基于Borda算法的模糊C-均值在亚齐-乌塔拉食物易发区聚类判定系统中的应用

Mutammimul Ula, M. Ula, Desvina Yulisda, S. Susanti
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引用次数: 0

摘要

在这项研究中,亚齐乌塔马的食物易发地区的聚类是基于Badan Ketahanan Pangan(BKP)使用模糊C均值(FCM)和Borda算法编制的指数Ketahanan-Pangan(IKP)指标。使用模糊C均值算法将食物易发区域分为三类:非常易发、中等易发和易发。Borda算法用于从非常容易发生的聚类中选择最容易发生的区域,决策者迫切需要跟进这些聚类。根据研究结果发现,在食物可得性方面,有4个分区中度易发,10个易发,13个非常易发。在食物负担能力方面,调查发现,12个分区中等倾向,7个倾向,8个非常倾向。在食物利用方面,一个街道是中度易发区,三个是易发区和23个是非常易发区。在非常容易发生的集群中,使用Borda算法的投票结果是从食物供应方面获得的Sawang区,从食物可负担性方面获得的Syamtalira Aron区,以及从食物利用方面获得的Lapang区。集群系统是使用PHP编程语言基于web构建的。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Fuzzy C-Means with Borda Algorithm in Cluster Determination System for Food Prone Areas in Aceh Utara
In this research, the clustering of food prone areas in Aceh Utama is based on the Index Ketahanan Pangan (IKP) indicators compiled by Badan Ketahanan Pangan (BKP) using Fuzzy C-Means (FCM) and Borda algorithms. The fuzzy C-Means algorithm was used to classify food-prone areas with three clusters: very prone, moderately prone, and prone. The Borda algorithm was used to choose the most prone area from very prone clusters, which are considered urgently to be followed up by decision-makers. Based on the research results, it was found that in the aspect of food availability, four sub-districts are moderately prone, 10 are prone, and 13 are very prone. Regarding food affordability, it found that 12 sub-districts are moderately prone, seven are prone, and eight are very prone. Regarding food utilization, one sub-district is moderately prone, three are prone, and 23 are very prone. The results of voting using the Borda algorithm in very prone clusters are obtained Sawang District from the aspect of food availability, Syamtalira Aron District from the aspect of food affordability, and Lapang District from the aspect of food utilization. The clustering system is built based on the web using the PHP programming language.
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