CRISP-DM method on Indonesian micro industries (UMKM) using K-Means clustering algorithm

Wulandari, Cahya, Anshori, Yusuf and Holle, Khadijah Fahmi Hayati ORCID: https://orcid.org/0000-0002-6991-1748 (2022) CRISP-DM method on Indonesian micro industries (UMKM) using K-Means clustering algorithm. Matics : Jurnal Ilmu Komputer dan Teknologi Informasi, 14 (2). pp. 35-40. ISSN 1978-161X(p); 2477-2550(e)

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Abstract

UMKM plays an important role in supporting the economy in Indonesia. As one of the steps to reduce poverty, the govwordernment should pay more attention to the growth of its UMKM based on existing data. Data of UMKM collected from 2014 to 2018 in several economic sectors such as the leather industry, Metal Industry, Woven Industry, Pottery Industry, Fabric Industry, Food and Beverage Industry, and Other Industry can be used as government guidelines in efforts to solve poverty problems by processing them using k-means algorithm. The research was carried out using the CRISP-DM method and K-Means algorithm to determine the cluster of provinces so that the policy or decision making can be made more wisely. By using RapidMiner, data processing can be done quickly. The result of the study shows that DBI values of each data using 5 as k are 0.308, 0.312, 0.259, 0.272, 0.333, 0.369, 0.289, and 0.266. Based on that, Jawa Timur and Jawa Tengah have a large industrial growth while Jawa Barat seems to start leaving traditional industries. Besides, the other provinces' industrial growth appears to be stable. It is expected that the government would make wise policies to support the growth of UMKM in Indonesia.

Item Type:	Journal Article
Keywords:	UMKM; K-Means; CRISP-DM; RapidMiner
Subjects:	08 INFORMATION AND COMPUTING SCIENCES > 0801 Artificial Intelligence and Image Processing > 080109 Pattern Recognition and Data Mining
Divisions:	Faculty of Technology > Department of Informatics Engineering
Depositing User:	Khadijah Fahmi Hayati Holle
Date Deposited:	09 Jun 2023 06:22

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