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@thesis{thesis, author={Humulungo Djainab}, title ={PREDIKSI PENERIMAAN NEGARA BUKAN PAJAK (PNBP) LPP RRI GORONTALO MENGGUNAKAN ALGORITMA NEURAL NETWORK}, year={2021}, url={http://repository.umgo.ac.id/1018/}, abstract={Non-Tax State Revenue (PNBP in indonesian) is one of the most important sources of state revenue in addition to tax revenues. So in this case, prediction is important in knowing the amount of Non-Tax State Revenue at LPP RRI Gorontalo whether it can meet the targets set by the government. Central RRI LPP or not. The data used were 240 datas. with using the Neural Network Algorithm, the accuracy of the Neural Network Algorithm in predicting NonTax State Revenue is not good. This is evidenced by the smallest RMSE level which is still at 0.076. and after testing used calculations in Microsoft Excel, it can be seen that the prediction results from 48 weeks in 2020 and the error range of the data resulting from denormalization of data with a range value from 23855200 to -104726900} }

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