Micro, Small, and Medium Enterprises’ Business Vulnerability Cluster in Indonesia: An Analysis Using Optimized Fuzzy Geodemographic Clustering

Rezzy Eko Caraka,Prana Ugiana Gio,Robert Kurniawan,Jamilatuzzahro Jamilatuzzahro,Bahrul Ilmi Nasution,Mohammad Basyuni,Bens Pardamean

doi:10.3390/su13147807

Abstract

The COVID-19 pandemic has caused effects in many sectors, including in businesses and enterprises. The most vulnerable businesses to COVID-19 are micro, small, and medium enterprises (MSMEs). Therefore, this paper aims to analyze the business vulnerability of MSMEs in Indonesia using the fuzzy spatial clustering approach. The fuzzy spatial clustering approach had been implemented to analyze the social vulnerability to natural hazards in Indonesia. Moreover, this study proposes the Flower Pollination Algorithm (FPA) to optimize the Fuzzy Geographically Weighted Clustering (FGWC) in order to cluster the business vulnerability in Indonesia. We performed the data analysis with the dataset from Indonesia’s national socioeconomic and labor force survey (SUSENAS and SAKERNAS). We first compared the performance of FPA with traditional FGWC, as well as several known optimization algorithms in FGWC such as Artificial Bee Colony, Intelligent Firefly Algorithm, Particle Swarm Optimization, and Gravitational Search Algorithm. Our results showed that FPAFGWC has the best performance in optimizing the FGWC clustering result in the business vulnerability context. We found that almost all of the regions in Indonesia outside Java Island have vulnerable businesses. Meanwhile, in most of Java Island, particularly the JABODETABEK area that is the national economic backbone, businesses are not vulnerable. Based on the results of the study, we provide the recommendation to handle the gap between the number of micro and small enterprises (MSMEs) in Indonesia.

Highlights

We evaluate the performance of the Flower Pollination Algorithm (FPA) along with the classical fuzzy geographically weighted clustering (FGWC) and its four predecessor algorithms, namely ABC, Gravitational Search Algorithm (GSA), intelligent firefly algorithm (IFA), and Particle Swarm Optimization (PSO)
If we look at the details, the optimum performance was principally obtained by the FPA, in some numbers of clusters, the ABC and GSA performed best
It can be seen that the objective function, Xie and Beni index (XB), and IFV indexes could be used as our basis for using the elbow method because the Partition coefficient (PC) and Classification entropy (CE) indexes showed an inversed relationship than they should have across the number of clusters

Summary

Introduction

The COVID-19 pandemic has caused a contraction in the global economy as a result of various efforts in countries to reduce the COVID-19 pandemic numbers, such as lockdown policies and large-scale social restrictions. An economic decline has been felt by Indonesia of up to −2.07% (YoY) [1,2,3,4,5,6,7]. The manufacturing sector and service operations are the main driving force for economic growth [8,9,10,11]. In 2019, the contribution of the manufacturing sector to the Indonesian economy was

Methods

Results

Conclusion