Deteksi Sarung Samarinda Menggunakan Metode Naive Bayes Berbasis Pengolahan Citra

Septiarini, Anindita; Saputra, Rizqi; Tejawati, Andi; Wati, Masna

doi:10.29207/resti.v5i5.3435

Cited by 8 publications

(7 citation statements)

References 13 publications

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“…Percentage split is a commonly used approach in data processing and modelling [11]. In this method, the initial data is split by a certain percentage, where most of the data will be used to train the model (training data) and the rest will be used to test the performance of the model (testing data).…”

Section: Data Splittingmentioning

confidence: 99%

Image Classification of Vine Leaf Diseases Using Complex-Valued Neural Network

Putri,

Prasetya,

Fahrudin

2024

jiko

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Leaf diseases are a serious challenge in the agricultural industry affecting crop quality and yield especially in grapevines. Early recognition and classification of grape leaf diseases is crucial to enable farmers to take appropriate preventive measures in maintaining the health of their crops. The research utilized an innovative approach based on Complex-Valued Neural Network (CVNN) to address the problem. Using Complex-Valued Neural Network (CVNN) this research seeks to identify and classify grape leaf diseases through a series of experiments. A total of 100 images divided into 4 classes namely Black Rot, ESCA, Leaf Blight, and Healthy were collected to train the model. The results show that the trained CVNN model successfully achieved a training accuracy of 100% and a testing accuracy of 97%, demonstrating excellent performance in classifying grape leaf diseases. This states that the proposed approach has great potential to be an effective tool in helping growers manage their vineyards more efficiently and effectively. The developed image processing method is expected to be applied in designing a system to perform image classification of diseases on grape leaves.

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Section: Data Splittingmentioning

confidence: 99%

Image Classification of Vine Leaf Diseases Using Complex-Valued Neural Network

Putri,

Prasetya,

Fahrudin

2024

jiko

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“…The distance in the GLCM method will create a co-occurrence in the data of an image [6]. In the next stage, [15] you will get one level on the neighboring pixels with pixel values based on distance and orientation in the form of degrees [16]. Pixels can be determined through the distance between pixels which is determined by 1 to 10 pixels, while the angular orientation is formed from four angular directions, namely 0°, 45°, 90° and 135° [17].…”

Section: Gray Level Co-occurrence Matrix (Glcm)mentioning

confidence: 99%

“…Pixels can be determined through the distance between pixels which is determined by 1 to 10 pixels, while the angular orientation is formed from four angular directions, namely 0°, 45°, 90° and 135° [17]. The calculation value sought is as follows [16].…”

Section: Gray Level Co-occurrence Matrix (Glcm)mentioning

confidence: 99%

BPNN Optimization With Genetic Algorithm For Classification of Tobacco Leaves With GLCM Extraction Features

Evandari

Soeleman

Pramunendar

2023

J. RESTI (Rekayasa Sist. Teknol. Inf.)

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Tobacco leaves are one of the agricultural commodities cultivated by Indonesian farmers. In their application in the field, there are many obstacles in tobacco leaf cultivation, one of which is declining tobacco quality caused by weather factors. In this study, a technology-based analysis step was carried out to determine the classification in determining the quality of tobacco leaves. The research was carried out by applying the classification optimization of the Backpropagation Artificial Neural Network Method and genetic algorithms to determine the weights obtained from extracting GLCM features. You can get the weight value from the genetic algorithm on the homogeneity variable from this analysis step. The variable gets a weight value of 1. The results of this study obtained a classification value with the Backpropagation Artificial Neural Network Method model getting an accuracy value of 53.50% at a hidden layer value of 2,4,5,7. For classification with the Artificial Neural Network Method, Backpropagation, which is optimized with genetic algorithms, you get an accuracy value of 64.50% at the 4th hidden layer value. From this study, the value of optimization accuracy increased by 11% after being optimized with genetic algorithms.

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“…Energy is the intensity of the spread of pixels of an image in grayscale. If the values of energy of all pixels are relatively similar, it indicates a high category [12]. Entropy = ∑ ∑ 𝑝(𝑖, 𝑗).…”

Section: Glcm Implementationmentioning

confidence: 99%

Detecting Diseases on Clove Leaves Using GLCM and Clustering K-Means

Jumarlis

Mirfan

2022

J. RESTI (Rekayasa Sist. Teknol. Inf.)

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The detection of disease in clove plant leaves is generally carried out by diagnosing the symptoms that appear on clove plants. This diagnosis is conducted by clove farmers only by relying on their experience or even having to seek information from other clove farmers. This is because the agricultural sector has no disease detection system for clove leaves by utilizing digital image processing technology to detect diseases in clove leaves. In this study, the researchers applied two methods to make it easier for clove farmers to diagnose diseases in their clove plants. Those methods were the imaging system using Gray Level Co-Occurrence Matrix (GLCM) and disease clustering using the K-Means algorithm. The objective of this study was to design and build image pattern recognition by utilizing 4 features of the GLCM: energy, entropy, homogeneity, and contrast. These 4 features were used to obtain the extraction value from an image. The outcomes were then used to cluster the clove plant diseases using the K-Means method. In making the software, the researchers used Javascript, HTML, CSS, PHP, and MySql to create a database. The output in this study was an information system application that provides disease-type clustering using the K-Means algorithm. The results of the GLCM concerning extracting images of clove plant leaves affected by disease indicated that the created system can be used to help clove farmers in diagnosing what diseases are infecting their plants by only uploading photos from affected leaves of the clove plant. Furthermore, the results of the K-Means calculation on the examined data showed several categories of Anthracnose leaf spot diseases. In addition, sample number #40 was included in cluster 2 status, in which the average values for energy, entropy, homogeneity, and contrast were 0.583, 0.175, 0.939, and 0.175, respectively.

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Deteksi Sarung Samarinda Menggunakan Metode Naive Bayes Berbasis Pengolahan Citra

Cited by 8 publications

References 13 publications

Image Classification of Vine Leaf Diseases Using Complex-Valued Neural Network

Image Classification of Vine Leaf Diseases Using Complex-Valued Neural Network

BPNN Optimization With Genetic Algorithm For Classification of Tobacco Leaves With GLCM Extraction Features

Detecting Diseases on Clove Leaves Using GLCM and Clustering K-Means

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