deteksi objek plat kendaraan menggunakan metode Convolutional Neural Network

Yuslianto, Calvin (2023) deteksi objek plat kendaraan menggunakan metode Convolutional Neural Network. Bachelor thesis, Universitas Pelita Harapan.

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Abstract

Vehicle plate classification is one of computer vision field. Based on data from Badan Pusat Statistik, the number of vehicles in Indonesia increasing each year, such as from 2018 to 2019 where the number of vehicles increased by 1% to 133,617,012. With the amount of vehicle increasing each year, vehicle plate classification will be very useful in various places such as transportation industry, highways, parking lots, and other places. In recent years, computer vision has developed rapidly and has been successful in classifying images in various fields. By looking at the previous research by Budi Setiyono, that used the convolutional neural network (CNN) method which achieved an accuracy of 87.1%. Therefore, the purpose of this research is to build a model that can classify vehicle plate images using the CNN method. There are various stages in building the CNN model, including building the CNN model, training the model, and testing the model. The accuracy obtained in this research is only 73.85%./Klasifikasi gambar plat kendaraan merupakan salah satu bagian dari computer vision. Berdasarkan data Badan Pusat Statistik jumlah kendaraan di Indonesia terus meningkat dari tahun ke tahun, seperti dari tahun 2018 ke 2019 jumlah kendaraan di Indonesia meningkatkan 1% menjadi 133,617,012. Dengan bertambahnya jumlah kendaraan ini, maka klasifikasi plat kendaraan akan sangat berguna di berbagai tempat seperti industri transportasi, jalan raya, lahan parkir dan berbagai tempat lainnya. Dalam beberapa tahun ini computer vision telah berkembang dengan sangat pesat dan telah berhasil dalam mengklasifikasikan gambar di berbagai bidang dengan baik. Hal ini terbukti dengan penelitian sebelumnya yang dilakukan oleh Budi Setiyono menggunakan metode convolutional neural network (CNN) berhasil mendapatkan akurasi 87.1%. Oleh karena itu, tujuan penelitian ini adalah membangun model yang dapat mengklasifikasikan gambar plat kendaraan dengan menggunakan metode CNN. Dalam membangun model CNN terdapat berbagai tahap seperti membangun model CNN, training model, dan testing model. Hasil akurasi yang didapatkan dalam penelitian ini hanya mendapatkan akurasi sebesar 73.85%.

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