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Timbang Sirait
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Spatial Autoregressive Modeling on Linear Mixed Models for Dependency Between Regions Timbang Sirait
Aceh International Journal of Science and Technology Vol 12, No 1 (2023): April 2023
Publisher : Graduate Program of Syiah Kuala University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.13170/aijst.12.1.30403

Abstract

This study develops a linear mixed model (LMM) that includes spatial effects between regions with a spatial autoregressive model (SAR model). Between observations (regions) on that LMM are usually assumed to be independent. However, these assumptions are not always fulfilled due to dependency between regions. There are two important parts in spatial modeling: spatial dependence and spatial heterogeneity. In this study, we are concerned with the spatial lag or SAR models because dependency between variables of interest is easier to predict. On the other hand, all observations are real and can be directly seen from the data patterns. In addition, as a challenge for researchers to find all estimators while the values of the spatial dependence, sampling variance, and component variance are all unknown. This study aims to find all parameter estimators using a numerical approach and exact solutions. All exact estimators obtained are consistent estimators.
Property Crime in Java Island 2022 based on Demography and Socioeconomic Aspects using Spatial Analysis Approach Fabian La Wima Vallessy; Timbang Sirait
Limits: Journal of Mathematics and Its Applications Vol. 22 No. 1 (2025): Limits: Journal of Mathematics and Its Applications Volume 22 Nomor 1 Edisi Ma
Publisher : Pusat Publikasi Ilmiah LPPM Institut Teknologi Sepuluh Nopember

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12962/limits.v22i1.3371

Abstract

Property crime is the most common type of crime in Indonesia with the most rapid increasing in 2022. Java is the island with the highest magnitude of 65.85% if it is compared to the previous year and accounts for more than one third of the total cases in Indonesia. This study aims to determine an overview of these types of criminal offenses and the variables that affect them spatially. The analysis method uses in this study is descriptive analysis which will followed by inferential analysis, namely spatial analysis using Geographically Weighted Negative Binomial Regression (GWNBR). Based on this research, it is found that there are four regional groupings with variables that significantly affect all regions, namely life expectancy and Gini ratio. Meanwhile, there are variables that affect some regions, namely mean years schooling and total population. In addition, it is found that Geographically Weighted Negative Binomial Regression is better used than negative binomial regression in modeling property crime in Java Island in 2022.
Variables that Influence Urban Sprawl in DKI Jakarta, West Java and Banten Provinces in 2020 Azzahra Dhisa Khamila; Timbang Sirait
Limits: Journal of Mathematics and Its Applications Vol. 22 No. 1 (2025): Limits: Journal of Mathematics and Its Applications Volume 22 Nomor 1 Edisi Ma
Publisher : Pusat Publikasi Ilmiah LPPM Institut Teknologi Sepuluh Nopember

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12962/limits.v22i1.3372

Abstract

DKI Jakarta, West Java and Banten provinces are the place of two large metropolitan areas in Indonesia that are interconnected. As a result, these areas have a high level of urbanization which can lead to urban sprawl. Urban Sprawl can cause various negative impacts, especially on the environment. Therefore, it is necessary to minimize urban sprawl, one of many ways is by analyzing the variables that affect urban sprawl. Several studies on spatial analysis of urban sprawl have been made extensively using satellite imagery data, one of them states that NDBI can capture patterns, characteristics and the causes of urban sprawl. However, research that utilizes NDBI as a variable approach for the urban sprawl has never been conducted in Indonesia. Therefore, this research was conducted with the aim of analyzing the effect of variables that indicated influence urban sprawl in the provinces of DKI Jakarta, West Java and Banten using spatial analysis. The results show that the average NDBI value is high in urban areas where the majority are in DKI Jakarta province. The variables that significantly influence urban sprawl are percentage of migrant population and tertiary sector of GRDP. By focusing on these variables, the government can make policies to minimize and control urban sprawl that occur in their area.
Ketimpangan Pendapatan antara Wilayah Perkotaan dan Perdesaan di Indonesia tahun 2019-2023 Rika Lusiana Simbolon; Timbang Sirait
Limits: Journal of Mathematics and Its Applications Vol. 22 No. 2 (2025): Limits: Journal of Mathematics and Its Applications Volume 22 Nomor 2 Edisi Ju
Publisher : Pusat Publikasi Ilmiah LPPM Institut Teknologi Sepuluh Nopember

