Teaching Aid For Diagnosing Motorcycle Damages Using Back Propagation Artificial Neural Network
The challenge of learning media in the world within the next 1 to 2 years is Bring Your Own Device. It forces the learning paradigm to think quickly to follow the development of technology that can optimally use it. In the Control Systems II course, there are some stereotypes that some of the materi...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | Indonesian |
| Published: |
Univerisitas Negeri Yogyakarta
2020-09-01
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| Series: | Jurnal Pendidikan Teknologi dan Kejuruan |
| Subjects: | |
| Online Access: | https://journal.uny.ac.id/index.php/jptk/article/view/21262 |
| Summary: | The challenge of learning media in the world within the next 1 to 2 years is Bring Your Own Device. It forces the learning paradigm to think quickly to follow the development of technology that can optimally use it. In the Control Systems II course, there are some stereotypes that some of the material is mainly an Artificial Neural Network (ANN) was limited to theory and simulations and is difficult to be applied. Teaching aids are interpreted as teaching material that is used to help teachers in carrying out the teaching and learning activities in the classroom. The purposes of this study are: (1) to create teaching aid for ANN material to diagnose motorcycle damage in the Control System II Course (2) to define the accuracy of the application of the teaching aid for the material of ANN in the Control System II Course. The prototyping approach model is used to generally define the teaching aid product that will be developed. In detail, the development methods include (1) listen to the customer, (2) build or revise a mock-up, and (3) customer test drives mockup. Teaching aids products are built in the form of application for the diagnosis of motorcycle damages using the Back-Propagation ANN. This application can detect four types of motorcycle damages based on the sample sounds of motorcycles included. The application can recognize the type of damage from 100 new sound data outside its knowledge-base with a 60% accuracy level. |
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| ISSN: | 0854-4735 2477-2410 |