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All Journal Indonesian Journal of Applied Statistics BAREKENG: Jurnal Ilmu Matematika dan Terapan Jurnal Varian
Davala, Muhammad Ridzky
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Agriculture, Biological Sciences & Forestry Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Earth & Planetary Sciences Economics, Econometrics & Finance Energy Engineering Environmental Science Mathematics Mechanical Engineering Physics Social Sciences Transportation Other

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Deterministic Economic Resilience Through Gross Regional Domestic Product Using Nonparametric Geographically Weighted Regression Spline Truncated Annisa, Nurul Mutiara; Octavia, Dhita Hartanti; Davala, Muhammad Ridzky
Jurnal Varian Vol. 8 No. 2 (2025)
Publisher : Universitas Bumigora

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30812/varian.v8i2.4303

Abstract

Megatrends are large-scale global movements with huge impacts, influenced by socio-economic, political, ecological and technological factors. As a developing country, Indonesia faces challenges such as political instability and limited infrastructure, so strengthening economic resilience through increasing Gross Regional Domestic Product (GRDP) is important. The aim of this research is to analyze Indonesia's GRDP data in 2022, which shows significant spatial variability between provinces to see the resilience of the Indonesian economy. The method used is Nonparametric Geographically Weighted Regression - Spline Truncated (NGWR-ST). The NGWR-ST approach is well suited because it allows location-specific parameter variations, captures complex nonlinear relationships through spline functions, and minimizes the influence of extreme values ​​using truncation. The results indicate that an optimal model is achieved with two knot points (GCV = 0.293) and a fixed kernel bi-square weighting function with a 19.174 bandwidth (CV = 974.621), providing optimal spatial weighting. Among the factors analyzed, the Human Development Index (HDI) and the Rate of Return (ROR) are identified as having a significant influence on GRDP, contributing insights for strengthening Indonesia’s economic resilience. Thus, this study will contribute to formulating appropriate regional policy strategies to strengthen the economy in facing the World Megatrend in 2045  
A MODIFIED GEOGRAPHICALLY AND TEMPORALLY WEIGHTED REGRESSION MODELING ON OPEN UNEMPLOYMENT RATE IN SOUTH SULAWESI Siswanto, Siswanto; Sunusi, Nurtiti; Yunita, Andi Isna; Davala, Muhammad Ridzky; Baso, Andi M. Alfin; Nurfadilah, Nurfadilah
BAREKENG: Jurnal Ilmu Matematika dan Terapan Vol 20 No 2 (2026): BAREKENG: Journal of Mathematics and Its Application
Publisher : PATTIMURA UNIVERSITY

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30598/barekengvol20iss2pp1099-1110

Abstract

The Open Unemployment Rate (OUR) in Indonesia is still a challenge despite a decline, namely 4.82% in February 2024 and around 7.2 million unemployed people. The main cause of the OUR is the imbalance between the number of the workforce and the availability of jobs. This issue is directly related to the Sustainable Development Goals (SDGs), especially Goal 8 which focuses on the creation of decent jobs and economic growth. South Sulawesi Province has experienced a spike in the OUR in the last five years, especially due to the Covid-19 pandemic which caused the poverty rate to decline to 6.31% in 2020. Along with economic recovery, this figure decreased to 4.19% in August 2024. Although low, the thickness of the layer remains a concern because 4 out of 100 people have not been absorbed in the labor market. Therefore, it is important to identify the factors that influence the OUR in South Sulawesi in order to design a reduction strategy. Various factors that influence the OUR include the human development index, percentage of poor people, average length of schooling, life expectancy, population density, and regional gross domestic product. To analyze the influence of these factors, this study uses the Geographically and Temporally Weighted Regression (GTWR) method which can capture spatial and temporal variations. Modifications are made using the Mahalanobis distance to consider inter-regional correlation and the Locally Compensated Ridge (LCR) approach to overcome high collinearity in the data. The data used comes from the Central Statistics Agency of South Sulawesi Province. Meanwhile, partial testing obtained each observation of the influencing factors varying from 2020 to 2023. In general, the factors that significantly influence the open poverty rate in South Sulawesi in 2020-2023 are the human development index, percentage of poor people, average length of schooling and life expectancy.
Pemodelan Data Kemiskinan di Pulau Sumatera dengan Regresi Multilevel Spline Linear Truncated Davala, Muhammad Ridzky; Annisa, Nurul Mutiara; Siswanto, Siswanto; Kalondeng, Anisa
Indonesian Journal of Applied Statistics Vol 7, No 1 (2024)
Publisher : Universitas Sebelas Maret

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.13057/ijas.v7i1.80768

Abstract

Poverty is one of the world's biggest challenges that is still a problem, both in developing and developed countries, including Indonesia. Around 27.5 million people live below the national poverty line in Indonesia. Because it is the largest archipelago, poverty problems in each region also vary, including on the Sumatra Island. One of the efforts to alleviate poverty can be done through identifying factors that affect the percentage of poor population using truncated linear spline multilevel regression model. Multilevel modeling is a statistical approach specifically used to analyze data with a two-level structure. This approach allows an understanding of the contribution of individual and group-level factors to the response variable. The predictor variables considered are per capita expenditure, open unemployment rate, and human development index at the district/city level (level-1), as well as population growth rate and economic growth rate at the provincial level (level-2). The results of this study show that the best multilevel regression model at level-1 uses three knot points, while at level-2 it uses two knot points. The factors that affect PPM in Sumatra Island in 2021 at level-1 are per capita expenditure and at level-2 are population growth rate and economic growth rate. The factors that affect percentage of poor population in Sumatra Island in 2021 are expected to provide a more in-depth view of the socio-economic conditions on the island of Sumatra.
Co-Authors Annisa, Nurul Mutiara Baso, Andi M. Alfin Kalondeng, Anisa Nurfadilah Nurfadilah NURTITI SUNUSI Octavia, Dhita Hartanti S Siswanto Yunita, Andi Isna