UNRAVELING THE SUPPLY-SIDE FACTORS SHAPING EAST JAVA’S ECONOMY: INSIGHTS FROM PCA AND MACHINE LEARNING

None Muhammad Firdaus Al Farohi, None Muhammad Jamie Rofie Quality, None As'ary Ricklas Hidayat
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Abstract

Post the COVID-19 pandemic has become a moment for economic recovery for countries around the world, including Indonesia. However, the intense competition in the market, aftershocks of the pandemic, extreme weather conditions, and rapid social, economic, and technological changes have made the global economic situation much more unstable. This has resulted in economic downturns in various countries. Nevertheless, the Indonesian economy has shown strong growth. The economic growth in Indonesia is supported by various factors in terms of demand, such as household consumption, and supply, such as the diversity of business fields. By using data from the Central Statistics Agency (BPS) regarding the factors supporting economic growth from the production side, this research aims to examine the determinant factors that affect the economic performance of East Java. Through Machine Learning analysis using principal component analysis and clustering analysis, certain characteristics were found among districts and cities in East Java. PCA was used to reduce the number of variables and resulted in several components that are consistent with general categorization. Urban areas consistently exhibit high human resource components, while another cluster shows high dependence on natural resources.
揭示影响东爪哇经济的供给侧因素:来自pca和机器学习的见解
2019冠状病毒病大流行后已成为包括印度尼西亚在内的世界各国经济复苏的时刻。然而,激烈的市场竞争、大流行的余震、极端天气条件以及快速的社会、经济和技术变革使全球经济形势更加不稳定。这导致了许多国家的经济衰退。尽管如此,印尼经济仍显示出强劲的增长。印尼的经济增长受到多种因素的支持,包括需求,如家庭消费;供应,如商业领域的多样性。通过使用中央统计局(BPS)关于从生产方面支持经济增长的因素的数据,本研究旨在检查影响东爪哇经济绩效的决定因素。通过使用主成分分析和聚类分析的机器学习分析,发现了东爪哇地区和城市之间的某些特征。PCA用于减少变量的数量,并产生与一般分类一致的几个成分。城市区域始终表现出高人力资源组成,而另一个集群则表现出对自然资源的高度依赖。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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