{"title":"k -均值聚类在学校识别中的应用在DPRD成员资助资金分配中的应用","authors":"Eka Hayana Hasibuan, Aripin Rambe, Dinur Syahputra","doi":"10.25008/bcsee.v3i2.1163","DOIUrl":null,"url":null,"abstract":"In this study, the k-means algorithm was used to group schools and categorize DPRD grants into very feasible, feasible, and impractical categories for better focus. Based on the results of computational analysis using the K-Means clustering algorithm using the Euclidean distance equation for the distribution of DPRD suction subsidies from 52 schools, 28 schools are in the very decent category and 11 schools are decent. In that category, 13 schools were found with fewer categories. executable category. RapidMiner Studio v.7.6 software can group schools based on the distribution needs of DPRD suction tools for more effective and efficient results.","PeriodicalId":43514,"journal":{"name":"University Politehnica of Bucharest Scientific Bulletin Series C-Electrical Engineering and Computer Science","volume":null,"pages":null},"PeriodicalIF":0.2000,"publicationDate":"2023-01-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Application of K-Means Clustering on School Identification in the Distribution of Assistance Funds for DPRD Members\",\"authors\":\"Eka Hayana Hasibuan, Aripin Rambe, Dinur Syahputra\",\"doi\":\"10.25008/bcsee.v3i2.1163\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"In this study, the k-means algorithm was used to group schools and categorize DPRD grants into very feasible, feasible, and impractical categories for better focus. Based on the results of computational analysis using the K-Means clustering algorithm using the Euclidean distance equation for the distribution of DPRD suction subsidies from 52 schools, 28 schools are in the very decent category and 11 schools are decent. In that category, 13 schools were found with fewer categories. executable category. RapidMiner Studio v.7.6 software can group schools based on the distribution needs of DPRD suction tools for more effective and efficient results.\",\"PeriodicalId\":43514,\"journal\":{\"name\":\"University Politehnica of Bucharest Scientific Bulletin Series C-Electrical Engineering and Computer Science\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":0.2000,\"publicationDate\":\"2023-01-02\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"University Politehnica of Bucharest Scientific Bulletin Series C-Electrical Engineering and Computer Science\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.25008/bcsee.v3i2.1163\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q4\",\"JCRName\":\"ENGINEERING, ELECTRICAL & ELECTRONIC\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"University Politehnica of Bucharest Scientific Bulletin Series C-Electrical Engineering and Computer Science","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.25008/bcsee.v3i2.1163","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q4","JCRName":"ENGINEERING, ELECTRICAL & ELECTRONIC","Score":null,"Total":0}
引用次数: 0
摘要
在本研究中，使用k-means算法对学校进行分组，并将DPRD资助分为非常可行、可行和不可行三类，以便更好地集中注意力。利用欧氏距离方程对52所学校的DPRD吸力补贴分布进行K-Means聚类算法的计算分析结果显示，非常不错的学校有28所，不错的学校有11所。在这一类别中，有13所学校的类别较少。可执行的范畴。RapidMiner Studio v.7.6软件可以根据DPRD抽吸工具的分布需求对学校进行分组，以获得更有效和高效的结果。
Application of K-Means Clustering on School Identification in the Distribution of Assistance Funds for DPRD Members
In this study, the k-means algorithm was used to group schools and categorize DPRD grants into very feasible, feasible, and impractical categories for better focus. Based on the results of computational analysis using the K-Means clustering algorithm using the Euclidean distance equation for the distribution of DPRD suction subsidies from 52 schools, 28 schools are in the very decent category and 11 schools are decent. In that category, 13 schools were found with fewer categories. executable category. RapidMiner Studio v.7.6 software can group schools based on the distribution needs of DPRD suction tools for more effective and efficient results.
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