Suyitno, R. Boris (2020) IMPLEMENTASI JARINGAN SARAF TIRUAN BACKPROPAGATION UNTUK PREDIKSI PRODUKSI JAGUNG (Studi Kasus : Provinsi Daerah Istimewa Yogyakarta). Tugas Akhir thesis, University of Technology Yogyakarta.
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Naskah Publikasi-R. Boris Bregas Budi Suyitno-5130411144.pdf Download (890kB) | Preview |
Abstract
Corn is one of the staple foods in Indonesia and there are a lot of processed foods from corn. Corn is one of the staple foods is because corn is one of the carbohydrate-producing plants and is even the most important carbohydrate-producing plant in the world besides rice and wheat. Seeing the importance of this plant, it is necessary to have predictions that can see production from year to year, it is necessary to monitor what is the progress of corn farming in the Special Province of Yogyakarta. If the amount of production next year can be predicted, the government can take action to do something about the corn farming sector. From this problem the author hopes for an application that can predict annual corn production in the Special Province of Yogyakarta. Prediction application that will be made using artificial neural networks. Learning algorithm used is backpropagation. By using the backpropagation artificial neural network, it is expected to provide another alternative in calculating and predicting the level of corn production in the Special Province of Yogyakarta next year. So that it can help the government in monitoring corn production in the Special Province of Yogyakarta. The use of backpropagation method in the corn production prediction system is quite good, while the results of data testing reached 90% with the amount of data as much as 5 data using hidden layer 12, MSE 0.0001, and learning rate 0.1. Keywords: Prediction Application, Backpropagation, Artificial Neural Networks, Corn
| 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: | 25 Mar 2020 07:29 |
| Last Modified: | 25 Mar 2020 07:30 |
| URI: | http://eprints.uty.ac.id/id/eprint/4808 |
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