Implementation of Deep Learning for Classification Type of Orange Using The Method Convolutional Neural Network

Denata, Irvan; Rismawan, Tedy; Ruslianto, Ikhwan

doi:10.31315/telematika.v18i3.5541

Cited by 2 publications

(2 citation statements)

References 7 publications

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“…Convolutional Neural Network (CNN) is one of the most popular algorithms for deep learning, where the learning model is specifically designed for direct classification of two-dimensional media such as images, video, text or audio [12]. One type of neural network commonly used for image data is CNN.…”

Section: B Convolutional Neural Networkmentioning

confidence: 99%

Review of Deep Learning Using Convolutional Neural Network Model

Kurniawan,

Erlangga,

Tanjung

et al. 2024

Engineering Headway

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Machine Learning can be used to process a lot of data and learn patterns from that data to predict the future. One of the most widely used parts of machine learning is Deep Learning. The Deep Learning method that currently provides the most significant results in image recognition is Convolutional Neural Network (CNN). Convolutional Neural Network (CNN) is one of the deep learning algorithms used for computer vision use cases such as image or video classification and detecting objects within images or even image areas. Some research related to the CNN model states that this model has a very good accuracy of 92% but with a fairly small amount of data and the use of epochs, namely 100, resulting in a higher validation error value than the error value in the training process, so that over fitting will occur. Based on several problems in the related research literature, this article aims to identify the weaknesses and shortcomings of Deep Learning algorithms using CNN models that refer to the state of the art, so that they can be used as a reference for further research. The state of the art related to research in the last five years, the Deep Learning algorithm using the CNN model found that (1) The number of epochs can affect the accuracy of the CNN model, (2) 2. The application of architecture can affect the accuracy of the CNN model, (3) the application of the type of layer can affect the accuracy of the CNN model. Based on several problems in the research literature related to the identification of weaknesses and shortcomings of Deep Learning using the CNN model which refers to Table 1. State of the Art summary of literature review research for the last five years, it can be concluded that to increase the accuracy of the CNN model, it is necessary to increase the number of epochs, apply the right architecture according to the problems in the research conducted, and use the type of layer. The hypothesis of this article can be used as a reference for further research related to Deep Learning using the CNN model.

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Section: B Convolutional Neural Networkmentioning

confidence: 99%

Review of Deep Learning Using Convolutional Neural Network Model

Kurniawan,

Erlangga,

Tanjung

et al. 2024

Engineering Headway

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“…Arsitektur CNN dapat dibagi menjadi dua tahap, yaitu pembelajaran fitur dan klasifikasi. Tahap pembelajaran fitur melibatkan convolution, activation, dan polling, sedangkan tahap klasifikasi menggunakan fully connected layer dan fungsi softmax [19], [20]. CNN telah digunakan dalam berbagai aplikasi seperti pengenalan gambar, sistem rekomendasi, analisis citra medis, dan pemrosesan bahasa alami.…”

Section: Yolov5unclassified

Personalisasi Otomatis Aplikasi Caca (Cari Cafe) Berbasis Artificial Intelligence

Pusriwijayanti,

Prasetiadi,

Chika Fransisca

2023

smartcomp

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Peningkatan produksi kopi menciptakan peluang bisnis olahan kopi, memunculkan banyak cafe. Dalam mengikuti perkembangan zaman, pengusaha perlu menggunakan teknologi terkini dengan menyediakan aplikasi reservasi cafe untuk meningkatkan kepuasan pelanggan dan bersaing di pasar yang semakin kompetitif. Penelitian sebelumnya mengenai aplikasi berbasis mobile dengan Extreme Programming yang fokus pada kepuasan pelanggan, memungkinkan pemesanan makanan lebih awal, dan memantau pesanan pelanggan melalui website. Penelitian ini membuat aplikasi CACA (Cari Cafe) yang dirancang untuk melakukan reservasi cafe dan menyediakan informasi tentang cafe-cafe pada satu aplikasi berbasis website. Dalam mencapai personalisasi otomatis, teknologi artificial intelligence seperti Optical Character Recognition (OCR), Convolutional Neural Network (CNN), dan Siamese Neural Network (SNN) digunakan.Personalisasi otomatis aplikasi CACA melibatkan pembacaan e-KTP sebagai data registrasi, pengenalan gambar wajah pengguna untuk memberikan rekomendasi cafe berdasarkan kesukaan atau kebiasaan, dan pencocokan wajah pengguna untuk verifikasi akun member. Penelitian ini berhasil mengimplementasikan OCR pada gambar e-KTP dengan bounding box di mana nilai box_loss sebesar 0.05211 dan nilai cls_loss sebesar 0.01598. Penggunaan transfer learning model VGG16 dengan fungsi aktivasi sigmoid untuk menebak 11 komponen kesukaan atau kebiasaan pengguna juga mencapai tingkat keberhasilan yang optimal. Selain itu, metode verifikasi menggunakan SNN juga memberikan hasil yang baik, dengan mencocokan foto pada gambar e-KTP dengan foto selfie dan mencapai akurasi sebesar 0.9285 dengan nilai loss 0.0170.

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Implementation of Deep Learning for Classification Type of Orange Using The Method Convolutional Neural Network

Cited by 2 publications

References 7 publications

Review of Deep Learning Using Convolutional Neural Network Model

Review of Deep Learning Using Convolutional Neural Network Model

Personalisasi Otomatis Aplikasi Caca (Cari Cafe) Berbasis Artificial Intelligence

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