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All Journal Al-Jabar : Jurnal Pendidikan Matematika BAREKENG: Jurnal Ilmu Matematika dan Terapan Jurnal Aplikasi Statistika & Komputasi Statistik Journal of Data Science and Its Applications Majalah Ilmiah Matematika dan Statistika (MIMS) Prosiding Seminar Nasional Official Statistics
Novi Hidayat Pusponegoro
Politeknik Statistika STIS, Indonesia
Humanities Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Energy Engineering Mathematics Mechanical Engineering Physics Social Sciences Transportation

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Geo-additive Models in Small Area Estimation of Poverty Novi Hidayat Pusponegoro; Anik Djuraidah; Anwar Fitrianto; I Made Sumertajaya
Journal of Data Science and Its Applications Vol 2 No 1 (2019): Journal of Data Science and Its Applications
Publisher : Telkom University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21108/jdsa.2019.2.15

Abstract

Spatial data contains of observation and region information, it can describe spatial patterns such as disease distribution, reproductive outcome and poverty. The main flaw in direct estimation especially in poverty research is the sample adequacy fulfilment otherwise it will produce large estimate parameter variant. The Small Area Estimation (SAE) developed to handle that flaw. Since, the small area estimation techniques require “borrow strength” across the neighbor areas thus SAE was developed by integrating spatial information into the model, named as Spatial SAE. SAE and spatial SAE model require the fulfilment of covariate linearity assumption as well as the normality of the response distribution that is sometimes violated, and the geo-additive model offers to handle that violation using the smoothing function. Therefore, the purpose of this paper is to compare the SAE, Spatial SAE and Geo-additive model in order to estimate at sub-district level mean of per capita income of each area using the poverty survey data in Bangka Belitung province at 2017 by Polytechnic of Statistics STIS. The findings of the paper are the Geo-additive is the best fit model based on AIC, and spatial information don't influence the estimation in SAE and spatial SAE model since they have the similar estimation performance.
Pendugaan Standard Error dan Confidence Interval Koefisien Gini dengan Metode Bootstrap: Dwi Indri Arieska; Novi Hidayat Pusponegoro
Jurnal Aplikasi Statistika & Komputasi Statistik Vol 8 No 2 (2016): Journal of Statistical Application and Computational Statistics
Publisher : Pusat Penelitian dan Pengabdian Masyarakat Politeknik Statistika STIS

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (1234.301 KB) | DOI: 10.34123/jurnalasks.v8i2.50

Abstract

Income inequality is one of economic development indicators. As a kind of inequality indicators which is commonly used in Indonesia, gini coefficient is published as a point estimation. This estimation are lacking in its function as an estimator because it doesn’t considerate the probability accuration of the estimate value.Thus, the confidence interval estimation is needed as a comprehensive estimator. The objective of this study is estimate the standard errors and confidence intervals Gini coefficients with the bootstrap method. This study used National Social Economics Household Survey for West Papua Province in 2013. The Gini coefficient that used is a bias-corrected gini coefficient as consideration the bias in the calculation. The standard error of bias-corrected gini coefficient in West Papua is carried out of two data, which are the original sample and resample nonparametric bootstrap method. This research found out that bootstrap-t confidence interval confidence interval is the best confidence interval since it has the smallest standard error and shortest interval.
Penerapan geographically weighted regression (GWR) dalam menganalisis kemiskinan di Pulau Jawa tahun 2022 Novaldi, Jeremia; Pusponegoro, Novi Hidayat
Majalah Ilmiah Matematika dan Statistika Vol. 24 No. 1 (2024): Majalah Ilmiah Matematika dan Statistika
Publisher : Jurusan Matematika FMIPA Universitas Jember

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.19184/mims.v24i1.42717

Abstract

Poverty is a priority issue for Indonesia. However, efforts to eradicate poverty in Indonesia have always failed to fulfill the targets set out in the RPJMN. Java Island, which is known as the center of the economy, has not yet solved this poverty problem. In 2022, the majority of provinces on Java Island still have a higher poverty rate than the target in the 2020-2024 RPJMN, which is 6.5-7 percent. Therefore, the objective of this study is to analyze the relationship between the socio-economic conditions of the society, represented by aspects of education, health, and income, and poverty in 119 regencies or cities on Java Island. Geographically weighted regression (GWR) with a fixed bi-square kernel is applied to fulfill the study objective. The results showed that poverty is affected by RLS in 84 districts/cities, influenced by AHH in 15 regencies or cities, and influenced by AHH and income per capita in 8 regencies or cities. However, these three variables do not affect the poverty rate in the 12 regencies or cities. Keywords: Poverty, GWR, Spatial Analysis, SocioeconomicMSC2020: 91B72
Analisis Spasial Determinan Ketimpangan Distribusi Pendapatan Antarwilayah Tahun 2022 Falahuddin, Muhammad Ilzam; Zukhrufah, Awika Yuliati; Sitanggang, Claudia Janefer Romora; Pusponegoro, Novi Hidayat
Seminar Nasional Official Statistics Vol 2024 No 1 (2024): Seminar Nasional Official Statistics 2024
Publisher : Politeknik Statistika STIS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34123/semnasoffstat.v2024i1.2147

