Sari, Muthia Pandan (2019) Pemodelan Tahanan Kapal Displacement Menggunakan Metode Artificial Neural Network. Other thesis, Institut Teknologi Sepuluh Nopember.
Preview |
Text
06211540000122-Undergraduate_Theses.pdf Download (1MB) | Preview |
Abstract
Pengujian tahanan kapal memiliki tujuan untuk mengetahui daya yang dibutuhkan sebuah kapal agar dapat bergerak. Salah satu cara untuk mendapatkan nilai tahanan kapal tanpa melakukan suatu pengujian adalah dengan pemodelan. Untuk mendapatkan model nilai tahanan kapal dan variabel yang berpengaruh terhadap tahanan kapal, penelitian ini menggunakan metode Artificial Neural Network. Pada metode ANN tidak bergantung pada bentuk asumsi yang mendasari data. Penelitian ini menunjukan bahwa jumlah neuron yang optimum yaitu berjumlah enam neuron dan enam variabel input. Variabel input yang digunakan yaitu panjang kapal tercelup, lebar kapal tercelup, syarat tercelup air, total keseluruhan berat kapal, koefisien primastik, dan kecepatan kapal dengan nilai RMSE sebesar 133.2891.
=================================================================================================================================
Testing the resistance of a ship have an objective to discover the power needed for the ship to move. One of the methods to achieve the value of resistance is by modelling. To find the value of the resistance model with its variables that affects the ship resistance, this research used Artificial Neural Network method. The ANN method doesn't assume but relies on data. This research shows that the optimum number of neurons is 6 and 6 input variables. Input variables that is used are length of waterline, width, draft, displacement, prism coefficient and ship speed with a value of RMSE equal to 133,2891.
| Item Type: | Thesis (Other) |
|---|---|
| Additional Information: | RSSt 519.72 Sar p-1 2019 |
| Uncontrolled Keywords: | Artificial Neural Network, RMSE, Tahanan Kapal |
| Subjects: | Q Science > QA Mathematics > QA278.2 Regression Analysis. Logistic regression Q Science > QA Mathematics > QA336 Artificial Intelligence Q Science > QA Mathematics > QA76.87 Neural networks (Computer Science) T Technology > T Technology (General) > T57.74 Linear programming |
| Divisions: | Faculty of Mathematics, Computation, and Data Science > Statistics > 49201-(S1) Undergraduate Thesis |
| Depositing User: | Muthia Pandan Sari |
| Date Deposited: | 03 Jan 2024 04:01 |
| Last Modified: | 03 Jan 2024 04:01 |
| URI: | http://repository.its.ac.id/id/eprint/64301 |
Actions (login required)
 |
View Item |