Imam Sapuan, Muhammad Hilmi Fauzan, Christina Juliane
{"title":"Implementasi Data Mining untuk Klasterisasi dan Prediksi Kelompok Keluarga","authors":"Imam Sapuan, Muhammad Hilmi Fauzan, Christina Juliane","doi":"10.31544/jtera.v7.i1.2022.149-156","DOIUrl":null,"url":null,"abstract":"tahap selanjutnya, sedangkan metode decision tree digunakan sebagai algoritma prediksinya. Hasil penelitian menunjukkan bahwa kedua metode ini berhasil menyelesaikan masalah dengan tingkat akurasi yang sangat tinggi yaitu sebesar 95,3%, presisi sebesar 95,4%, dan recall sebesar 95,3%. Abstract The grouping of families into rich and poor clusters is very much needed as a reference for various future activities such as government assistance or other related parties. Data mining is one approach that can be used to solve this problem. Data mining methods that are suitable are clustering and prediction. This study aims to implement data mining for clustering and predicting family groups. There are two algorithms used in this study, namely kModes and decision tree. The kModes algorithm functions to generate clusters that will be used in the next stage, while the decision tree method is used as the prediction algorithm. The results showed that these two methods succeeded in solving the problem with a very high level of accuracy, namely 95.3%, precision 95.4%, and recall of 95.3%.","PeriodicalId":17680,"journal":{"name":"JTERA (Jurnal Teknologi Rekayasa)","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2022-06-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"JTERA (Jurnal Teknologi Rekayasa)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.31544/jtera.v7.i1.2022.149-156","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0
Abstract
tahap selanjutnya, sedangkan metode decision tree digunakan sebagai algoritma prediksinya. Hasil penelitian menunjukkan bahwa kedua metode ini berhasil menyelesaikan masalah dengan tingkat akurasi yang sangat tinggi yaitu sebesar 95,3%, presisi sebesar 95,4%, dan recall sebesar 95,3%. Abstract The grouping of families into rich and poor clusters is very much needed as a reference for various future activities such as government assistance or other related parties. Data mining is one approach that can be used to solve this problem. Data mining methods that are suitable are clustering and prediction. This study aims to implement data mining for clustering and predicting family groups. There are two algorithms used in this study, namely kModes and decision tree. The kModes algorithm functions to generate clusters that will be used in the next stage, while the decision tree method is used as the prediction algorithm. The results showed that these two methods succeeded in solving the problem with a very high level of accuracy, namely 95.3%, precision 95.4%, and recall of 95.3%.
Tahap selanjutnya, sedangkan方法决策树digunakan sebagai算法预测。Hasil penelitian menunjukkan bahwa kedua mede ini berhasil menyelesaikan masalah dengan tingkat akurasi yang sangat tinggi yitu sebesar 95,3%, presisi sebesar 95,4%, dan recall sebesar 95,3%。将家庭划分为富裕和贫穷的集群是非常必要的,作为政府援助或其他相关方今后各种活动的参考。数据挖掘是一种可以用来解决这个问题的方法。适合的数据挖掘方法是聚类和预测。本研究的目的是实现数据挖掘的聚类和预测家庭群体。本研究使用了两种算法,即kModes和decision tree。kModes算法用于生成将在下一阶段使用的聚类,而决策树方法被用作预测算法。结果表明,这两种方法都能很好地解决问题,准确率为95.3%,精密度为95.4%,召回率为95.3%。