Deep Learning untuk Klasifikasi Glaukoma dengan menggunakan Arsitektur EfficientNet

PERDANI, WAHYUNI RIZKY; MAGDALENA, RITA; PRATIWI, NOR KUMALASARI CAECAR

doi:10.26760/elkomika.v10i2.322

Cited by 2 publications

(2 citation statements)

References 1 publication

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“…However, so that the original rectangular image can be resized into a new rectangular image, it is necessary to do an additional process by improving the image's aspect ratio or adding padding to the new image to be resized. The step for resizing the image will affect the accuracy of the classification [15]. Also Adding padding can be done in three ways [16]:…”

Section: ) Convolutional Neural Network (Cnn) Methodsmentioning

confidence: 99%

Cedarwood Quality Classification using SVM Classifier and Convolutional Neural Network (CNN)

Murti¹,

Setianingsih²,

Kusumawardhani³

et al. 2022

IJACSA

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Cedarwood is one of the most sought-after materials since it can be used to create a wide variety of household appliances. Other than its unique aroma, the product's quality is the most important selling attribute. Fiber patterns allow for a qualitative categorization of this wood. Traditionally, workers in the wood-processing business have relied solely on their eyesight to sort materials into several categories. As a result, there will be discrepancies in precision and efficiency, which will hurt the reputation of the regional wood sector. The answer to this issue is machine learning. In this study, we compare the performance of two different cedarwood quality classification systems where both systems use different machine learning methods namely Support Vector Machine (SVM) and Convolutional Neural Network (CNN). Each system will be sent images captured with a Logitech Brio 4K equipped with a joystick and ultrasonic sensors, labeled as belonging to one of five cedar classes (A, B, C, D, or E). In the initial method to learn the wood's pattern and texture, the Histogram of Oriented Gradient (HOG) will be used to identify the material. Meanwhile, the classification method uses a Support Vector Machine (SVM) which will be compared to find the best accuracy and time computation. The first system's experiment achieves 90 percent accuracy with a computation time of 1.40 seconds. For the second, we use a Convolutional Neural Network, a deep learning technique, to classify cedarwood (CNN). Extraction of features occurs in the convolution, activation, and pooling layers. Experimental results demonstrated a considerable enhancement, with an accuracy of 97% and a prediction speed of 0.56 seconds.

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Section: ) Convolutional Neural Network (Cnn) Methodsmentioning

confidence: 99%

Cedarwood Quality Classification using SVM Classifier and Convolutional Neural Network (CNN)

Murti¹,

Setianingsih²,

Kusumawardhani³

et al. 2022

IJACSA

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“…EfficientNet terdiri dari 7 blok, di mana setiap blok memiliki berbagai sub-blok yang beragam. Metode ini menggunakan koefisien gabungan untuk menyesuaikan skala lebar, kedalaman, dan resolusi jaringan dengan seragam [16].…”

Section: Efficientnet-b6unclassified

Klasifikasi Penyakit Daun Padi Menggunakan Model Deep Learning Efficientnet-B6

Milano

2024

JITET

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Tanaman padi merupakan tanaman pangan utama di Indonesia. Tanaman ini diolah menjadi beras dan menjadi makanan utama sehari-hari seluruh masyarakat Indonesia. Pertumbuhan tanaman padi sangat penting untuk menjaga kualitas padi yang baik dan aman untuk dikonsumsi. Salah satu bagian tanaman padi yang mudah terserang penyakit yaitu daun tanaman padi, hal tersebut bisa mempersulit pertumbuhan tanaman padi dan menjadikan kualitas tanaman padi menurun. Metode Convolutional Neural Network dengan menggunakan arsitektur EfficientNet-B6 merupakan metode yang dapat digunakan untuk klasifikasi gambar atau citra yang mana bisa membantu untuk mengetahui penyakit tanaman padi dan melakukan pencegahan dan pemeliharaan tanaman padi lebih lanjut. Pada penelitian ini menggunakan 3355 citra daun tanaman padi yang dibagi menjadi empat kelas yaitu Healthy sebanyak 1488 citra, LeafBlast sebanyak 779 citra, Hispa sebanyak 565 citra, dan BrownSpot sebanyak 523 citra. Klasifikasi dilakukan menggunakan metode Deep Learning dengan arsitektur EfficientNet-B6 dan diperoleh hasil kinerja terbaik yakni pada skenario dengan nilai size input = 224 dan jumlah epoch = 50, hasil akurasi tertinggi ada pada fold yang kelima dengan nilai akurasi sebesar 77.05%, presisi 77.11%, recall 77.05%, dan f1 score 76.29%. Serta, nilai AUC yang didapatkan skenario 2 juga paling optimal dengan nilai setiap fold dengan range 0.90 – 0.93.

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Deep Learning untuk Klasifikasi Glaukoma dengan menggunakan Arsitektur EfficientNet

Cited by 2 publications

References 1 publication

Cedarwood Quality Classification using SVM Classifier and Convolutional Neural Network (CNN)

Cedarwood Quality Classification using SVM Classifier and Convolutional Neural Network (CNN)

Klasifikasi Penyakit Daun Padi Menggunakan Model Deep Learning Efficientnet-B6

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