<![CDATA[This research evaluates and compares linear and polynomial regression methods in predicting the revenue of Cellular Operator from SMS Application to Person (A2P) services, which are packaged in a website-based platform. The background of this study highlights the importance of having an accurate revenue prediction system to support more effective and efficient marketing strategy planning. This study utilizes historical revenue data from the SMS A2P service of Cellular Operator, collected from May 2017 to May 2024 through literature review methods, documentation, and observations from the internal settlement application of Cellular Operator. Both regression methods are applied and analyzed to determine their accuracy in revenue prediction. The results of the study revealed that the polynomial regression method provides higher accuracy and has a smaller prediction error compared to the linear regression method. The accuracy of polynomial regression is 83.48%, while linear regression has an accuracy of 74.42%. The implementation of this prediction system is expected to serve as a tool for Cellular Operator in planning better business strategies and increasing revenue from SMS A2P services.]]>
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