Classification of Pneumonia Using Support Vector Machine

Wati, Risha Ambar; Irsyad, Hafiz; Rivan, Muhammad Ezar Al

doi:10.35957/algoritme.v1i1.429

Cited by 6 publications

(6 citation statements)

References 1 publication

(1 reference statement)

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“…The classification method used in the study was Support Vector Machine (SVM) and Gray Level Co-Occurrence (GLCM) for the extraction method. The results of the study showed that the best accuracy obtained was 62.66% (Wati et al, 2020). This is also different from previous research that uses the K-Nearest Neighbor method in classifying guava quality using the K-Nearest Neighbor method based on color and texture features.…”

Section: Literature Reviewcontrasting

confidence: 84%

Comparison of Tomato Leaf Disease Classification Accuracy Using Support Vector Machine and K-Nearest Neighbor Methods

Zer¹,

Tambunan²,

Rosnelly³

et al. 2023

SinkrOn

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Tomato Leaf Disease is one of the common things for farmers in growing tomatoes. Tomatoes are one of the popular crops that can grow in low and high areas but are susceptible to disease. For this reason, farmers take precautions by looking at the characteristics and texture of tomato leaves. However, this requires more time and money and a long process. One of the efforts that can be made is to classify tomato leaf diseases. This research aims to classify using the Support Vector Machine and K-Nearest Neighbor methods. The dataset used is tomato leaf image data with 4 classes of leaves affected by disease and 1 healthy leaf. We evaluate and analyze all models using 5-Fold, 10-Fold, and 20-Fold Cross Validation with accuracy, precision, and recall for the best accuracy. The best results of this study are accuracy in the SVM method of 0.953 or 95.3%, Precision of 0.953 or 95.3%, and Recall of 0.953 or 95.3% with 10-Fold Cross-Validation. Compared to the K-NN method, it only obtained an accuracy of 0.907 or 90.7%, a Precision of 0.908 or 90.8%, and a Recall of 0.907 or 90.7% with 10-Fold Cross-Validation.

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Section: Literature Reviewcontrasting

confidence: 84%

Comparison of Tomato Leaf Disease Classification Accuracy Using Support Vector Machine and K-Nearest Neighbor Methods

Zer¹,

Tambunan²,

Rosnelly³

et al. 2023

SinkrOn

View full text Add to dashboard Cite

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“…CNN menerima erorr klasifikasi tertinggi sebesar 0,023% pada tahun 2017 [7], Penelitian tentang image processing menunjukkan bahwa metode ini menghasilkan hasil akurasi yang lebih baik dari pada metode lainnya [8]. Terdapat beberapa variasi arsitektur yang dimiliki CNN, salah satunya Inception, VGGNet, MobileNet, Densenet, dll [9]. Visual Geometry Group atau VGG, adalah arsitektur yang ada dimetode CNN, algoritma ini diusulkan oleh K. Simonyan dan A. Zisserman dari Oxford university pada acara kompetisi ILSVRC2014 [10].…”

Section: Pendahuluanunclassified

“…Penelitian klasifikasi chest-xray pneumonia sudah dilakukan oleh beberapa peneliti sebelumnya seperti pada Pada penelitian klasifikasi penyakit Pneumonia Menggunakan algoritma Support Vector Machine (SVM) dengan Fungsi GLCM, pada penelitian ini menghasilkan akurasi terbaik sebesar 62,66% [9].…”

Section: Tinjauan Literaturunclassified

X-Ray Classification of Pneumonia by Neural Networks Convolution using Vgg Architecture

Arifin¹,

Surya²

2022

SISTEMASI

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Pneumonia menjadi salah satu penyakit yang paling mematikan dan membunuh 3 juta orang di negara maju seperti Amerika Serikat. Berdasarkan pendapat WHO, penyakit pneumonia merupakan salah satu penyebab utama kematian pada anak di dunia, WHO mengatakan banyak anak di bawah usia 5 tahun meninggal dunia yang disebabkan oleh pneumonia. Dan ditahun 2017 WHO melaporkan pneumonia ini telah merenggut 800.000 jiwa anak balita. hal ini menjadi landasan mengapa para peneliti mengembangkan program ini, yaitu agar dapat membantu masyarakat umum untuk mendiagnosis adanya pneumonia. Penelitian ini mengusulkan implementasi Deep Learning menggunakan algoritma Convolutional neural network (CNN) dan arsitektur VGG16 untuk klasifikasi pneumonia rontgen dada dan hasil normal. Hasil penelitian menunjukkan bahwa metode Convolutional neural network dapat mengklasifikasikan hasil rontgen dada yang mengidap pneumonia dengan akurasi tertinggi sebesar 0,9772.

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“…Sudut diwakilkan dalam derajat sedangkan jarak diwakilkan dalam piksel. Jarak antar piksel adalah 1 piksel sedangkan dalam orientasi sudut yaitu 0°, 45°, 90°, dan 135° (Wati et al, 2020).…”

Section: Pendahuluanunclassified

Untitled

Utami,

Meilani,

Fuad

2022

JMAT

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Detection of Covid-19 using X-ray image data has advantages, namely, an affordable price with a high sensitivity value when compared to PCR, Swab, Antigen, and Genose tests. Therefore, this study aims to detect whether a patient is exposed to Covid-19 or not, based on lung X-ray image data using the Gray-Level Co-occurrence matrix (GLCM) feature extraction and the detection method using the Support Vector Machine (SVM). The evaluation model used in this study is the confusion matrix. This study uses 600 lung X-ray image data, consisting of 300 Covid-19 data and 300 normal data. The trials carried out in this study were using four angle orientations, namely 0°, 45°, 90°, and 135°. In addition, three types of kernels were tested, namely linear, polynomial, and RBF kernels. Data sharing uses k-fold cross validation with k=10. The best results were obtained from trials at 45° and 90° angle orientations using a polynomial kernel. The results of sensitivity, accuracy, and specificity obtained have the same value, namely 96.7% respectively. This system has an error of 3.3%. Where there is one normal Covid-19 data detected and one normal Covid-19 data detected, because the accuracy value exceeds 90.0%, it is concluded that the system built is good in detecting Covid-19 disease.

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Classification of Pneumonia Using Support Vector Machine

Cited by 6 publications

References 1 publication

Comparison of Tomato Leaf Disease Classification Accuracy Using Support Vector Machine and K-Nearest Neighbor Methods

Comparison of Tomato Leaf Disease Classification Accuracy Using Support Vector Machine and K-Nearest Neighbor Methods

X-Ray Classification of Pneumonia by Neural Networks Convolution using Vgg Architecture

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