D. M. Khairina, S. Maharani, P. P. Widagdo, Ramlawati, H. R. Hatta
{"title":"Forecasting Model of Amount of Water Production Using Double Moving Average Method","authors":"D. M. Khairina, S. Maharani, P. P. Widagdo, Ramlawati, H. R. Hatta","doi":"10.1109/IC2IE50715.2020.9274603","DOIUrl":null,"url":null,"abstract":"The population continues to increase causing high water consumption. This causes the need for clean water to continue to increase while the supply of clean water is uncertain every year. Uncontrolled use of water is a challenge for the organization in meeting clean water needs. A forecasting system is needed that is able to predict the use of water for several periods in order to minimize the problem of uneven water distribution. Water demand predictions can also be utilized by companies to allocate water distribution to customers so that they do not experience shortages or waste. Forecasting the amount of water production is carried out using actual data within the period of 2 (two) previous years ie from 2017 to 2018 using the Double Moving Average (DMA) method. Forecasting trials are carried out by comparing the actual data of 2018 with the estimated data of 2018 by using the movement value of 3 periods and 4 periods. The forecasting accuracy method is used the Mean Absolute Percentage Error (MAPE) method to calculate the percentage of errors at each method movement value DMA every month. Based on the tests conducted, the best forecast of the amount of water production is shown in the movement value with 3 periods which results in a smaller accuracy or error rate of MAPE so that it can be said the accuracy value is better and more recommended than the movement value with 4 periods in the DMA method so determining the value of movement can affect the value of forecasting accuracy.","PeriodicalId":211983,"journal":{"name":"2020 3rd International Conference on Computer and Informatics Engineering (IC2IE)","volume":"173 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2020-09-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"3","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2020 3rd International Conference on Computer and Informatics Engineering (IC2IE)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/IC2IE50715.2020.9274603","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 3
Abstract
The population continues to increase causing high water consumption. This causes the need for clean water to continue to increase while the supply of clean water is uncertain every year. Uncontrolled use of water is a challenge for the organization in meeting clean water needs. A forecasting system is needed that is able to predict the use of water for several periods in order to minimize the problem of uneven water distribution. Water demand predictions can also be utilized by companies to allocate water distribution to customers so that they do not experience shortages or waste. Forecasting the amount of water production is carried out using actual data within the period of 2 (two) previous years ie from 2017 to 2018 using the Double Moving Average (DMA) method. Forecasting trials are carried out by comparing the actual data of 2018 with the estimated data of 2018 by using the movement value of 3 periods and 4 periods. The forecasting accuracy method is used the Mean Absolute Percentage Error (MAPE) method to calculate the percentage of errors at each method movement value DMA every month. Based on the tests conducted, the best forecast of the amount of water production is shown in the movement value with 3 periods which results in a smaller accuracy or error rate of MAPE so that it can be said the accuracy value is better and more recommended than the movement value with 4 periods in the DMA method so determining the value of movement can affect the value of forecasting accuracy.