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All Journal TELKOMNIKA (Telecommunication Computing Electronics and Control) Jurnal Ilmiah Teknik Elektro Komputer dan Informatika (JITEKI) Jurnal sistem informasi, Teknologi informasi dan komputer Jurnal Edukasi dan Penelitian Informatika (JEPIN) POROS TEKNIK JOIN (Jurnal Online Informatika) JOIV : International Journal on Informatics Visualization Sinkron : Jurnal dan Penelitian Teknik Informatika Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control JITK (Jurnal Ilmu Pengetahuan dan Komputer) MATRIK : Jurnal Manajemen, Teknik Informatika, dan Rekayasa Komputer CYBERNETICS JIPI (Jurnal Ilmiah Penelitian dan Pembelajaran Informatika) JPPM (Jurnal Pengabdian dan Pemberdayaan Masyarakat) Journal of Electronics, Electromedical Engineering, and Medical Informatics Jurnal Teknologi Informasi, Komputer, dan Aplikasinya (JTIKA ) Jurnal Repositor Jurnal Perempuan & Anak Jurnal Impresi Indonesia Makara Journal of Technology Indonesian Journal of Electronics, Electromedical Engineering, and Medical Informatics
Chandranegara, Didih Rizki
Universitas Muhammadiyah Malang
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Journal : JOIV : International Journal on Informatics Visualization

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Classification of Dermoscopic Images Using CNN-SVM Minarno, Agus Eko; Fadhlan, Muhammad; Munarko, Yuda; Chandranegara, Didih Rizki
JOIV : International Journal on Informatics Visualization Vol 8, No 2 (2024)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62527/joiv.8.2.2153

Abstract

Traditional machine learning methods like GLCM and ABCD rules have long been employed for image classification tasks. However, they come with inherent limitations, primarily the need for manual feature extraction. This manual feature extraction process is time-consuming and relies on expert domain knowledge, making it challenging for non-experts to use effectively. Deep learning methods, specifically Convolutional Neural Networks (CNN), have revolutionized image classification by automating the feature extraction. CNNs can learn hierarchical features directly from the raw pixel values, eliminating the need for manual feature engineering. Despite their powerful capabilities, CNNs have limitations, mainly when working with small image datasets. They may overfit the data or struggle to generalize effectively. In light of these considerations, this study adopts a hybrid approach that leverages the strengths of both deep learning and traditional machine learning. CNNs are automatic feature extractors, allowing the model to capture meaningful image patterns. These extracted features are then fed into a Support Vector Machine (SVM) classifier, known for its efficiency and effectiveness in handling small datasets. The results of this study are encouraging, with an accuracy of 0.94 and an AUC score of 0.94. Notably, these metrics outperform Abbas' previous research by a significant margin, underscoring the effectiveness of the hybrid CNN-SVM approach. This research reinforces that SVM classifiers are well-suited for tasks involving limited image data, yielding improved classification accuracy and highlighting the potential for broader applications in image analysis.
Analysis of Pneumonia on Chest X-Ray Images Using Convolutional Neural Network Model iResNet-RS Chandranegara, Didih Rizki; Vitanti, Vizza Dwi; Suharso, Wildan; Wibowo, Hardianto; Arifianto, Sofyan
JOIV : International Journal on Informatics Visualization Vol 8, No 1 (2024)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62527/joiv.8.1.1728

Abstract

Pneumonia, a prevalent inflammatory condition affecting lung tissue, poses a significant health threat across all age groups and remains a leading cause of infectious mortality among children worldwide. Early diagnosis is critical in preventing severe complications and potential fatality. Chest X-rays are a valuable diagnostic tool for pneumonia; however, their interpretation can be challenging due to unclear images, overlapping diagnoses, and various abnormalities. Consequently, expedient, and accurate analysis of medical images using computer-aided methods has become crucial. This research proposes a Convolutional Neural Network (CNN) model, specifically the ResNet-RS Model, to automate pneumonia identification. The Contrast Limited Adaptive Histogram Equalization (CLAHE) technique enhances image contrast and highlights abnormalities in pneumonia images. Additionally, data augmentation techniques are applied to expand the image dataset while preserving the intrinsic characteristics of the original images. The proposed methodology is evaluated through three testing scenarios, employing chest X-ray images and pneumonia dataset. The third testing scenario, which incorporates the ResNet-RS model, CLAHE preprocessing, and data augmentation, achieves superior performance among these scenarios. The results show an accuracy of 92% and a training loss of 0.0526. Moreover, this approach effectively mitigates overfitting, a common challenge in deep learning models. By leveraging the power of the ResNet-RS model, along with CLAHE preprocessing and data augmentation techniques, this research demonstrates a promising methodology for accurately detecting pneumonia in chest X-ray images. Such advancements contribute to the early diagnosis and timely treatment of pneumonia, ultimately improving patient outcomes and reducing mortality rates.
Co-Authors Agus Eko Minarno Agus Zainal Arifin Ahmad Hanif Nurfauzi Akbar, Muhammad Sulthoni Akbi, Denar Regata Aminudin, Aminudin Aprilya Dinathi, Devi Ashari, Anzilludin Aulyah, Khairunnisa Azzahra, Kiara christian s.k.aditya Christian Sri Kusuma Aditya Christian Sri kusuma Aditya, Christian Sri kusuma christianskaditya Dewantoro, Muhammad Bagas Dinanthi, Devi Aprilya Ekanita Rakhmah Eko Budi Cahyono Elan Cahya Niswary Elsandy Wirahadi Kusumah Fachry Abda El Rahman Ferdiansyah, Danny Gayatri, Agnes Ayu Maharani Dyah Hanif, Lathifah Hardianto Wibowo Heni Pujiastuti Ianatut Thoifah Imron, Moch Khasanah, Rahayu Nurul L. Yasril Imam Lathifah Hanif Lathifah, Fildzah Moch Imron Moch. Rosid Noviansyah Ningsih Djamsi Novidianto, Raditya Nuryasin, Ilyas Raffi Ainul Afif Rahayu Nurul Khasanah Rakhmah, Ekanita Ramadanti, Elisa Rista Azizah Arilya Rizaldi, Muhammad Ikhwananda Rizky, Wahyu Saiful Amien Sari, Zamah Setiono, Fauzan Adrivano Setyarini, Dela Ananda Sofyan Arifianto Sumadi, Fauzi Dwi Setiawan Vallent Austin Theasar Kurniawan Vitanti, Vizza Dwi Wanvy Arifha Saputra Wicaksono, Galih Wasis Wildan Suharso Wildan Suharso Yuda Munarko Yufis Azhar Zamah Sari