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All Journal BAREKENG: Jurnal Ilmu Matematika dan Terapan Jurnal Aplikasi Statistika & Komputasi Statistik
Dewanti, Maria Setya
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Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Energy Engineering Mathematics Mechanical Engineering Physics Transportation

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Modeling the Stunting Prevalence Rate in Indonesia Using Multi-Predictor Truncated Spline Nonparametric Regression Alda Fuadiyah Suryono; Kurniawan, Ardi; Widyangga, Pressylia Aluisina Putri; Dewanti, Maria Setya
Jurnal Aplikasi Statistika & Komputasi Statistik Vol 16 No 1 (2024): Jurnal Aplikasi Statistika & Komputasi Statistik
Publisher : Politeknik Statistika STIS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34123/jurnalasks.v16i1.719

Abstract

Introduction/Main Objectives: Stunting is the impaired growth and development that children experience from poor nutrition, repeated infection, and inadequate psychosocial stimulation. Background Problems: Based on data from the National Nutrition Status Survey (SSGI) in 2022, the prevalence of stunting in Indonesia was 21.6%, which is still above the WHO standard of below 20%. Novelty: This study was conducted with the aim of analysing the factors that influence the stunting prevalence rate in Indonesia using multi-predictor truncated spline nonparametric regression. Research Methods: The research data is secondary data taken from Health Statistics 2022 with response variables in the form of stunting prevalence. Finding Result: Based on the analysis, the best model to model the stunting prevalence rate is a multi-predictor truncated spline with three knots. In addition, it was found that four predictor variables which are the percentage of infants under 6 months old receiving exclusive breastfeeding, the average age of a mother's first pregnancy, the percentage of married women aged 15-49 using contraception, and the percentage of mothers who gave birth to a live child in the past two years and initiated early breastfeeding had a significant effect simultaneously and partially on the stunting prevalence rate in Indonesia.
PREDICTION OF NATURAL GAS PRICES ON THE NEW YORK MERCANTILE EXCHANGE BASED ON A PULSE FUNCTION INTERVENTION ANALYSIS APPROACH Sediono, Sediono; Saifudin, Toha; Dewanti, Maria Setya; Azis, Aurelia Islami
BAREKENG: Jurnal Ilmu Matematika dan Terapan Vol 19 No 4 (2025): BAREKENG: Journal of Mathematics and Its Application
Publisher : PATTIMURA UNIVERSITY

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30598/barekengvol19iss4pp2647-2660

Abstract

Natural gas is a key energy commodity with significant global economic impact, and its pricing is influenced by factors like weather, energy policies, geopolitics, and supply-demand balance. The Russia-Ukraine conflict disrupted Russia’s gas exports, causing price volatility and affecting global markets, including Indonesia. This has heightened the need for accurate price prediction to support policy and investment decisions. Previous studies show ARIMA-GARCH models predict well but need pulse function intervention for sudden shocks. This study aims to apply pulse function intervention analysis, which captures the immediate effects of external events on time-series data, to improve the precision of natural gas price forecasts, aiding government and industry decision-makers. The optimal intervention model for predicting natural gas prices on the New York Mercantile Exchange is the Probabilistic ARIMA (0,2,1) with a pulse function intervention order of b=0, r=2, and s=0. Using this model with the pulse function intervention approach yields consistent fluctuation patterns over time and achieves a MAPE value of 12.2586%, indicating that the model provides good predictive accuracy.
Co-Authors Alda Fuadiyah Suryono Ardi Kurniawan Azis, Aurelia Islami Sediono, Sediono Toha Saifudin Widyangga, Pressylia Aluisina Putri