Pemetaan UMKM dalam Upaya Pengentasan Kemiskinan dan Penyerapan Tenaga Kerja Menggunakan Algoritma K-Means

  • Herwinda Kurniadewi Universitas Singaperbangsa Karawang
  • Rijal Abdul Hakim
  • Mohamad Jajuli
  • Jajam Haerul Jaman
Keywords: Algoritma K-Means, Clustering, Python, silhouette coefficient

Abstract

Covid pandemic created an economic crisis. Increase the poverty rate by double digits in one year in Indonesia. Covid pandemic has also had an impact on Indonesia's employment conditions, such as finding it difficult to find work. Absorption of labor has a close correlation with poverty. The workforce has a significant influence on the poverty level. One of the regencies in West Java which has a high poverty rate and job seekers is increasing compared to the previous year, Purwakarta Regency. Poverty alleviation by developing MSMEs has good potential. The development of MSMEs will be able to absorb more workers and increase people's income so that it can encourage the rate of economic growth. In this study using the CRISP-DM methodology. In this study, MSMEs in Purwakarta Regency were grouped based on location, number of MSMEs, number of poor people and number of job seekers by using the k-means algorithm and mapping using python. The results of the grouping obtained 3 clusters, namely clusters as many as 6 districts, clusters as many as 8 districts and clusters as many as 3 districts. To determine the performance of the model, an evaluation of the silhouette coefficient which obtained a value of 0.45.

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Published
2022-08-30
How to Cite
[1]
H. Kurniadewi, R. Hakim, M. Jajuli, and J. Jaman, “Pemetaan UMKM dalam Upaya Pengentasan Kemiskinan dan Penyerapan Tenaga Kerja Menggunakan Algoritma K-Means”, JAIC, vol. 6, no. 2, pp. 113-119, Aug. 2022.
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Articles