Decision Support System for Direct Target Cash Recipients Using the AHP and K-Means Method

Pamungkas, Teguh Sri; Nugroho, Agus Susilo; Wasiso, Ichsan; Anggoro, Tri; Kusrini, Kusrini

doi:10.25273/research.v3i2.7084

Cited by 5 publications

(2 citation statements)

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“…K-Means is a non-hierarchical clustering method used to group data into one or more clusters. In one cluster there are data with the same characteristics and data with different characteristics will be grouped in another cluster, so that the data in one cluster has a very small level of variation [6] .…”

Section: K-meansmentioning

confidence: 99%

Implementation of K-Means and SAW Methods in Determining Non-Cash Food Aid Recipients

Yunita,

Kurniati,

Rodiah

et al. 2023

ccitj

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Determination of prospective non cash food assistance recipients, especially in Air Talas village, still uses a manual system so that in the process of determining the recipient there is a risk that the recipient will be inaccurate, so that the village government needs a system that can assist the process of determining prospective non cash food assistance recipients. This study aims to implement the K-Means and SAW methods in determining recipients of non cash food assistance in Air Talas village. The benefits of this research can help the Air Talas village government in determining and recommending prospective non cash food assistance recipients in accordance with established criteria, making it easier to filter, group, and rank appropriate population data according to criteria. In addition, this research is also useful for providing convenience to the community through data collection, clustering, and ranking in a transparent, real, and fast and accurate manner using decision support system software. The K-Means clustering method and the Simple Additive Weighting Ranking method were used in this study with data collection techniques through interviewing sources, in this case the village government, the social section of the community, and through collecting village archive data and relevant journals. The research location is Air Talas village with 316 data used. The results of the study are clustering data as much as 77 data obtained from feasible clusters. The cluster data was then tested using the accuracy value and obtained a value of 80%. Then the research is also in the form of ranking data using clustered data which obtains an accuracy value of 64%.

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Section: K-meansmentioning

confidence: 99%

Implementation of K-Means and SAW Methods in Determining Non-Cash Food Aid Recipients

Yunita,

Kurniati,

Rodiah

et al. 2023

ccitj

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show abstract

“…XYZ. The comparison value between criteria is determined based on their classification from a scale of 1 to 9 by Saaty (Pamungkas et al, 2020) as listed in…”

Section: Criteria Comparison Matrixmentioning

confidence: 99%

Vendor Selection Analysis Based on AHP to Enhance Service Quality and Consumer Satisfaction at PT XYZ

Alhamda,

Putri,

Fernandez

et al. 2022

J. Log. & SC.

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In the midst of intense economic competition, all companies are required to improve their performance in order to maintain their existence in the business world. Not only must they be able to produce high-quality goods, but the company must also be able to provide extraordinary service to its consumers, especially companies engaged in logistics. One way to achieve the best service aspects is through selecting the right freight forwarding vendor. This study aims to determine the best vendor for PT. XYZ based on the most appropriate criteria using the Analytic Hierarchy Process (AHP) method. The qualitative method with a descriptive approach is the method used in writing this article with the results showing that the criteria that have the highest rating weights are the level of service criteria with 47%, the location criteria with 26%, the responsiveness criteria with 19% and the price criteria with 8%. After considering these four criteria, the sequence of vendors with the highest priority value is Vendor E with a total priority value of 0,59. Meanwhile, Vendor D and Vendor F occupy the second and third positions with a total priority score of 0,57 and 0,54.

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Analysis of Barriers for the Build the Resilient Supply Chain Networks Post-COVID-19

Dadsena

Mathiyazhagan²,

Taghipour

2021

Management and Industrial Engineering

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Decision Support System for Direct Target Cash Recipients Using the AHP and K-Means Method

Cited by 5 publications

References 0 publications

Implementation of K-Means and SAW Methods in Determining Non-Cash Food Aid Recipients

Implementation of K-Means and SAW Methods in Determining Non-Cash Food Aid Recipients

Vendor Selection Analysis Based on AHP to Enhance Service Quality and Consumer Satisfaction at PT XYZ

Analysis of Barriers for the Build the Resilient Supply Chain Networks Post-COVID-19

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