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12962/limits.v22i2.3912

Abstract

One of the biggest challenges in income inequality is the existence of income inequality between urban and rural areas, especially in developing countries. In 2019-2023, the average income of urban residents was 1.7 times higher than that of rural residents. The existence of income gaps between urban and rural areas can hinder equitable economic growth and worsen social inequality. Currently, household income is greatly influenced by the integration between the digital economy and the real economy. The development of the digital economy plays a role in increasing economic growth. However, its acceleration risks worsening social exclusion, inequality between groups, and wealth concentration. Therefore, this study aims to analyze the influence of the digital economy on income inequality between urban and rural areas in 33 provinces in Indonesia in 2019-2023 using a panel regression model. The results of measuring income inequality using the Theil L index show that the contribution of income inequality between urban and rural areas to national inequality has decreased in 2019-2023. The development of the digital economy in terms of infrastructure, business climate, research and innovation, and funding/investment has a significant influence in reducing income inequality between urban and rural areas. Meanwhile, in terms of human resources, it has a significant influence in increasing income inequality between urban and rural areas.
Pemodelan Ekspor Turunan CPO Indonesia ke China Periode 2007-2023 dengan ARDL Clarissa Azarine; Timbang Sirait; Nurvita Trianasari
Limits: Journal of Mathematics and Its Applications Vol. 23 No. 1 (2026): Limits: Journal of Mathematics and Its Applications Volume 23 Nomor 1 Edisi Ap
Publisher : Pusat Publikasi Ilmiah LPPM Institut Teknologi Sepuluh Nopember

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12962/limits.v23i1.7936

Abstract

Exports play a vital role in supporting Indonesia’s economic growth, with crude palm oil (CPO) derivative products being among its top export commodities. China serves as a primary export destination, however, since 2019, shipments to the country have exhibited a declining trend. This study aims to analize the long-term and short-term determinants of Indonesian CPO derivative exports to China using the Autoregressive Distributed Lag (ARDL) model. This study uses secondary data sourced from Statistics Indonesia (BPS), National Bureau of Statistics of China (NBS China), International Monetary Fund (IMF), and Ministry of Finance of the Republic of Indonesia. The results of this study show that, in the long-term, the CPO production positively influences exports, whereas export prices have a negative effect. While in the short-term, exports and China's Gross Domestic Product (GDP) in the current and previous periods, as well as export duties, have a positive effect, whereas previous CPO production has a negative effect. These results highlight the significant roles of both domestic and external factors in shaping export performance, underscoring the need for strategies that address price fluctuations, ensure production stability, and consider the economic conditions and policies of trading partners
Analysis of Indonesian government foreign debt Dyah Tri Larasati; Timbang Sirait
Priviet Social Sciences Journal Vol. 6 No. 6 (2026): June 2026
Publisher : Privietlab

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55942/pssj.v6i6.1227

Abstract

In the government sector, there is still little research on foreign debt issues. Monthly time series data and a combination of macroeconomic variables as independent variables are unique approaches that have not been used in previous studies. This study aims to analyze the short- and long-term impacts of foreign exchange reserves, net exports, exchange rates, inflation, and London Interbank Offered Rate (LIBOR) on the Indonesian government foreign debt from January 2014 to December 2022. This study uses cointegration and the Error Correction Mechanism (ECM). Based on 108 monthly observations spanning a nine-year timeframe, the results reveal that Indonesian government foreign debt is positively and significantly influenced by foreign exchange reserves in the long run. Conversely, inflation and the exchange rate exert a negative and significant impact. Within the short-term analysis, foreign exchange reserves continue to exhibit a positive and significant correlation, whereas negative and significant effects are observed for the LIBOR interest rate, inflation, and the exchange rate. Finally, the study finds that net exports consistently show no significant influence in the short or long term. The empirical implication of this study is to determine the policies and programs that are influential in maintaining the stability of Indonesian government foreign debt in both the short and long term.
Co-Authors Amirul Falah Mustaqim Azzahra Dhisa Khamila Clarissa Azarine Dyah Tri Larasati Fabian La Wima Vallessy Nurvita Trianasari Rika Lusiana Simbolon