Data partition and hidden neuron value formulation combination in neural network prediction model: Case study: Non-tax revenue prediction for Indonesian government unit

摘要

High quality economics planning proved by high level of accuracy between planning and realization data. As well in government revenue prediction, specifically for non-tax revenue case study, planning using qualitative interpretation with partial information rather than data analysis is one of the problems in existing process. In order to address this specific problem, this paper propose the artificial neural network (ANN) as one of machine learning method, to construct a model for the non-tax revenue plan based on the previous series data and analyze for dependent variables. ANN with its functionality in learning process can help this process by extracting the patterns formed in previous annual non-tax revenue data and related non-tax revenue variable, to get the most accurate model prediction. The analysis process focused on two objects, data partitioning (partition - non partition data set) and number of hidden neuron as one of variable in neural network algorithm (obtained from formulation from related researches). Both combined and compared then to get the best model with least error prediction. These all quantitative analysis planning-based expected can be the alternate process for accurate, realistic and measurable non-tax revenue plan arrangement. The result, the combination of data with government unit attribute partition and p = 2/3 (n+o) with 7 hidden processing neurons resulting MSE = 0,00002059 selected as the best model to proposed for this case study.