Clustering K Means for Criteria Weighting With Improvement Result of Alternative Decisions Using SAW and TOPSIS

Daniati, Erna; Utama, Hastari

doi:10.1109/icitisee48480.2019.9003858

Cited by 5 publications

(5 citation statements)

References 8 publications

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“…One method that is often used in normalization is the min-max method. This is done so that the data can be processed better [13].…”

Section: Data Normalizationmentioning

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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“…One method that is often used in normalization is the min-max method. This is done so that the data can be processed better [13].…”

Section: Data Normalizationmentioning

confidence: 99%

“…The K-means clustering algorithm groups data based on the distance between the data and the cluster centroid point. obtained through an iterative process [12] [13]. The analysis needs to determine the number K as algorithm input.…”

Section: Introductionmentioning

confidence: 99%

Integration K-Means clustering and AHP for recommendations batik MSMEs

Kustiyahningsih,

Khozaimi,

Khotimah

et al. 2024

E3S Web Conf.

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show abstract

“…DBI is an evaluation method when the data has been formed into clusters to evaluate the cluster results quantitatively. DBI describes the quality of clustering based on the spread level across clusters and the closeness of data objects within the same group [14]. The DBI idea is to maximize the distance between clusters while minimizing the distance between points inside the cluster.…”

Section: Dbimentioning

confidence: 99%

“…K-Means, an established algorithm within the domain of unsupervised learning, is distinguished from classification algorithms by the absence of a target variable [14]. This venerable clustering algorithm has been extensively deployed in the analysis of SME characteristics.…”

Section: Introductionmentioning

confidence: 99%

Enhancing Small and Medium Enterprises: A Hybrid Clustering and AHP-TOPSIS Decision Support Framework

Khotimah,

Anamisa,

Kustiyahningsih

et al. 2024

ISI

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The vitality of small and medium enterprises (SMEs) is integral to the economic fortification of nations, necessitating refined enhancement mechanisms from governmental bodies. Distinctive in their developmental trajectory, SMEs present a unique challenge in prioritizing interventions. A Decision Support System (DSS), employing a hybridized methodology of Clustering and Analytic Hierarchy Process-Technique for Order Preference by Similarity to Ideal Solution (AHP-TOPSIS), is proposed to facilitate the stratification of SMEs and guide governmental action based on a hierarchical scale of priorities. In this study, K-Means clustering was adopted for the segmentation of SMEs, leveraging its capability to efficiently partition high-dimensional data with minimal error. Subsequently, the TOPSIS method was utilized to rank SMEs within each cluster. However, the critical step of computing criteria weights to ascertain their relative importance was achieved through the AHP method. The latter effectively addresses multivariate considerations encompassing both quantitative and qualitative criteria through pairwise comparison matrices. The research incorporated 11 attributes, encompassing essential characteristics intrinsic to business entities, such as business name, location, operational status, sector, tax identification number, workforce size, average revenue, production costs, operational challenges, credit accessibility, and external financing needs. The clustering process, executed via Self-Organizing Maps (SOM), yielded optimal clusters at 1000 epochs, evidenced by a Davies Bouldin Index (DBI) of 0.74785, translating to an accuracy of 91.2601%. Notably, the SOM's performance in clustering SME data surpassed that of K-Means, as demonstrated by superior results in both Sum of Squared Error (SSE) and DBI metrics, thus showcasing its proficiency in managing data with heterogeneous criteria. The methodology engenders an n-cluster output, from which members are earmarked for priority ranking. AHP-derived weights are calculated, with a Consistency Ratio (CR) exceeding 0 denoting consistency, thereby determining the significance of each SME. TOPSIS calculations then ascribe the final score, delineating the SMEs' standings. This integrated DSS framework presents a robust tool for policymakers, ensuring targeted and efficient allocation of resources towards the advancement of SMEs.

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“…TOPSIS produces better sensitivity test values and more varied preference values [13] [14]. Previous studies by Erna & Hastari, using Simple Additive Weighting (SAW) and TOPSIS on Information System students, found that TOPSIS is more absolute and straightforward compared to SAW [15]. Furthermore, research by Siti Maesyaroh, using Analytic Hierarchy Process (AHP) and TOPSIS, shows that the accuracy of TOPSIS is greater than AHP in selecting laboratory assistants at Faculty of Computing (FKOM) UNIKU (73% compared to AHP 45%) [16].…”

Section: Introductionmentioning

confidence: 99%

Comparison of TOPSIS and SMARTER Methods in Selecting Delivery Services

Aini,

Pratama,

Rinanda

et al. 2024

IJATIS

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The rapid growth of the e-commerce world has propelled the demand for freight forwarding services, a pivotal component in maintaining the smooth flow of this business. Major companies like JNE, TIKI, Kantor Pos Indonesia, SiCepat, and J&T Express are involved. However, despite this convenience, various challenges often accompany the shipping process. Some of these include delayed deliveries, lost or damaged items, or even misdeliveries to the wrong customers. This presents a dilemma for leading e-commerce companies in selecting the most suitable delivery service partner. Hence, a decision support model is necessary in choosing a freight forwarding service. This study will outline comparison methods based on both Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) and Simple Multi Attribute Rating Technique Exploiting Ranks (SMARTER). The proposed methods have strong relevance to the rapid growth of the e-commerce world. This research emphasizes the importance of selecting freight forwarding services in maintaining the smooth operation of e-commerce businesses. The results of this study have obtained ranking results from the final value of each method. The first place in the TOPSIS method with a value of 0.7033 at sensitivity 3 and the lowest in the SMARTER method with a value of 0.1303 at the first sensitivity, and at the second sensitivity, all methods have the same value of 0.2. The conclusion is that TOPSIS is the best method compared to the SMARTER method as a decision support for the selection of freight forwarding services.

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Clustering K Means for Criteria Weighting With Improvement Result of Alternative Decisions Using SAW and TOPSIS

Cited by 5 publications

References 8 publications

Integration K-Means clustering and AHP for recommendations batik MSMEs

Integration K-Means clustering and AHP for recommendations batik MSMEs

Enhancing Small and Medium Enterprises: A Hybrid Clustering and AHP-TOPSIS Decision Support Framework

Comparison of TOPSIS and SMARTER Methods in Selecting Delivery Services

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