THE APPLICATION OF MACHINE LEARNING APPROACH TO ADDRESS THE GPV BIAS ON POS TRANSACTION

Each transaction always produces junk data or bias data either due to errors or intentions. The junk data volume is always increase day by day, mainly in the using of public and free to use applications. Junk data is a disruption in every decision making which can cause the material or immaterial losses. This kind of problems are also occurring in the Qasir.id application, a POS application developed by PT. Solusi Teknologi Niaga for MSME entrepreneurs in Indonesia. In the company case, the junk data of POS transaction causes a poor quality of GPV (Gross Payment Value) information. The article presents the results of study in the POS transaction junk data handling. The junk data handling is performed by to validate three machine learning techniques and to deploy the best model in the company s Business Intelligence (BI) system. Based on the result of qualitative and quantitative evaluations, it is shown that the proposed approach provide a significant contribution to the company s decision-making process. The evaluation applied to the operational data sample reveals the accuracy score in the handling of junk data is 0.96 in precision, 0.73 in recall value, and the f1 score is 0.831. Whereas the qualitative evaluation based on users feed back of two-month operation indicates that users were greatly assisted in decision-making regarding the GPV.

1.  UEU-Journal-21233-11_1825.pdf - 1522 KB