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All Journal CAUCHY: Jurnal Matematika Murni dan Aplikasi Indonesian Journal of Applied Statistics Jurnal Ketenagakerjaan Prosiding Seminar Nasional Official Statistics
Hasrat Ifolala Zebua
Badan Pusat Statistik Kabupaten Nias
Agriculture, Biological Sciences & Forestry Humanities Computer Science & IT Earth & Planetary Sciences Economics, Econometrics & Finance Environmental Science Law, Crime, Criminology & Criminal Justice Mathematics Social Sciences

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Spatial Autoregressive Model of Tuberculosis Cases in Central Java Province 2019 Zebua, Hasrat Ifolala; Jaya, I Gede Nyoman Mindra
CAUCHY Vol 7, No 2 (2022): CAUCHY: Jurnal Matematika Murni dan Aplikasi (May 2022) (Issue in Progress)
Publisher : Mathematics Department, Maulana Malik Ibrahim State Islamic University of Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18860/ca.v7i2.13451

Abstract

Tuberculosis is an infectious disease caused by infection with the bacterium Mycobacterium tuberculosis. Central Java is one of the three provinces with the highest tuberculosis cases in Indonesia. Some of the risk factors used in this research are the spatial lag of the number of tuberculosis cases representing the agent component, the morbidity rate representing the host component, population density, proper sanitation, and proper drinking water which represent environmental components. This study uses the Spatial Autoregressive (SAR) model. The SAR model is a regression model where the response variable has a spatial correlation. The estimation method usually used in SAR model is maximum likelihood. The value of Moran's I on the number of tuberculosis cases in Central Java is 0.499 and is significant, which means that there is a positive spatial autocorrelation. The model was chosen based on the LM test and AIC. The best model is the SAR model. The results of the analysis obtained show that the greater the number of tuberculosis cases is influenced by the number of tuberculosis cases in the surrounding area. Proper sanitation has a negative effect, on the contrary, the dense population has a positive effect on the number of tuberculosis cases in the province of Central Java.
Structural Equation Model (SEM) dalam Pemodelan Kemiskinan di Pulau Sumatera Hasrat Ifolala Zebua; Geni Andalria Harefa
Indonesian Journal of Applied Statistics Vol 5, No 1 (2022)
Publisher : Universitas Sebelas Maret

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.13057/ijas.v5i1.50493

Abstract

Poverty is a serious issue that must be addressed immediately by countries in the world, including Indonesia. The Indonesian government has implemented a variety of poverty reduction projects, such as providing education and health insurance. The rising poverty rate is due to the poor quality of education and health care. On Sumatra, there are 5,83 million poor people or 22,06 percent of the total number of poor people in Indonesia. This statistic appears to be quite large, and the government should be concerned about it. Factors causing poverty such as education and health are latent variables that cannot be measured directly. The suitable statistical method used is Structural Equation Model (SEM). In SEM analysis, there are three types of model fit tests: measurement model fit with Confirmatory Factor Analysis (CFA), overall model fit, and structural model fit. The results indicated that the model was fit or suitable for the model's tests. From the SEM model that was formed, it was found that health had a negative and significant effect on poverty and education did not have a significant effect on poverty and 77 percent of the variation in poverty could be explained by the SEM model that was formed.Keywords: poverty; education; health; SEM; CFA
Pemodelan Kemiskinan di Sumatera Utara Menggunakan Regresi Nonparametrik Kernel dan Splines Hasrat Ifolala Zebua
Seminar Nasional Official Statistics Vol 2021 No 1 (2021): Seminar Nasional Official Statistics 2021
Publisher : Politeknik Statistika STIS

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (243.746 KB) | DOI: 10.34123/semnasoffstat.v2021i1.1087

Abstract

The Sustainable Development Goals' main goal is to reduce poverty (SDGs). Low human capital is the cause of poverty. The Human Development Index is one indicator that can be used to assess human capital (HDI). Despite having the largest population on the island of Sumatra, North Sumatra continues to have the fifth highest poverty rate. Because it is flexible and can model data at different levels, this study aims to model poverty with factors that influence it, namely HDI in North Sumatra using nonparametric regression and quantile regression. Kernel regression and smoothing splines are the nonparametric regression techniques used in this study. The optimal bandwidth of the gaussian kernel function with NWE was 2.13512 with GCV 11.78793, modeling with smoothing splines produced an optimal smoothing parameter value of 0.00544 with GCV 47.29301, and modeling with quantile regression smoothing splines produced an optimal smoothing parameter value of 0.11 with a GCV of 3.81497. The smoothing splines quantile regression method is the best method, according to the results of the model comparison, because it has a regression curve that follows the distribution of data relationships and lower GCV and RMSE values.
Ketika Informalitas Bertemu Kemiskinan: Kerentanan Pekerja Informal di Perdesaan dan Perkotaan di Sumatera Utara Damanik, Rolinta; Zebua, Hasrat Ifolala
Jurnal Ketenagakerjaan Vol 20 No 1 (2025)
Publisher : Pusat Pengembangan Kebijakan Ketenagakerjaan Kementerian Ketenagakerjaan Republik Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47198/jnaker.v20i1.463

Abstract

The province of North Sumatra, with its 33 districts/cities, faces significant challenges in managing the informal sector, where a high proportion of the workforce is employed compared to the formal sector. This study aims to analyze the vulnerability of informal workers to poverty in the region, focusing on demographic characteristics, economic factors, and regional disparities. The research employs ordinal logistic regression analysis to identify key determinants of vulnerability to extreme poverty among informal workers. The results show that informal workers, particularly in rural areas, are more likely to experience poverty, with factors such as low education levels, limited access to financing (especially microcredit), lack of luxury goods, short working hours, and marital status being significant contributors. Workers in urban areas have better access to formal sector jobs and are less vulnerable, while those in rural areas are highly dependent on the informal sector with limited opportunities for economic advancement. The study concludes that improving access to financing, education, and employment opportunities in rural areas can reduce the vulnerability of informal workers to poverty. Additionally, policies supporting the growth of the formal sector in rural areas and enhancing social safety nets are essential for reducing economic disparities.
Co-Authors Damanik, Rolinta Geni Andalria Harefa I Gede Nyoman Mindra Jaya