Article Per Year (5 Year)
p-Index From 2020 - 2025
0.444
P-Index
This Author published in this journals
All Journal Desimal: Jurnal Matematika Communication in Biomathematical Sciences
Habsy, Muhammad Yusuf Al
Unknown Affiliation
Education Mathematics Social Sciences

Published : 2 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 2 Documents
Search

 1 
Bayesian spatial data analysis: Application of pneumonia spread in west java Habsy, Muhammad Yusuf Al; Husein, Fulkan Kafilah Al; Yahya, Muhammad Harun; Rachmawati, Ro'fah Nur
Desimal: Jurnal Matematika Vol. 7 No. 1 (2024): Desimal: Jurnal Matematika
Publisher : Universitas Islam Negeri Raden Intan Lampung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24042/djm.v7i1.23154

Abstract

Pneumonia has a notable influence on public health, especially among susceptible demographics like children and the elderly. This respiratory disease can be transmitted through human interaction. Analyzing the spread of the illness within a community requires assessing the characteristics of the community itself. The objective of this research is to describe the distribution of pneumonia cases and their causes in the West Java Province using RStudio software. The analytical method employed is the Integrated Nested Laplace Approximations (INLA) approach, a Bayesian statistical method used for estimation in complex Bayesian models, particularly in hierarchical or nested structure. The sample utilized comprises the entire population, totaling 27 Districts/Cities within West Java Province. The influence of differences in population size, number of people living in poverty, waste production, the quantity of primary healthcare facilities, total number of vehicles, and the count of HIV patients in Cities/Regencies in West Java on the spread of pneumonia will be analyzed. The result of analysis show that the population and number of health centers variables had a significant influence on the mapping of pneumonia disease in each location. And also, the Relative Risk (RR) and Standardized Incidence Ratio (SIR) maps show that some regions have a higher risk of pneumonia compared to other regions. These findings are expected to provide insights for public policies in addressing health issues, particularly in the efforts to prevent and control diseases like pneumonia. Moreover, these results serve as a foundation for further studies regarding other factors that might contribute to the spread of this disease at the local level.
Regresi Multiskala Tertimbang Geografis dan Temporal dengan LASSO dan Adaptif LASSO untuk Pemetaan Kejadian Tuberkulosis di Jawa Barat Habsy, Muhammad Yusuf Al; Rachmawati, Ro'fah Nur; Khotimah, Purnomo Husnul; Natari, Rifani Bhakti; Riswantini, Dianadewi; Munandar, Devi; Izzaturrahim, Muh. Hafizh
Communication in Biomathematical Sciences Vol. 8 No. 1 (2025)
Publisher : The Indonesian Bio-Mathematical Society

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.5614/cbms.2025.8.1.6

Abstract

Tuberculosis (TB) is a global health issue caused by Mycobacterium tuberculosis and can affect any organ of the body, especially the lungs. The trend of TB cases varies between regions, and analytic assessment is required to identify the predictor variables. The purpose of this research is to compare the Multiscale Geographically and Temporally Weighted Regression (MGTWR) and the Geographically and Temporally Weighted Regression (GTWR) method, which both use Gaussian, Exponential, Uniform, and Bi-Square kernel functions, to identify significant variables in each region annually. The MGTWR method has the advantage of using a flexible bandwidth for each observation, that results in more accurate coefficient estimates. The sample used was 27 districts and cities in West Java Province, involving 36 variables divided into 5 dimensions, namely global climate, health, demography, population, and government policy, with a time span of 2019–2022. To overcome the problem of multicollinearity, the approach was carried out using the Least Absolute Shrinkage Selection Operator (LASSO) and Adaptive LASSO methods. In determining the best model, the prioritized criteria are to achieve the highest R2, which indicates the optimal level of model fit, as well as the smallest AIC, which indicates the most efficient model goodness of fit. The best model is MGTWR with LASSO variable selection on the Bi-Square kernel. This model has an R2 of 91.25% and the smallest AIC of 139.868. From the best model, each region emerged with a cluster structure affected by various variables from 2019 to 2022, providing an in-depth understanding of TB mapping that can assist in formulating more effective intervention measures.
Co-Authors Devi Munandar, Devi Husein, Fulkan Kafilah Al Izzaturrahim, Muh. Hafizh Khotimah, Purnomo Husnul Natari, Rifani Bhakti Rachmawati, Ro'fah Nur Riswantini, Dianadewi Yahya, Muhammad Harun