Penentuan Target Pajak Kendaraan Bermotor Di Provinsi Bali Menggunakan ARIMA Dan Algoritma Genetik
Abstract
Bali Regional Income Board is a regional organization tasked with determining the amount of local tax revenue target for the next fiscal year. Currently still done manually in accordance with existing upgrading trends from previous years. So it needs to be done in way that can be measured and accurate forecasting. In recent studies, it shows that forecasting by combining conventional and artificial intelligence (hybrid) methods results in better forecasting accuracy. By that reason, the writer tries to forecast the target of revenue from Motor Vehicle Tax (PKB and BBNKB), which contribute 70% to Bali Province income by combining ARIMA method and Genetic Algorithm. The data used consisted of five groups: yearly and new Vehicles that have linear data types, and as Reverse Names, Entrance Mutations and Output Mutations that have non-linear data types. Each data group consisted of PKB and BBNKB, where it’s monthly realization data from 2011 to 2016 used to be training data and realization data for 2017 as test data. The Combined forecasting mechanism is performed using ARIMA to forecast linear data and using Genetic Algorithms for non-linear data.
As a benchmark for combined forecasting using ARIMA and Genetic Algorithms, forecasting using ARIMA and Genetic Algorithms independently is used for all data types (linear and nonlinear). Testing is done by comparing data of forecasting result with that 3 different methods for year 2017 with data realization year 2017. Then the error percentage is counted using MAPE. From the test results obtained for ARIMA MAPE value of 3.63, Genetic Algorithm 4.72 and combined ARIMA and Genetic Algorithm of 1.13. Thus, the result of forecasting with combination ARIMA and Genetic Algorithm have the best result and then used to forecasting target of PKB for 2018 and so on
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References
[2] Buku Pendapatan Daerah Provinsi Bali, 2014, Dinas Pendapatan Daerah Provinsi Bali.
[3] Peraturan Gubernur Bali Nomor 68 Tahun 2011 Tentang Rincian Tugas Pokok Dinas Pendapatan Provinsi Bali.
[4] Makridakis, S, Wheelwright,S.C. & Hyndman, R.J (1998), Forecasting: Methods and Application, 3rd edition, John Wiley & Sons, New York.
[5] Khatoon, Shahida, Ibraheem, Arumesh Kr. Singh, Priti, 2014. Judul jurnal : Analysis and Comparison of Various Methods Available for Load Forecasting : An Overview.
[6] Zhang, Honyu, 2008. Judul Jurnal : An initial sudy of Growth of Eclipse Defect.
[7] K. Karabulata, A. Alkanb,dan A.S Yilmaz. 2008. Judul jurnal : Long term energy consumption forecasting genetic programming.
[8] Chen, Kuan-Yu dan Chen Hua Wang, 2007. Judul jurnal : Support vector regression with Genetic Algorithm in forecasting tourism demand. (Elsevier).
[9] Zheng, Fengxia dan Shouming Zhong, 2011. Judul jurnal : Time series forecasting using an ensemble model incorporating ARIMA and ANN based on combined objectives. (IEEE).
[10] Jayadeep Pati dan K.K Shukla, 2014. Judul jurnal : A Comparison of ARIMA, Neural Network and a Hybrid Technique for Debian Bug Number Prediction. (IEEE).
[11] Imelda Purba, Adhistya Erna Permanasari, Noor Akhmad Setiawan, 2014. Judul jurnal : Optimization of Neural Network Using Genetic Algorithm in Forecasting Third Part Fund Bank. (IEEE).
[12] Joao Fausto Lorenzato de Oliveira, Teresa B Ludermir, 2014. Judul Jurnal : A Distribute PSO-ARIMA-SVR Hybrid System for Time Series Forecasting (IEEE).
[13] Sheng Lu, Zhong-jian Cai dan Xiao-bin Zhang, 2009. Judul Jurnal : Application of GA-SVM Time Series Prediction in Tax Forecasting. (IEEE).
[14] Jose Carlos R. Filho, Carolina M. Affonso, Roberto C.L. Oliveira, 2014. Judul jurnal : Energy Price Forecasting in the North Brazilian Market using NN – ARIMA model and Explanatory Variables. (IEEE).
[15] Dezhi Liu, Rufei Zhang, Juan Li, 2011. Judul jurnal : Tax Revenue Combination Forecast of Hebei Province Based on the IOWA Operator. (IEEE).
[16] Rubita Sudirman, Dr. Kaveh Ashenayi dan Mostafa Golbaba, 2012. Judul jurnal : Comparison of Methods Used for Forecasting Solar Radiation. (IEEE).
[17] K.M.U Ahmed, M. Amptzis, P.H. Nguyen, W.L. Kling, 2014. Judul jurnal : Application of Time Series and Artificial Neural Network Models in Short term Load Forecasting for Scheduling of Storage Devices. (IEEE).
This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.
This work is licensed under a Creative Commons Attribution 4.0 International License
