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Decision support for predicting revenue target determination with comparison of double moving average and double exponential smoothing
Author(s) -
Dyna Marisa Khairina,
Yulius Daniel,
Putut Pamilih Widagdo,
Septya Maharani,
Shabrina Shabrina
Publication year - 2022
Publication title -
iaes international journal of artificial intelligence
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.341
H-Index - 7
eISSN - 2252-8938
pISSN - 2089-4872
DOI - 10.11591/ijai.v11.i2.pp440-447
Subject(s) - exponential smoothing , smoothing , revenue , computer science , limit (mathematics) , control limits , mean absolute percentage error , exponential function , operations research , control chart , artificial neural network , artificial intelligence , mathematics , finance , business , process (computing) , mathematical analysis , computer vision , operating system
The success of the company requires careful planning. Perusahaan Daerah Air Minum (PDAM) is a drinking water facility management company that plays an important role in supporting the smooth development of the region with the influence of revenue targets. Prediction of revenue targets is deemed necessary for accurate and effective decision making. Predictions are made by comparing the double moving average (DMA) and double exponential smoothing (DES) methods which refer to the actual data from the previous five (5) years. Measuring forecasting accuracy using mean absolute percentage error (MAPE) and assessing accuracy analysis results using tracking signal. Prediction test uses five (5) order values on DMA and five (5) alpha values on DES. Based on the test, it shows that the DMA has the advantage of a smaller MAPE value <10 with very good criteria and the results of the analysis of the pattern graph on the tracking signal that do not exceed the upper control limit (UCL) and the lower control limit (LCL). It is concluded that the DMA method is more recommended as a reference approach to support decisions to determine PDAM revenue targets and as a basis for planning and policy making to predict future revenue targets.

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