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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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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.
Automatic Plant Disease Classification with Unknown Class Rejection using Siamese Networks Putra, Rizal Kusuma; Alfarisy, Gusti Ahmad Fanshuri; Nugraha, Faizal Widya; Nuryono, Aninditya Anggari
Buletin Ilmiah Sarjana Teknik Elektro Vol. 6 No. 3 (2024): September
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/biste.v6i3.11619

Abstract

Potatoes are one of the horticultural commodities with significant trade value both domestically and internationally. To produce high-quality potatoes, healthy and disease-free potato plants are essential. The most common diseases affecting potato plants are late blight and early blight. These diseases appear randomly in different positions and sizes on potato leaves, resulting in numerous combinations of infected leaves. This study proposes an architecture focused on a similarity-based approach, namely the Siamese Neural Network (SNN). SNN can recognize images by comparing two or more images and categorizing the test image accordingly. Thus, SNN has an advantage over classification-based approaches as it can identify various combinations of disease spots on potato plants using a similarity-based approach. This study is divided into two main scenarios: testing with data categories which were previously seen during the training process (traditional testing) and testing with the addition of new data categories that were not seen during training. In the first scenario, SNN showed better accuracy with an accuracy rate of 98.4%, while in the second scenario, SNN achieved an accuracy of 97.1%. That result suggests that SNN can categorize data very well, even recognizing data which never seen during training. These results offer hope that SNN can recognize more disease spots/patterns on potato plants or even identify new diseases by adding these new diseases to the SNN support set without retraining.
Peningkatan Kapasitas Pengetahuan Teknologi Informasi Bagi Guru TKIT Mutiara Ilmu Lamandau Nugraha, Faizal Widya
ABDIMASKU : JURNAL PENGABDIAN MASYARAKAT Vol 6, No 3 (2023): September 2023
Publisher : LPPM UNIVERSITAS DIAN NUSWANTORO

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62411/ja.v6i3.1570

Abstract

Pengabdian kepada masyarakat untuk peningkatan kapasitas pengetahuan teknologi informasi bagi guru TKIT Mutiara Ilmu Lamandau ini merupakan program yang digagas dalam rangka adaptasi dengan kemajuan zaman di era informasi ini. Dengan pesatnya kemajuan zaman, maka sektor Pendidikan diharuskan mampu menyesuaikan dengan perkembangan tersebut, sehingga peningkatan kapasitas pengetahuan merupakan solusi untuk permasalahan tersebut. Pengabdian kepada masyarakat ini dilaksanakan dengan metode pelatihan secara langsung mempraktikan ceramah yang disampaikan. Hasil dari pelaksanaan pengabdian kepada masyarakat ini menunjukan nilai rata-rata aspek yang dinilai sebesar 94%. Nilai ini menujukan bahwa peserta pelatihan dari segi pembelajaran, pelaksanaan pelatihan, kepuasan, dan hasil dari pelatihan mendapatkan banyak manfaat dan pada tingkat sangat puas pada pelatihan yang diberikan. 
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