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Zero : Jurnal Sains, Matematika, dan Terapan
Published by Universitas Islam Negeri Sumatera Utara
ISSN : 2580569X     EISSN : 25805754     DOI : 10.30829
Core Subject : Science, Education, Agriculture,
Agriculture, Biological Sciences & Forestry Chemistry Mathematics Physics
Arjuna Subject : -
Articles 293 Documents
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Spatial Determinants of Stunting in East Java: A Comparative Assessment between OLS and Spatial Models Approach Mieke Nurmalasari; Dede Yoga Paramartha; Nandya Rezky Utami; Dinda Fahrani; Andrey Sinlay; Tria Saras Pertiwi; Setia Pramana
ZERO: Jurnal Sains, Matematika dan Terapan Vol 10, No 2 (2026): Zero: Jurnal Sains Matematika dan Terapan
Publisher : UIN Sumatera Utara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30829/zero.v10i2.30392

Abstract

Stunting remains a critical challenge in Indonesia, aligning with the 2030 SDGs. This study examines the spatial patterns of stunting prevalence across 29 districts and 9 cities in East Java Province using 2022 data. Classic Ordinary Least Squares (OLS), Spatial Autoregressive (SAR), and Spatial Error Models (SEM) were deployed. Baseline OLS shows that Gender Development Index, Poverty, GRDP per Capita, Access to Adequate Sanitation, and nurse density simultaneously exert a significant joint effect on stunting. Although Moran's I indicated marginal evidence of positive spatial autocorrelation in the OLS residuals (p = 0.0556), the SAR and SEM specifications yielded non-significant spatial parameters (ρ and λ) and no meaningful AIC improvement over OLS. Local Indicators of Spatial Association (LISA) further show that, unlike sanitation access, stunting itself does not form a statistically significant High-High cluster, suggesting that the observed residual spatial dependence may be partly accounted for by the included structural covariates, although the study's small sample size (n = 38) may also limit the power to detect spatial effects directly. Consequently, the classical OLS specification was retained as the preferred model for inference, with SAR and SEM results reported as spatial diagnostics.
Comparing Volatility and Neural Network Models for Forecasting Bitcoin Prices in Indonesian Rupiah Raihan Akbar; I Wayan Mangku; Bib Paruhum Silalahi
ZERO: Jurnal Sains, Matematika dan Terapan Vol 10, No 2 (2026): Zero: Jurnal Sains Matematika dan Terapan
Publisher : UIN Sumatera Utara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30829/zero.v10i2.30726

Abstract

As Indonesia emerges among Southeast Asia's largest cryptocurrency markets, forecasting Bitcoin priced in rupiah (BTC/IDR) has gained practical relevance, yet most research focuses on BTC/USD and overlooks domestic macroeconomic conditions. This study tests whether augmenting an Exponential GARCH with Exogenous Variables (eGARCH-X) model with a Nonlinear Autoregressive network with eXogenous inputs (NARX) improves forecasts of weekly BTC/IDR log returns, using IHSG, USD/IDR, gold price, and BI rate as exogenous inputs. Using 437 weekly observations from January 2018 to June 2026, the hybrid was benchmarked against eGARCH-X, NARX, and a restricted eGARCH without exogenous terms across three splits, evaluated with RMSE, MAE, directional accuracy, and the Diebold­­­­ Mariano test, with each network comparison replicated over ten random initializations. The eGARCH identified a well-determined conditional variance process: volatility was highly persistent (0.976), responded asymmetrically to the sign of innovations (0.038, p = 0.010), and displayed heavy tails, with standardized residuals passing all diagnostics. No model differed significantly from the eGARCH-X benchmark, directional accuracy was indistinguishable from chance throughout (43.7–55.5%), and the exogenous regressors were insignificant both inSeaksample and out-of-sample. Weekly BTC/IDR returns thus appear tractable in their variance but close to unforecastable in their conditional mean, locating the practical value of these models in volatility estimation rather than directional prediction.
Nighttime Lights in Unit-Level Small Area Estimation for Estimating per Capita Expenditure Diaztri Hazam; Erfiani Erfiani; Anang Kurnia
ZERO: Jurnal Sains, Matematika dan Terapan Vol 10, No 2 (2026): Zero: Jurnal Sains Matematika dan Terapan
Publisher : UIN Sumatera Utara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30829/zero.v10i2.28716

Abstract

The most widely used auxiliary variables for estimating per capita expenditure using small area estimation (SAE) are from National Socio-Economic Survey (SUSENAS) or Village Potential (PODES) data. Another alternative is remote sensing, which can quickly and cheaply identify area characteristics, such as nighttime lights (NTL). This study will compare the SAE model with auxiliary variables using PODES data, NTL data, and a combination of both. The method used is a unit-level SAE model with log-transformation to estimate per capita expenditure at the subdistrict level in Bandung Regency.  Model performance was assessed using Relative Root Mean Squared Error (RRMSE), Akaike Information Criterion (AIC), and Bayesian Information Criterion (BIC). Estimation from all models have a similar range. The model with auxiliary variables using PODES data and combined data has similar RRMSE, AIC, and BIC. The model with auxiliary variables using only NTL data has the smallest RRMSE, AIC, and BIC.

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