<![CDATA[PT PGN (Persero) gets an erratic gas sales volume every year, this can affect the existing gas supply. If the existing gas supply can't fulfill the demand or vice versa, the existing supply becomes excessive and affect company performance. Of these problems, it is necessary to predict sales volume to determine future inventory. This research uses Multiple Linear Regression Algorithm by applying the Cross-Industry Standard Process for Data Mining (CRISP-DM) method. The Multiple Linear Regression Algorithm aims to find the value of the regression equation, after getting the regression equation, the next step is to do it. Error analysis to determine the accuracy of predictions using MAD, MSE, and MAPE through R.Studio software. From the processing results, the results obtained from the sales volume in 2016 amounted to 109 443.97, 2017 amounted to 79 521.42, 2018 amounted to 102 059.01 and in 2019 amounted to 86 799.89 at PT.PGN (Persero). Then with the resulting error analysis, the MAD value is 27741.58, the MSE value is 791516224.16 and the MAPE value is 27.18%.]]>
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