Developing cluster strategy of apples dodol SMEs by integration K-means clustering and analytical hierarchy process method

Mustaniroh, Siti Asmaul; Effendi, Usman; Silalahi, Rizky Luthfian Ramadhan; Sari, Tita Luthfia; Ala, Mohammad

doi:10.1088/1755-1315/131/1/012033

Cited by 1 publication

(2 citation statements)

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“…In the East Java region, the average growth of MSMEs reaches 54.34% per year and around 98% of the workforce can be absorbed in this sector. In order to increase the competitiveness of MSMEs in its region, the East Java Provincial Government is committed to providing support to the MSME sector which is currently increasingly popular [2][5] [6]. Much research on MSME clustering has been carried out using the K-Means Clustering method using cluster optimization using SSE, ELBOW and Sillhoutte Coefficient [7] [8].…”

Section: Introductionmentioning

confidence: 99%

“…The k-means clustering method is one of the best data mining methods for determining clustering because grouping is done based on data that is similar to finding the shortest distance using Euclidean distance [9] [6]. Kmeans clustering is an unsupervised learning algorithm which is included in non-hierarchical cluster analysis which is used to group data based on variables or features [10] [11].…”

Section: Introductionmentioning

confidence: 99%

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Integration K-Means clustering and AHP for recommendations batik MSMEs

Kustiyahningsih,

Khozaimi,

Khotimah

et al. 2024

E3S Web Conf.

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Batik MSME industry is a creative industry sector in Indonesia which contributes quite a lot to Gross Domestic Product. Batik products have been recognized worldwide as one of creative products from Indonesia by UNESCO which confirmed batik as an intangible Cultural Heritage of Humanity. There are around 250 batik makers in Madura Indonesia. The problem is that the large number of batik craftsmen makes it difficult for cooperatives to determine MSME priorities and the Cooperative Work Program. Some batik indicator data is not all filled and there is still categorical and numerical data. The aim is to group batik based on the number of workers, number of products, age, education, business license, turnover, and number of batik motifs. The method used is data preprocessing using Min-Max normalization to convert categorical data into numerical and averages to overcome imputation of empty data. The data grouping method uses K-Means Clustering. AHP is used to determine indicators that have most influence on clustering and ranking of Batik MSMEs. The research contribution is integration of K-Means with AHP and preprocessing techniques. The most optimal cluster evaluation technique uses SSE. Based on the test results, the optimal cluster is K=3, with an SSE value = 0.287, Cluster 1 (Low) = 28%, Cluster 2 (medium) = 33%, and cluster 3 (High) = 39%. The results of recommendations for four highest weighting criteria using AHP are number of customers 24%, employee training 18.8%, product branding 17%, market place 16.3%.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%