Application of the Artificial Neural Network Algorithm to Predict the Realization of the Duty Tax on the Name of Motor Vehicles in Lampung Province

Dian Kurniasari; Putri Salsabila Ramadhani; Wamiliana Wamiliana; Warsono Warsono

doi:10.24014/ijaidm.v7i2.29456

Application of the Artificial Neural Network Algorithm to Predict the Realization of the Duty Tax on the Name of Motor Vehicles in Lampung Province

Dian Kurniasari, Putri Salsabila Ramadhani, Wamiliana Wamiliana, Warsono Warsono

Abstract

Regional taxes, specifically the Motor Vehicle Name Return Tax (BBNKB), provide the primary source of revenue for regions from the several forms of taxes. The BBNKB tax is crucial in funding government and regional development due to its significant annual growth, encompassing four-wheeled and two-wheeled vehicles. Furthermore, the BBNKB tax catalyzes regional economic expansion and significantly contributes to the government's income. Hence, predicting and forecasting the BBNKB Tax in Lampung Province is necessary to monitor future tax rate fluctuations. That will enable the government to devise innovative tax payment systems and establish tax revenue targets. This study utilizes the Artificial Neural Network (ANN) methodology, using many approaches for distributing training and testing data to forecast. In addition, we utilize hyper-tuning on several factors to obtain the most favourable configurations. The ideal model achieved has a training data allocation of 80% and a testing data allocation of 20%. It was trained for 50 epochs and used a batch size of 16. The model has exceptional predictability, attaining an accuracy rating of 96.51%. Additionally, it showcases a low Root Mean Square Error (RMSE) of 0.246 and a minimal Mean Absolute Percentage Error (MAPE) score of 3.48%. Therefore, it is appropriate to predict the next two-year term. As a result, the forecast for the amount of tax collected from motor vehicle name returns in Lampung has fluctuated.

Keywords

Artificial Neural Network; BBNKB Tax; Lampung Province; Machine Learning; Predictions

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DOI: http://dx.doi.org/10.24014/ijaidm.v7i2.29456

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