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All Journal JTAM (Jurnal Teori dan Aplikasi Matematika) Journal of Maternal and Child Health Jurnal Sains, Teknologi dan Komputer
Jamaluddin, Sri Rezki Wahdania
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Agriculture, Biological Sciences & Forestry Chemistry Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Mathematics Medicine & Pharmacology Public Health

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Relationships between Duration of Breastfeeding, Child Nutritional Status, and Development Jamaluddin, Sri Rezki Wahdania; Faradilah, Andi; Fitriani, Rini
Journal of Maternal and Child Health Vol. 6 No. 3 (2021)
Publisher : Masters Program in Public Health, Universitas Sebelas Maret, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (33.795 KB) | DOI: 10.26911/thejmch.2021.06.03.05

Abstract

Background: Breast milk is one of the factors that can affect growth and development. WHO is promoted exclusive breastfeeding and also encourages mother to raise breastfeeding until the children reach 2 year of age. This study was aimed to examine the relationships between the duration of breastfeeding, child nutritional status and development. Subjects and Method: This was an analytic obser­vational study with a cross sectional design. The study was con­duc­ted in three health centers, in Makassar, South Sulawesi, Indonesia, from December 2019 to January 2020. A total of 200 mot­hers with children aged 1-3 years old were selected by purposive sampling. The depen­dent variables were child nutritional status and child development. The independent variable was duration of breast­feeding. Child development was measured using developmental pre-screening question­naire. The other variables were collected by questionnaire. The relationships between vari­ables were analyzed by Pearson correlation. Results: Duration of breastfeeding was not associated with child nutritional status (r= -0.01; p= 0.970). Duration of breastfeeding increased child development, but it was statis­tically not significant (r= 0.04; p= 0.550). Conclusion: Duration of breastfeeding is not associated with child nutritional status. It increases child development, but it is statis­ti­cally not significant.
Pemodelan Data Angka Kematian Bayi Menggunakan Regresi Robust Ahmad Husain; Jamaluddin, Sri Rezki Wahdania
Jurnal Sains, Teknologi & Komputer Vol. 1 No. 1 (2024): Jurnal Sains, Teknologi & Komputer (SAINTEK)
Publisher : Lembaga Riset Mutiara Akbar

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56495/saintek.v1i1.326

Abstract

The ordinary least square method (MKT) is a classic method to estimate parameters in regression modelling. The MKT has classical assumption, where these assumptions must be fulfilled. One of the causes of violation of classical assumptions is the presence of outlier observations. Robust regression is an alternative for correcting parameters in data that indicate outliers. In this article, we apply robust regression using estimation-M in data modelling of infant mortality rates that occur in West Java. Based on the mean squared error (MSE) criterion in the estimation-M, it is obtained that the Huber approach has a lower MSE than Tukey Bisquare. The results of estimation using Huber show that only the low number of births has a significant effect on infant mortality in West Java, with an effect of 2.047.
Bayesian Logistic Regression for Inhomogeneous Poisson Point Process: A Case Study of Post-Harvest Facilities in Sidenreng Rappang Husain, Ahmad; Sam, Marwan; Jamaluddin, Sri Rezki Wahdania
JTAM (Jurnal Teori dan Aplikasi Matematika) Vol 10, No 2 (2026): April
Publisher : Universitas Muhammadiyah Mataram

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31764/jtam.v10i2.35956

Abstract

Understanding the spatial distribution of post-harvest infrastructure is crucial for improving the efficiency and resilience of agricultural supply chains, particularly in major food-producing regions. This study aims to extend the estimating equations based on the logistic regression likelihood within the Bayesian framework to model the spatial intensity of an Inhomogeneous Poisson Point Process (IPP). The proposed approach integrates prior information into the logistic regression likelihood by constructing posterior distributions, enabling a more comprehensive inference by quantifying parameter uncertainty. In contrast to conventional maximum likelihood (ML) estimation, which produces only point estimates, the Bayesian method provides a probabilistic characterization of parameter estimates using the Markov Chain Monte Carlo (MCMC) approach, specifically the Gibbs Sampling algorithm, to approximate posterior distributions. The methodological framework is applied to the spatial distribution of post-harvest rice facilities in Sidenreng Rappang Regency, Indonesia. The analysis is based on georeferenced observational data obtained from local goverment records and agricultural statistics, processed usign Geographic Information System (GIS) tools and statistical software. Spatial covariates include the proportion of paddy field area per village (Z_1), rice producing area (Z_2), and distance to the nearest Bulog warehouse (Z_3 ). The results indicate that Z₁ and Z₃ significantly affect the spatial intensity of post-harvest facilities, where areas with larger paddy field proportions are more likely to host such facilities, while increasing distance from Bulog reduces the likelihood of facility presence. The posterior trace and density plots demonstrate good convergence and mixing, confirming the reliability of the Gibbs Sampling procedure. Model comparison through the Akaike Information Criterion (AIC) and likelihood values shows that the Bayesian approach yields a substantially lower AIC, ten times smaller than the ML-based logistic regression, indicating superior model fit and computational efficiency. The findings suggest that integrating Bayesian inference into the IPP logistic framework enhances model interpretability and robustness, particularly in accounting for uncertainty and prior knowledge. The study underscores the practical importance of spatial modeling for agricultural infrastructure planning and offers a flexible computational framework applicable to other spatial point pattern analyses across diverse domains.
Co-Authors Faradilah, Andi Husain, Ahmad Indradewa, Rhian Rini Fitriani Sam, Marwan