Implementasi Arsitektur Xception Pada Model Machine Learning Klasifikasi Sampah Anorganik

Kurniawan, Rian; Wintoro, Puput Budi; Mulyani, Yessi; Komarudin, Muhamad

doi:10.23960/jitet.v11i2.3034

Cited by 3 publications

(3 citation statements)

References 5 publications

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“…Before being trained, the model was compiled using an optimization technique, Adam, to maximize accuracy. Some hyperparameters, such as the learning rate, were sized to streamline the training process [43]. The learning rate used in this research was 0.0001.…”

Section: Cnnmentioning

confidence: 99%

Comparative Analysis of SVM and CNN for Pneumonia Detection in Chest X-Ray

Saraswati,

Dewi,

Pirozmand

2024

LKJITI

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Recognizing pneumonia sufferers can be done by analyzing chest X-ray images. Pneumonia sufferers experience pleural effusion, where fluid is between the lungs’ layers. It causes the lungs’ X-ray picture to be cloudy or hazy. It differs from the appearance of X-rays on normal lungs which are dark in color. These differences in X-Ray images can be classified automatically with the help of Artificial Intelligence This research used convolutional neural networks and support vector machine methods to recognize X-ray images of pneumonia. This research applied Principal Component Analysis and Wavelet Transformation support to both methods. This research aimed to evaluate the performance of each model combination. The PCA-SVM model gave the best performance, with an accuracy of 94.545% and an F1 score of 94.675%. The SVM model outperforms the CNN model in recognizing images; in this case, it could be due to the relatively small amount of training data.

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Section: Cnnmentioning

confidence: 99%

Comparative Analysis of SVM and CNN for Pneumonia Detection in Chest X-Ray

Saraswati,

Dewi,

Pirozmand

2024

LKJITI

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“…Xtreme of Inception (Xception) merupakan sebuah arsitektur model algoritma CNN dengan menggunakan metode konvolusi yang dapat dipisahkan secara mendalam. Arsitektur xception adalah hasil dari pengembangan salah satu arsitektur model CNN Inception yang memiliki 36 convolutional layer yang menjadi dasar dari jaringan ekstraksi fitur [17]. Selain itu ditambahkan pula 1 flatten layer, dan 1 output layer dalam model CNN yang akan diimplementasikan.…”

Section: Implementasi Model Cnnunclassified

Use of Augmentation Data and Hyperparameter Tuning in Batik Type Classification using the CNN Model

Auliaddina,

Arifin

2024

SISTEMASI

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Batik adalah salah satu budaya seni Indonesia yang paling dikenal di dunia dan memiliki motif serta jenis batik tradisional yang berbeda-beda dan memiliki keunikannya sendiri-sendiri. Namun sayangnya, masih begitu banyak masayarakat Indonesia yang belum dapat membedakan jenis-jenis batik berdasarakan motifnya. Karena itulah dibutuhkan sebuah cara untuk membantu masyarakat agar mudah untuk dapat membedakan jenis-jenis batik berdasarkan motifnya. Penelitian ini bertujuan untuk mengklasifikasikan jenis-jenis batik berdasarkan motifnya dengan menggunakan model deep learning Convolutional Neural Network dengan menggunakan Data Augmentation dan Hyperparameter Tuning. CNN termasuk dalam jenis Deep Neural Network karena kedalaman jaringannya yang tinggi dan banyak diterapkan pada data citra. Selain itu diterapkan pula Data Augmentation dan Hyperparameter Tuning untuk mengurangi overfitting. Hasil dari penelitian ini menunjukkan model CNN yang menggunakan optimasi Data Augmentation dan Hyperparameter Tuning mendapatkan nilai akurasi validasi, presisi dan recall jauh lebih tinggi yaitu sebesar 66,67% dibandingkan dengan mode CNN yang tidak menggunakan Data Augmentation dan Hyperparameter Tuning yang memiliki akurasi validasi, presisi, dan recall sebesar 28,15%. Selain itu diantara Data Augmentation dan Hyperparameter Tuning, Data Augmentation lah yang paling mempengaruhi peningkatan akurasi validasi, presisi, dan recall dibandingkan Hyperparameter Tuning dengan peningkatan akurasi validasi menjadi 64% dari akurasi validasi sebesar 28,15%.

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“…Xception adalah suatu struktur Convolutional Neural Network yang mengadopsi pendekatan konvolusi yang dapat dipisahkan secara mendalam. Arsitektur ini merupakan hasil pengembangan dari struktur Inception [16]. Arsitektur Xception menggabungkan depthwise convolution dan pointwise convolution untuk membentuk blok-blok residu yang memungkinkan informasi untuk mengalir melalui lapisan-lapisan secara lebih efisien.…”

Section: Gambar 2 Model Yang Digunakan 1 Xceptionunclassified

Klasifikasi Hama Serangga pada Pertanian Menggunakan Metode Convolutional Neural Network

Akram,

Fayakun,

Ramza

2023

bits

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Insect pest attacks pose a serious threat that can potentially cause significant losses in agricultural production. Therefore, the effective recognition and control of insect pests are crucial for maintaining agricultural productivity and quality of yields. With the advancement of computer technology and artificial intelligence, computer technology can be utilized to automatically recognize images in object recognition, particularly for insect pest classification using the Convolutional Neural Network (CNN) method with the Xception architecture. CNN is one of the types of deep feed-forward artificial neural networks widely used in digital image analysis and can process data in the form of grid patterns. CNN consists of three types of layers: convolutional layer, pooling layer, and fully connected layer. The use of CNN in this research aims to facilitate the classification of insect pests. The CNN process involves stages of training, testing, and validation on insect pests to determine the classification of images of various insect pest species. This research utilizes 1363 image samples with 13 classes of insect pests. The training process of CNN involves several parameters such as batch size, number of epochs, learning rate, and optimizer. The experiment's results indicate that the best accuracy achieved by this model is 93.81% during the training phase and 81.75% during the validation phase. This demonstrates that the model successfully performs insect pest classification using the CNN method.

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Implementasi Arsitektur Xception Pada Model Machine Learning Klasifikasi Sampah Anorganik

Cited by 3 publications

References 5 publications

Comparative Analysis of SVM and CNN for Pneumonia Detection in Chest X-Ray

Comparative Analysis of SVM and CNN for Pneumonia Detection in Chest X-Ray

Use of Augmentation Data and Hyperparameter Tuning in Batik Type Classification using the CNN Model

Klasifikasi Hama Serangga pada Pertanian Menggunakan Metode Convolutional Neural Network

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