ISHAK, ISHAK (2015) PENERAPAN JARINGAN SYARAF TIRUAN UNTUK MENGKLASIFIKASI JENIS GEMPA GUNUNG RINJANI SEMBALUN, LOMBOK. S1 thesis, Universitas Mataram.
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Abstract
This research applying back propagation neural network to classify the type of earthquake Sembalun Mount Rinjani, Lombok. Network Backpropagation neural network trained to strike a balance between the ability of the network to recognize patterns used during training as well as the network's ability to provide the correct response to the input pattern with the pattern used during training. Back propagation neural network is used to classify the type of earthquake Sembalun Mount Rinjani, Lombok using one year of data (1995) by dividing the data into two parts, namely the training data and test data. From the results of the 1119 study Backpropagation ANN training data and test data 687 seismographs that have been known types of earthquake by comparing 2 different architecture that is 3-3-3-3 network architecture consists of 3 cells in the input layer, the hidden layer 3 cells one, three cells in the hidden layer two and three cell in the output layer, with the percentage of successful trials in 1119 using the training data can classify distant tectonic 44.95%, 9.56% and Volcanic A successful test using the test data 687 may classify the tectonic far 59.53%, 10.91% Volcanic A. 3-5-10-3 and network architecture consists of 3 cells in the input layer, hidden layer 5 cells at one, 10 cells in the hidden layer two and three cell in the output layer, with the percentage of successful tests using a 1119 training data can classify Tectonics far 60.68%, 1.25% and Volcanic A Data 687 687 successful test using the test data can classify 71.17% Away Tectonic, Volcanic A 1.02%.
| Item Type: | Thesis (S1) |
|---|---|
| Keywords (Kata Kunci): | Earthquake, Neural Networks, Backpropagation |
| Subjects: | T Technology > TK Electrical engineering. Electronics Nuclear engineering |
| Divisions: | Fakultas Teknik |
| Depositing User: | Ayus Suyarsih |
| Date Deposited: | 29 Jul 2018 23:55 |
| Last Modified: | 29 Jul 2018 23:55 |
| URI: | http://eprints.unram.ac.id/id/eprint/6873 |
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