Pratama, Romi (2019) Prediksi Jumlah Pendapatan Asli Daerah D.I Yogyakarta Menggunakan Jaringan Saraf Tiruan Backpropagation. Tugas Akhir thesis, University of Technology Yogyakarta.
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Abstract
The enactment of law number 32 of 2004 and number 33 of 2004 relating to the implementation of regional autonomy, local governments are given the authority to regulate and manage their government in meeting the needs of their respective regions. One of them is to determine the APBD in which there are three revenues, namely local revenue, balancing funds, and other legitimate regional income. Of the three revenues, the region's original income contributes quite significantly. The own-source revenues are from four revenues, namely regional taxes, regional levies, separated regional wealth management results, and other legitimate local revenue. Regional original income is a source of regional income which income is derived from various sectors, thus, there are many factors which affect regional income. Of the many factors, there are three factors which influence regional original income, namely: GRDP in the industrial sector, levies, and local taxes. For optimal regional income to be optimal, there is a need to forecast local revenue for the next year based on factors which influence regional income. Therefore, a forecasting method is needed which in this study uses the Backpropagation Artificial Neural Network method to study the problem. ANN is an information processing system such as a processing system in the neural network of the human brain. ANN has been widely used in many applications, one of which is learning. From the testing which has been carried out using 2 test data with the provisions of 3 input nodes, 5 hidden1 nodes, 6 hidden2 nodes, 1 output node, learning rate 0.1, 0.7 momentum and 0.000001 error limit the predicted results reach 99, 4476% accuracy. Thus, the conclusions from the system of predicting the original income of the region with Backpropagation ANN can predict PAD optimally. Keywords: Artificial Neural Networks, Backpropagation, Regional Budget.
| Item Type: | Thesis (Skripsi, Tugas Akhir or Kerja Praktek) (Tugas Akhir) |
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| Subjects: | T Technology > T Technology (General) |
| Divisions: | Fakultas Sains Dan Teknologi > S1 Informatika |
| Depositing User: | Kaprodi S1 Informatika UTY |
| Date Deposited: | 04 Apr 2019 10:01 |
| Last Modified: | 04 Apr 2019 10:01 |
| URI: | http://eprints.uty.ac.id/id/eprint/2680 |
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