Shabri Putra Wirman, Neneng Fitrya, Rahmat Junaidi, Noviarni Gafura Rizki
{"title":"设计一个车牌识别系统,使用假的神经通量网络","authors":"Shabri Putra Wirman, Neneng Fitrya, Rahmat Junaidi, Noviarni Gafura Rizki","doi":"10.37859/jp.v12i2.2586","DOIUrl":null,"url":null,"abstract":"The manual parking system allows for errors in recording, the service takes a long time, and there is no history of vehicle users. The vehicle license plate recognition system is designed as an alternative parking system that is more accurate in recording, fast service, and the presence of vehicle user data. Vehicle license plate recognition system has been designed equipped with Backpropagation Artificial Neural Network (ANN). The recognition system that has been designed in advance goes through an image processing process with grayscale, black and white, segmentation, and principal component analysis (PCA) stages. The system is integrated with backpropagation ANN with multi layer network with the best results at layer1 550 and layer2 500. The number plate recognition system is equipped with a Graphycal User Interface (GUI) to display the identification results of vehicle number plate identification. The results of identification number plate recognition resulted in 100% training accuracy and 97.95918367% testing accuracy.","PeriodicalId":219962,"journal":{"name":"Photon: Jurnal Sain dan Kesehatan","volume":"122 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2022-08-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Rancang Bangun Sistem Pengenalan Plat Nomor Kendaraan Menggunakan Jaringan Saraf Tiruan Backpropagation\",\"authors\":\"Shabri Putra Wirman, Neneng Fitrya, Rahmat Junaidi, Noviarni Gafura Rizki\",\"doi\":\"10.37859/jp.v12i2.2586\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"The manual parking system allows for errors in recording, the service takes a long time, and there is no history of vehicle users. The vehicle license plate recognition system is designed as an alternative parking system that is more accurate in recording, fast service, and the presence of vehicle user data. Vehicle license plate recognition system has been designed equipped with Backpropagation Artificial Neural Network (ANN). The recognition system that has been designed in advance goes through an image processing process with grayscale, black and white, segmentation, and principal component analysis (PCA) stages. The system is integrated with backpropagation ANN with multi layer network with the best results at layer1 550 and layer2 500. The number plate recognition system is equipped with a Graphycal User Interface (GUI) to display the identification results of vehicle number plate identification. The results of identification number plate recognition resulted in 100% training accuracy and 97.95918367% testing accuracy.\",\"PeriodicalId\":219962,\"journal\":{\"name\":\"Photon: Jurnal Sain dan Kesehatan\",\"volume\":\"122 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2022-08-23\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Photon: Jurnal Sain dan Kesehatan\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.37859/jp.v12i2.2586\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Photon: Jurnal Sain dan Kesehatan","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.37859/jp.v12i2.2586","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Rancang Bangun Sistem Pengenalan Plat Nomor Kendaraan Menggunakan Jaringan Saraf Tiruan Backpropagation
The manual parking system allows for errors in recording, the service takes a long time, and there is no history of vehicle users. The vehicle license plate recognition system is designed as an alternative parking system that is more accurate in recording, fast service, and the presence of vehicle user data. Vehicle license plate recognition system has been designed equipped with Backpropagation Artificial Neural Network (ANN). The recognition system that has been designed in advance goes through an image processing process with grayscale, black and white, segmentation, and principal component analysis (PCA) stages. The system is integrated with backpropagation ANN with multi layer network with the best results at layer1 550 and layer2 500. The number plate recognition system is equipped with a Graphycal User Interface (GUI) to display the identification results of vehicle number plate identification. The results of identification number plate recognition resulted in 100% training accuracy and 97.95918367% testing accuracy.