[9] J. Santony, (2020), Simulasi Penjadwalan Proyek Pembangunan Jembatan Gantung dengan Metode Monte Carlo, J. Informasi & Teknologi., vol. 2, no. 1, pp. 30–35, https://doi.org/10.37034/jidt.v2i1.34
[10] Surat Keputusan Menteri Perencanaan Pembangunan Nasional/ Kepala Badan Perencanaan Pembangunan Nasional, Nomor. 10/M.PPN/HK/02/2021 Tentang Penetapan Perluasan Kabupaten/Kota Lokasi Fokus Intervensi Penurunan Stunting Terintegrasi Tahun 2022 Tanggal 25 Februari 2021.
[11] Tarek Bouazizi, Mongi Lassoued, Zouhaier Hadhek, (2020), Oil Price Volatility Models during Coronavirus Crisis: Testing with Appropriate Models Using Further Univariate GARCH and Monte Carlo Simulation Models, International Journal of Energy Economics and Policy, 11(1), 281-292, DOI: https://doi.org/10.32479/ijeep.10374
[12] Abroon Qazi, Abdulrahim Shamayleh, Sameh El-Sayegh, Steven Formaneck, (2020) Prioritizing risks in sustainable construction projects using a risk matrix-based Monte Carlo Simulation approach, Elsevier Science Direct Sustainable Cities and Society journal, 2210-6707, https://doi.org/10.1016/j.scs.2020.102576
[13] Aminatunisa S., Diarnia M.S.S., Gultom Y., Enjelika M., Mazmur S.P., Evta I., (2019), Penerapan Metode Monte Carlo Untuk Simulasi Sistem Antrian Service Sepeda Motor Berbasis Web, Jurnal Sistem Informasi Ilmu Komputer Prima, Vol. 2 No. 2, Doi : https://doi.org/10.34012/jusikom.v2i2.442
[14] Geni B. Y., J.Santony., and Sumijan, (2019) Prediksi Pendapatan Terbesar pada Penjualan Produk Cat dengan Menggunakan Metode Monte Carlo, J. Inform. Ekon. Bisnis, vol. 1, no. 4, pp. 15–20, , doi: https://doi.org/10.37034/infeb.v1i4.5
[15] J. Santony, (2019), Prediksi Pajak Mineral Non Logam dan Batuan dengan Metode Monte Carlo, J. Informasi & Teknologi., vol. 1, no. 4, pp. 32-37, https://doi.org/ 10.37034/jidt.v1i4.33
[16] Manurung K. H. and Santony J., (2019), Simulasi Pengadaan Barang menggunakan Metode Monte Carlo, J. Sistim Inf. dan Teknol., vol. 1, no. 3, pp. 7–11, https://doi.org/ 10.35134/jsisfotek.v1i3.3
[17] Putra D.E., J.Santony., and G. W. Nurcahyo, (2019), Prediksi Pengeluaran Anggaran Operasional Perguruan Tinggi Swasta Dengan Menggunakan Metode Monte Carlo, J. Sistem Informasi Robotik., vol. 4, no. 2, pp. 49-60. https://doi.org/ /jsir.v4i2.50
[18] Safitri D., Dahdah.S.S., and Andesta.D., (2019), Penerapan Metode Monte Carlo Pada Perencanaan Jumlah Produksi Pestisida, Jurnal Sistem Dan Teknik Industri, vol.1, no.1, pp. 20-43. https://doi.org/10.30587/justicb.v1i1.2043
[19] Yicheng Zhou, Zhenzhou Lu, Kai Cheng, Wanying Yun, (2019), A Bayesian Monte Carlo-based method for efficient computation of global sensitivity indices, Elsevier Mechanical Systems and Signal Processing, 498-516, https://doi.org/10.1016/j.ymssp.2018.08.015
[20] Wijaya F.S., and Sulistio.H., (2019), Penerapan Metode Monte Carlo Pada Penjadwalan Proyek Serpong Garden Apartment, Jurnal Mitra Teknik Sipil, Vol. 2, No.3, 2019: pp. 189-198. https://doi.org/10.24912/jmts.v2i3.5828