| Issue |
E3S Web Conf.
Volume 125, 2019
The 4th International Conference on Energy, Environment, Epidemiology and Information System (ICENIS 2019)
|
|
|---|---|---|
| Article Number | 23006 | |
| Number of page(s) | 5 | |
| Section | Decision Support Systems | |
| DOI | https://doi.org/10.1051/e3sconf/201912523006 | |
| Published online | 28 October 2019 | |
Forecasting of Groundwater Tax Revenue Using Single Exponential Smoothing Method
Department of Computer Science, Faculty of Computer Science and Information Technology, Mulawarman University, Samarinda - Indonesia
* Corresponding author: dyna.ilkom@gmail.ac.id
Setting the target of groundwater tax revenues for the next year is an important thing for Kutai Kartanegara Regional Office of Revenue to maximize the regional income and accelerate regional development. Process of setting the target of groundwater tax revenue for the next year still using estimation only and not using a mathematical calculation method that can generate target reference value. If the realization of groundwater tax revenue is not approaching the target, the implementation of development in the Government of Kutai Kartanegara can be disrupted. The mathematical method commonly used to predict revenue value is the Single Exponential Smoothing (SES) method, which uses alpha constant value which is randomly selected for the calculation process. Forecasting of groundwater tax revenue for 2018 using groundwater tax revenue data from 2013 to 2017. Single Exponential Smoothing method using alpha constant value consists of 0.1, 0.2, 0.3, 0.4 and 0.5. The forecasting error value of each alpha value is calculated using the Mean Absolute Percentage Error (MAPE) method. The best result is forecasting using alpha value 0.1 with MAPE error value was 45.868 and the best forecasting value of groundwater tax for 2018 is Rp 443.904.600,7192.
Key words: Forecasting / Groundwater Tax / Single Exponential Smoothing Method / Mean Absolute Percentage Error
© The Authors, published by EDP Sciences, 2019
This is an Open Access article distributed under the terms of the Creative Commons Attribution License 4.0, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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