Bramandito Yusuf Rizqi Affandi, Y. Cahyana, Dwi Sulistya Kusumaningrum, April Lia Hananto, Fitri Marisa
{"title":"根据生产计划使用线性回归算法预测货物到达的工作流程效率","authors":"Bramandito Yusuf Rizqi Affandi, Y. Cahyana, Dwi Sulistya Kusumaningrum, April Lia Hananto, Fitri Marisa","doi":"10.59805/ecsit.v1i1.5","DOIUrl":null,"url":null,"abstract":"This study discusses how an algorithm can produce predictions used as a reference for implementing work efficiency using the Linear Regression Algorithm. Linear Regression Algorithm is an algorithm that allows to calculate the linear relationship between the dependent and independent variables to make predictions. In his observations, the researcher used one sample which is data on the arrival of goods in the Production Control department of PT XYZ Indonesia with a total IN part of 9055551, part OUT of 332037. The results of predictions made using the Linear Regression Algorithm in (February-May) in 2022 are 4981165 and on the results of testing the prediction results using the MAPE (Mean Absolute Percentage Error) method produces an error of 6% where 6% is still in category A <10% which is very accurate. The results of this prediction produce Man Power, Space and Shuttle efficiency with a reduction of 1 Man Power, 500m2 space and 5 shuttles with a total profit of Rp. 1,897,670,000 per year and can meet the demand for new suppliers to fill the warehouse area. Researchers can conclude that researchers can find out the stages, processes, and results in applying the Linear Regression Algorithm by an average of 90% from previous studies which can predict the arrival of goods and produce work efficiency.","PeriodicalId":202727,"journal":{"name":"Edutran Computer Science and Information Technology","volume":"40 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2023-03-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Work Process Efficiency With Goods Arrival Predictions Against Production Plans Using Linear Regression Algorithms\",\"authors\":\"Bramandito Yusuf Rizqi Affandi, Y. Cahyana, Dwi Sulistya Kusumaningrum, April Lia Hananto, Fitri Marisa\",\"doi\":\"10.59805/ecsit.v1i1.5\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"This study discusses how an algorithm can produce predictions used as a reference for implementing work efficiency using the Linear Regression Algorithm. Linear Regression Algorithm is an algorithm that allows to calculate the linear relationship between the dependent and independent variables to make predictions. In his observations, the researcher used one sample which is data on the arrival of goods in the Production Control department of PT XYZ Indonesia with a total IN part of 9055551, part OUT of 332037. The results of predictions made using the Linear Regression Algorithm in (February-May) in 2022 are 4981165 and on the results of testing the prediction results using the MAPE (Mean Absolute Percentage Error) method produces an error of 6% where 6% is still in category A <10% which is very accurate. The results of this prediction produce Man Power, Space and Shuttle efficiency with a reduction of 1 Man Power, 500m2 space and 5 shuttles with a total profit of Rp. 1,897,670,000 per year and can meet the demand for new suppliers to fill the warehouse area. Researchers can conclude that researchers can find out the stages, processes, and results in applying the Linear Regression Algorithm by an average of 90% from previous studies which can predict the arrival of goods and produce work efficiency.\",\"PeriodicalId\":202727,\"journal\":{\"name\":\"Edutran Computer Science and Information Technology\",\"volume\":\"40 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2023-03-13\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Edutran Computer Science and Information Technology\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.59805/ecsit.v1i1.5\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Edutran Computer Science and Information Technology","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.59805/ecsit.v1i1.5","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Work Process Efficiency With Goods Arrival Predictions Against Production Plans Using Linear Regression Algorithms
This study discusses how an algorithm can produce predictions used as a reference for implementing work efficiency using the Linear Regression Algorithm. Linear Regression Algorithm is an algorithm that allows to calculate the linear relationship between the dependent and independent variables to make predictions. In his observations, the researcher used one sample which is data on the arrival of goods in the Production Control department of PT XYZ Indonesia with a total IN part of 9055551, part OUT of 332037. The results of predictions made using the Linear Regression Algorithm in (February-May) in 2022 are 4981165 and on the results of testing the prediction results using the MAPE (Mean Absolute Percentage Error) method produces an error of 6% where 6% is still in category A <10% which is very accurate. The results of this prediction produce Man Power, Space and Shuttle efficiency with a reduction of 1 Man Power, 500m2 space and 5 shuttles with a total profit of Rp. 1,897,670,000 per year and can meet the demand for new suppliers to fill the warehouse area. Researchers can conclude that researchers can find out the stages, processes, and results in applying the Linear Regression Algorithm by an average of 90% from previous studies which can predict the arrival of goods and produce work efficiency.