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Peramalan Pelayanan Service Mobil (After-Sale) Menggunakan Backpropagation Neural Network (BPNN)
- Source :
- JISKA (Jurnal Informatika Sunan Kalijaga), Vol 6, Iss 3 (2021)
- Publication Year :
- 2021
- Publisher :
- Universitas Islam Negeri Sunan Kalijaga Yogyakarta, 2021.
-
Abstract
- The automotive industry in Indonesia, primarily cars, is getting more and more varied. Along with increasing the number of vehicles, Brand Holder Sole Agents (ATPM) compete to provide after-sale services (mobile service). However, the company has difficulty knowing the rate of growth in the number of mobile services handled, thus causing losses that impact sources of income. Therefore, we need a standard method in determining the forecasting of the number of car services in the following year. This study implements the Backpropagation Neural Network (BPNN) method in forecasting car service services (after-sale) and Mean Square Error (MSE) for the process of testing the accuracy of the forecasting results formed. The data used in this study is car service data (after-sale) for the last five years. The results show that the best architecture for forecasting after-sales services using BPNN is the 5-10-5-1 architectural model with a learning rate of 0.2 and the learning function of trainlm and MSE of 0.00045581. This proves that the BPNN method can predict mobile service (after-sale) services with good forecasting accuracy values.
- Subjects :
- Service (business)
Mean squared error
Artificial neural network
Computer science
business.industry
Process (computing)
Automotive industry
Information technology
Machine learning
computer.software_genre
After-Sale
T58.5-58.64
Backpropagation
MSE
Cars
Artificial intelligence
business
Prediction
computer
Architectural model
Mobile service
Backpropagation Neural Network
Subjects
Details
- Language :
- Indonesian
- ISSN :
- 25280074 and 25275836
- Volume :
- 6
- Issue :
- 3
- Database :
- OpenAIRE
- Journal :
- JISKA (Jurnal Informatika Sunan Kalijaga)
- Accession number :
- edsair.doi.dedup.....7b984872d1be27888b8e0fbd0270dc04