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All Journal Buletin Ilmiah Sarjana Teknik Elektro Abdimasku : Jurnal Pengabdian Masyarakat Journal of Soft Computing Exploration
Nugraha, Faizal Widya
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Computer Science & IT Economics, Econometrics & Finance Electrical & Electronics Engineering Health Professions Industrial & Manufacturing Engineering Social Sciences

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Journal : Journal of Soft Computing Exploration

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Mix histogram and gray level co-occurrence matrix to improve glaucoma prediction machine learning Jumanto, Jumanto; Nugraha, Faizal Widya; Harjoko, Agus; Muslim, Much Aziz; Alabid, Noralhuda N.
Journal of Soft Computing Exploration Vol. 4 No. 1 (2023): March 2023
Publisher : SHM Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52465/joscex.v4i1.99

Abstract

Glaucoma is an eye disease that is the second leading cause of blindness. Examination of glaucoma by an ophthalmologist is usually done by observing the retinal image directly. Observations from one doctor to another may differ, depending on their educational background, experience, and psychological condition. Therefore, a glaucoma detection system based on digital image processing is needed. The detection or classification of glaucoma with digital image processing is strongly influenced by the feature extraction method, feature selection, and the type of features used. Many researchers have carried out various kinds of feature extraction for glaucoma detection systems whose accuracy needs to be improved. In general, there are two groups of features, namely morphological features and non-morphological features (image-based features). In this study, it is proposed to detect glaucoma using texture features, namely the GLCM feature extraction method, histograms, and the combined GLCM-histogram extraction method. The GLCM method uses 5 features and the Histogram uses 6 features. To distinguish between glaucoma and non-glaucoma eyes, the multi-layer perceptron (MLP) artificial neural network model serves as a classifier. The data used in this study consisted of 136 fundus images (66 normal images and 70 images affected by glaucoma). The performance obtained with this approach is an accuracy of 93.4%, a sensitivity of 86.6%, and a specificity of 100%.
Lung cancer classification using convolutional neural network and DenseNet Damayanti, Nabila Putri; Ananda, Mohammad Nabiel Dwi; Nugraha, Faizal Widya
Journal of Soft Computing Exploration Vol. 4 No. 3 (2023): September 2023
Publisher : SHM Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52465/joscex.v4i3.177

Abstract

Lung cancer is a condition that has a major impact on public health. Convolutional Neural Network (CNN) and DenseNet approaches are suggested in this study to aid lung cancer detection and classification. In various fields of pattern recognition and medical imaging, CNN and DenseNet have demonstrated their efficacy. In this study, radiology images from individuals with lung cancer were used to create a set of medical lung images. The findings show that lung cancer can be accurately classified into malignant and benign from radiological images using CNN and DenseNet architectures, with a parameter accuracy of 99.48%. This research contributes to the creation of a deep learning-based system for detecting and classifying lung cancer. The findings can be the basis for creating a more accurate and productive lung cancer diagnostic system.
Co-Authors Agus Harjoko Alabid, Noralhuda N. Ananda, Mohammad Nabiel Dwi Damayanti, Nabila Putri Gusti Ahmad Fanshuri Alfarisy, Gusti Ahmad Fanshuri Jumanto Jumanto, Jumanto Much Aziz Muslim Nuryono, Aninditya Anggari Putra, Rizal Kusuma