Abstract

National development is a step that leads to an increase in the welfare of society. The success of development is measured through gross domestic product (GDP) which illustrates the level of economic growth. A high GDP increases per capita income. Economic growth must be balanced with the stability of the number of poor people to avoid income inequality. Java is the center of government and economy in Indonesia. Java is the largest contributor to Indonesia's GDP. The large contribution of GDP in Java is accompanied by income inequality in the region. This study aims to identify the existence of spatial effects on income inequality among districts/cities in Java Island and analyze the factors that affect income inequality in Java Island by considering spatial effects. The analysis shows that HDI, construction cost index, minimum wage, and per capita income affect income distribution inequality in Java. There is spatial interaction among 119 districts/cities in Java Island with Spatial Error Model (SEM).
Pemodelan Status Ketertinggalan Perekonomian Regional Menggunakan Geographically Weighted Logistic Regression (GWLR) Bani Syafii, Ghulam An-Nabalah; Hanifah, Ria Dini; Arisanti, Rohimma; Pusponegoro, Novi Hidayat
Seminar Nasional Official Statistics Vol 2024 No 1 (2024): Seminar Nasional Official Statistics 2024
Publisher : Politeknik Statistika STIS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34123/semnasoffstat.v2024i1.2208

Abstract

The goal of economic development is to improve the well-being of the people. However, economic development, especially in developing countries, including Indonesia, has been hampered by interregional disparities. This inequality leads to a grouping of economically backward people. This research aims to find out the general picture and identify the presence of spatial aspects on the status of the regional economy backwardness and the factors that influence it from the production side. Because there are indications of dependency and spatial heterogeneity, the study uses the Geographically Weighted Logistic Regression (GWLR) model. The results show that the rise in capital and the decrease in labor will lower the tendency for one to be categorized as a backward region. Therefore, investment needs to be intensified in industries in Indonesia and it is necessary to improve the quality of technologically literate human resources to streamline the production process.
Utilize imagery and crowdsourced data on spatial employment modelling Pusponegoro, Novi Hidayat; Rachmawati, Ro'fah Nur; Siallagan, Maria A. Hasiholan; Wicaksono, Ditto Satrio
Al-Jabar: Jurnal Pendidikan Matematika Vol 15 No 2 (2024): Al-Jabar: Jurnal Pendidikan Matematika
Publisher : Universitas Islam Raden Intan Lampung, INDONESIA

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24042/ajpm.v15i2.24518

Abstract

Background: Spatial employment modeling investigates employment distribution, patterns, influencing factors, neighboring area impact, and regional policy efficacy. Conventional studies often rely on traditional data sources, which may overlook critical employment-related phenomena. In 2022, Java recorded the lowest labor absorption rate in Indonesia, necessitating a new approach.Aim: This study combines imagery, crowdsourced data, and official statistics to identify factors influencing labor absorption in Java Island.Method: Geographically Weighted Regression (GWR) was employed to account for spatial effects in the data.Results: The model reveals that nighttime light intensity in urban and agricultural areas, along with environmental quality, significantly enhances labor absorption across Java. Internet facilities, universities, and the number of micro and small industries also positively influence most districts/cities.Conclusion: Incorporating new data sources offers valuable insights for understanding employment patterns and can enrich employment research frameworks.
SPATIAL REGRESSION APPROACH TO MODELLING POVERTY IN JAVA ISLAND 2022 Siallagan, Maria A. Hasiholan; Pusponegoro, Novi Hidayat
BAREKENG: Jurnal Ilmu Matematika dan Terapan Vol 18 No 3 (2024): BAREKENG: Journal of Mathematics and Its Application
Publisher : PATTIMURA UNIVERSITY

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30598/barekengvol18iss3pp1765-1778

Abstract

Geographically Weighted Regression (GWR) model is a powerful tool for analyzing spatial patterns in data. However, the standard form of a spatial model that uses a single bandwidth calibration may be unrealistic because the response-predictor relationship may be either linear or nonlinear. To address this issue, the Multiscale GWR (MSGWR) model offers improved model performance by employing Generalized Additive Model (GAM) with varying bandwidth or smoothing function for each covariate in the model. This research aims to analyze the Percentage of Poor Population (PPP) on Java Island in 2022 using the geospatial models and related socioeconomic and demographic attributes, such as Open Unemployment Rate, Human Development Index, Labor Force Participation Rate, and GRDP Per capita to identify the best model in explaining the spatial pattern and to find out the determinant of PPP on Java Island in 2022. This study uses secondary data from Statistics Indonesia. The findings reveal that the MSGWR model provides the highest R2 and smallest AICc value compared to single bandwidth models, specifically the GWR and MXGWR models. Furthermore, the MSGWR model indicates that HDI has a significant negative effect on PPP, whereas LFPR has a significant positive effect on PPP across all districts in Java Island in 2022.
Co-Authors Anik Djuraidah Arisanti, Rohimma Bani Syafii, Ghulam An-Nabalah Dwi Indri Arieska Falahuddin, Muhammad Ilzam Fitrianto, Anwar Hanifah, Ria Dini I Made Sumertajaya Novaldi, Jeremia Rachmawati, Ro'fah Nur Siallagan, Maria A. Hasiholan Sitanggang, Claudia Janefer Romora Wicaksono, Ditto Satrio Zukhrufah, Awika Yuliati