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All Journal JURNAL BISNIS STRATEGI TELKOMNIKA (Telecommunication Computing Electronics and Control) Bulletin of Electrical Engineering and Informatics Jurnal sistem informasi, Teknologi informasi dan komputer Jurnal Inovasi Teknologi Pendidikan Jurnal Edukasi dan Penelitian Informatika (JEPIN) POROS TEKNIK Journal of Animation & Games Studies Proceeding of the Electrical Engineering Computer Science and Informatics JOIN (Jurnal Online Informatika) JOIV : International Journal on Informatics Visualization Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control SINTECH (Science and Information Technology) Journal Jurnal ULTIMATICS CYBERNETICS Jurnal Keperawatan dan Fisioterapi (JKF) Jurnal Repositor Jurnal Perempuan & Anak Joutica : Journal of Informatic Unisla JURNAL INOVASI DAN PENGABDIAN MASYARAKAT INDONESIA Makara Journal of Technology JGGAG (Journal of Games, Game Art, and Gamification)
Hardianto Wibowo
Universitas Muhammadiyah Malang
Arts Humanities Chemical Engineering, Chemistry & Bioengineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Earth & Planetary Sciences Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Environmental Science Health Professions Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Library & Information Science Materials Science & Nanotechnology Mathematics Mechanical Engineering Public Health Social Sciences Other

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Journal : JOIV : International Journal on Informatics Visualization

 1 
Analysis of Pneumonia on Chest X-Ray Images Using Convolutional Neural Network Model iResNet-RS Didih Rizki Chandranegara; Vizza Dwi Vitanti; Wildan Suharso; Hardianto Wibowo; Sofyan Arifianto
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.
Classification of Skin Cancer Images Using Convolutional Neural Network with ResNet50 Pre-trained Model Minarno, Agus Eko; Lusianti, Aaliyah; Azhar, Yufis; Wibowo, Hardianto
JOIV : International Journal on Informatics Visualization Vol 8, No 4 (2024)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62527/joiv.8.4.2156

Abstract

The skin, an astonishingly expansive organ within the human body, plays a pivotal role in safeguarding us against the environment's harsh elements. It acts as a formidable barrier, shielding our delicate internal systems from the scorching heat of the sun and the harmful effects of relentless exposure to light. Nevertheless, it is not impervious to damage, especially when subjected to excessive sunlight and the potentially hazardous ultraviolet (UV) radiation that accompanies it. Prolonged UV exposure can wreak havoc on our skin cells, potentially setting the stage for the development of skin cancer. This condition demands prompt and accurate diagnosis for effective treatment. To address the pressing need for swift and precise skin cancer diagnosis, cutting-edge technology has come to the fore in the form of deep learning systems. These sophisticated systems have been meticulously designed and trained to classify skin lesions autonomously with remarkable accuracy. The Convolutional Neural Network (CNN) architecture is a formidable choice for handling image classification tasks among the array of deep learning techniques. In a recent breakthrough study, a CNN-based model was meticulously constructed to explicitly classify skin lesions, leveraging the power of a pre-trained ResNet50 architectural model to augment its capabilities. This groundbreaking ResNet50 architecture was meticulously trained to classify seven distinct skin lesions, surpassing the performance of its predecessor, MobileNet. The results achieved in this endeavor are nothing short of impressive. The overall accuracy of the ResNet50 model stands at a commendable 87.42% when tasked with classifying the seven diverse classes within the dataset. Delving further into its proficiency, we find that the Top2 and Top3 accuracy rates soar to an astounding 95.52% and 97.86%, respectively, illustrating the model's exceptional precision and reliability.
Co-Authors Abims Fardiansa Achmadi, Taufan Reza Agus Eko Minarno Ahmad Faiz, Ahmad Akbi, Denar Regata Ali Akbar Montazeri Andriyani, Vivi Aris Widayati Bayu Fajar Pratama BAYU PRASTOWO Chandra Sebastian Chandranegara, Didih Rizki Christian Sri Kusuma Aditya Dani Harmanto Dani Harmanto Daroe Iswatiningsih Dewantoro, Muhammad Bagas Diah Ayu Fitriani Didih Rizki Chandranegara Djamarullah, Ahmad Raihan Eko Budi Cahyono Eko Mulyanto Yuniarno Fachrunnisa Firdausi Firdausi, Fachrunnisa Firmansyah, Tino Fitriani, Diah Ayu Hapzi Ali Harmanto, Dani Hazmi Rizky Heni Pujiastuti Ibnu Jahsy Karyati, Rina L. Yasril Imam Lailatul Huasniah Lailatul Husniah Lusianti, Aaliyah Matahari Bhakti Nendya, Matahari Bhakti Mauridhi Hery Purnomo Mochammad Yusuf Fachroni Moh Ridwan Multohadi Hamzaturrazak Naser Jawas, Naser Norhabibah, Siti Novidianto, Raditya Nuryasin, Ilyas Raditya Novidianto Raffi Ainul Afif Rahayu Nurul Khasanah Rina Karyati Sabrina Nurul Ubay Safun Rahmanto Sari, