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All Journal SAINSMAT PIONIR: Jurnal Pendidikan MODELING: Jurnal Program Studi PGMI Al-Madrasah: Jurnal Ilmiah Pendidikan Madrasah Ibtidaiyah Ideguru: Jurnal Karya Ilmiah Guru MURHUM : JURNAL PENDIDIKAN ANAK USIA DINI SAINSMAT: Jurnal Ilmiah Ilmu Pengetahuan Alam
Zulhijrah, Zulhijrah
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Biochemistry, Genetics & Molecular Biology Chemistry Decision Sciences, Operations Research & Management Education Languange, Linguistic, Communication & Media Physics Other

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Risk Factors For Stunting Incidence In Urban and Rural Areas Of Indonesia Using Bayesian Spatial CAR Zulhijrah, Zulhijrah; Rifaldi, Destriana Aulia; Hapsari, Nimas Ayu; Sulaeman, Sulthan Naufal; Aidi, Muhammad Nur
Sainsmat : Jurnal Ilmiah Ilmu Pengetahuan Alam Vol 14, No 2 (2025): September
Publisher : Fakultas Matematika dan Ilmu Pengetahuan Alam Universitas Negeri Makassar

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35580/sainsmat142712542025

Abstract

Stunting is a chronic growth disorder in children under five that requires evidence-based interventions. To understand the factors that contribute to stunting in different regions of Indonesia, Bayesian Conditional Autoregressive (CAR) modeling was used to estimate the relative risk of stunting. The analysis showed that the Besag-York-Mollié (BYM) model with covariates provided the best results in estimating the risk of stunting. The data for this study were obtained from the 2018 Basic Health Research Survey. In urban areas, immunization coverage has a significant effect on stunting risk, while in rural areas, in addition to immunization, vitamin supplementation coverage and poverty level are also significant factors. Based on the modeling, the region with the highest risk in urban areas is West Sulawesi Province with a relative risk of 1.638, while the lowest is Bali Province with 0.564. In rural areas, Papua Province had the highest risk of 1.820, while North Sulawesi Province had the lowest risk of 0.599. These findings suggest that immunization coverage is instrumental in reducing stunting, both in urban and rural areas. In addition, in rural areas, increasing vitamin supplementation coverage and decreasing poverty levels can help reduce the risk of stunting. Therefore, intervention policies should be tailored to the characteristics of each region to be more effective in addressing stunting in Indonesia.
Time Series Intervention Analysis With Gradual Impact Function A Case Study Of Railway Passenger Volume In Java Island Zulhijrah, Zulhijrah; Isnaini, Mardatunnisa; Angraini, Yenni; Notodiputro, Khairil Anwar; Mualifah, Laily Nissa Atul
Sainsmat : Jurnal Ilmiah Ilmu Pengetahuan Alam Vol 14, No 2 (2025): September
Publisher : Fakultas Matematika dan Ilmu Pengetahuan Alam Universitas Negeri Makassar

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35580/sainsmat142774742025

Abstract

Java Island has been significantly impacted by the COVID-19 pandemic, which started in March 2020. This study aims to analyze the impact of the pandemic on the volume of railway passengers’ volume with a time series approach using an interventional ARIMA model. The data used is the number of monthly passengers from 2015 to 2024. Initial modeling on data before the pandemic produced the best model, namely ARIMA (0,2,1). To measure the impact of the pandemic, a gradual step intervention function is used which represents the gradual effect of the event. The estimation results show that the ARIMA (0,2,1) model with a gradual step intervention function is able to provide more accurate forecasting results, with a MAPE value of 18.39%. This model effectively captures changes in mobility patterns due to the pandemic, especially in the post-intervention recovery phase. The findings make an important contribution to transportation policy evaluation and future strategic planningKeywords: Time Series, ARIMA  Intervention, Gradual Function, Railway 
Co-Authors Andi Prastowo Bella, Sinta Ermis Suryana Hapsari, Nimas Ayu Hulkin, M. Hulkin, Muhammad Irawan, M. Ferry Irawan, Muhammad Ferry Isnaini, Mardatunnisa Larasati, Nabila Joti Mualifah, Laily Nissa Atul Muhammad Nur Aidi Nurhijatina, Havifa Rifaldi, Destriana Aulia Saputri, Hera Apriliana Shaleh, Shaleh Sulaeman, Sulthan Naufal Yenni Angraini Yulia Yulia