The Implementation of Support Vector Machine to Analyze Compliance of Land and Building Taxpayers - Nurul Nafisa; Rozi Kesuma Dinata; Rizki Suwanda (Faculty of Engineering, Malikussaleh University, 2024)
Land and Building Tax (LBT) is an important source of revenue for local governments, supporting development and community welfare. However, low taxpayer compliance rates often pose a challenge in achieving the targets for Local Own-Source Revenue (LOSR). This study aims to develop a data-driven classification system to map areas with varying levels of LBT taxpayer compliance in Lhokseumawe City and to implement the Support Vector Machine (SVM) method to improve the accuracy of predicting taxpayer compliance. The research data was obtained from the Regional Financial Management Agency (RFMA) of Lhokseumawe City, encompassing LBT data from 2021 to 2023, with variables such as principal amount, penalties, total payments, due dates, and payment dates. This classification system divides taxpayers into two categories: Compliant and Non-Compliant. The results of testing the SVM model indicate that Banda Sakti sub-district has a compliance rate of 98%, Muara Satu has a compliance rate of 99%, Muara Dua has a compliance rate of 99%, and Blang Mangat has a compliance rate of 100%. The accuracy metrics from the implementation of the Support Vector Machine method for assessing land and building tax compliance show a Precision of 86%, a Recall of 100%, and an Accuracy of 86%. By applying the SVM method, it is hoped that there will be an increase in efficiency in the tax collection and management processes, thereby optimally increasing Local Own-Source Revenue (LOSR) and supporting better regional development.