Klasifikasi Penyakit Daun Jagung Menggunakan Metode Convolutional Neural Network

Putra, Ivan Pratama; Rusbandi, Rusbandi; Alamsyah, Derry

doi:10.35957/algoritme.v2i2.2360

Cited by 10 publications

(10 citation statements)

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“…ReLU (Rectified Linear Unit) is a function that converts negative signals to zero while allowing positive signals to pass through unaltered. This activation mechanism aids in the learning of increasingly intricate data representations [17].…”

Section: Learningmentioning

confidence: 99%

Detection of mango leaf disease using the convolution neural network method

Saragih,

Nur Azizi,

Alfattah Atalarais

et al. 2024

tekno

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Mango trees are a common plant in Indonesia. In 2021, 2.84 million tonnes of mangoes were produced in Indonesia, according to the Central Statistics Agency (BPS). You can eat mangoes either ripe or unripe. Additionally, this fruit can be turned into meals and beverages. Many farmers grow mango plants, however, Some illnesses can infect mangoes and lead to crop failure or poor fruit quality. Fungal infections like anthracnose and black fungus, often known as black fungus, affect mango plants, but many farmers continue to mistakenly believe they are identifying plant illnesses and pests. To the type of disease in mango plants, the Convolutional Neural Network (CNN) method was applied in this study. It has been demonstrated that CNN is a very efficient way of processing images and identifying key elements for intricate pattern recognition. A total of 1,405 leaf photos from three different categories—525 anthracnose images, 656 black sooty mold images, and 224 healthy images—were used as samples for CNN to identify illnesses in mango plants. This image data was taken from the kaggle.com website. The CNN model is trained using distinct datasets into training data and validation data after data collection and preprocessing. On training data, the model is 95% accurate, while on validation data, it is 98% accurate. By feeding photos of mango leaves into the model and evaluating the predictions, the detection is put into practice. Action can be taken to control the illness in these mango trees based on prediction findings showing the presence of disease with a decent amount of confidence

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Section: Learningmentioning

confidence: 99%

Detection of mango leaf disease using the convolution neural network method

Saragih,

Nur Azizi,

Alfattah Atalarais

et al. 2024

tekno

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“…Penelitian terdahulu, seperti Klasifikasi Penyakit Daun Jagung menggunakan metode Convolutional Neural Network arsitektur ResNet-50 dengan optimizer Adam, Nadam, dan SGD yang menggunakan jumlah dataset sebanyak 4225 yang telah di resize menjadi ukuran 224x224 dan penelitian ini menghasilkan tingkat akurasi terbaik sebesar 98,4% pada optimizer Adam [9]. Kemudian, Klasifikasi Jenis Kanker Kulit menggunakan CNN-SVM.…”

Section: Pendahuluanunclassified

Klasifikasi Penyakit Cacar Monyet Menggunakan Metode Convolutional Neural Network

Saputra¹,

Alamsyah

2023

MDP-SC

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Cacar merupakan suatu kondisi pada kulit yang menyebabkan jerawat berisi cairan di seluruh tubuh dan wajah. Cacar terdiri dari beberapa jenis, salah satunya yaitu cacar monyet. Gejala pada penderita cacar monyet meliputi demam, ruam dan lesi kulit. Sampai saat ini masih dilakukan studi lebih lanjut untuk memahami sumber infeksi dan pola penularan dari cacar monyet. Dataset yang digunakan terdiri dari dua yaitu, dataset pertama berjumlah 3192 terdiri dari 2554 data latih dan 638 data validasi. Dataset kedua berjumlah 770 sebagai data uji. Maka, penelitian ini dilakukan untuk mengklasifikasi cacar monyet menggunakan Convolutional Neural Network arsitektur ResNet-50 dengan fungsi optimasi Adam. Hasil pengujian yang dilakukan menunjukkan akurasi validasi terbaik sebesar 94,82% dan hasil uji terbaik dengan tingkat akurasi sebesar 76,10%.

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“…Finally, there is an activation function layer and an output layer with 128 input neurons and 6 output neurons. These features are combined for encrypted block image classification [17]. The calculation method of the convolutional layer is shown in equation2:…”

Section: Original Image Classification Modelmentioning

confidence: 99%

Efficient and Privacy-Preserving Image Classification Using Homomorphic Encryption and Chunk-based Convolutional Neural Network

Jia,

Cai,

Yang

et al. 2023

Preprint

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Image feature categorization has emerged as a crucial component in many domains, including computer vision, machine learning, and biometrics, in the dynamic environment of big data and cloud computing. It is extremely difficult to guarantee image data security, privacy, and computing efficiency while also lowering storage and transmission costs. This paper introduces a novel method for classifying image features that combines multilevel homomorphic encryption and image data partitioning in an integrated manner. We employ a novel partitioning strategy to reduce computational complexity, significantly reducing computational load and improving classification accuracy. In the quest for increased data security and privacy, we introduce a novel, fully homomorphic encryption approach specialized to partitioned images. To counter the inherent complexity of encryption, we devise a compound encryption strategy that exploits the full potential of homomorphic computation, with an explicit objective to curtail computational and storage overheads. Evidently superior to conventional methods, our methodology showcases pronounced benefits in computational efficiency, storage and transmission cost reduction, and robust security and privacy preservation. Hence, the methodology put forth in this paper presents a pioneering and efficacious resolution to the multifaceted challenges of image feature classification within the intricate milieu of cloud computing and big data.

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Klasifikasi Penyakit Daun Jagung Menggunakan Metode Convolutional Neural Network

Cited by 10 publications

References 0 publications

Detection of mango leaf disease using the convolution neural network method

Detection of mango leaf disease using the convolution neural network method

Klasifikasi Penyakit Cacar Monyet Menggunakan Metode Convolutional Neural Network

Efficient and Privacy-Preserving Image Classification Using Homomorphic Encryption and Chunk-based Convolutional Neural Network

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