S. Hendra, H. R. Ngemba, R. Azhar, R. Laila, N. P. Domingo, R. Nur
{"title":"Classification system model for project sustainability","authors":"S. Hendra, H. R. Ngemba, R. Azhar, R. Laila, N. P. Domingo, R. Nur","doi":"10.31763/aet.v1i3.689","DOIUrl":null,"url":null,"abstract":"One of the problems faced by the state-owned electricity company (PT. PLN) in Indonesia is the difficulty of monitoring the progress of an ongoing project so that it requires a technology that can help project managers in monitoring project implementation. The data in this study consisted of 117 Win project data and 89 Lose project data with a total of 206 data. The system development used extreme programming with algorithmic testing, namely the configuration matrix. The result of this research showed that the model could produce an accuracy of 92.68% with an error percentage of 7.32%, which means that the model produced good accuracy in implementing the C4.5 algorithm in recognizing patterns of project development. The first implication of the proposed approach is that it can establish project work monitoring services. The second implication is that project managers can improve company performance","PeriodicalId":21010,"journal":{"name":"Research Journal of Applied Sciences, Engineering and Technology","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2022-04-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Research Journal of Applied Sciences, Engineering and Technology","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.31763/aet.v1i3.689","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
One of the problems faced by the state-owned electricity company (PT. PLN) in Indonesia is the difficulty of monitoring the progress of an ongoing project so that it requires a technology that can help project managers in monitoring project implementation. The data in this study consisted of 117 Win project data and 89 Lose project data with a total of 206 data. The system development used extreme programming with algorithmic testing, namely the configuration matrix. The result of this research showed that the model could produce an accuracy of 92.68% with an error percentage of 7.32%, which means that the model produced good accuracy in implementing the C4.5 algorithm in recognizing patterns of project development. The first implication of the proposed approach is that it can establish project work monitoring services. The second implication is that project managers can improve company performance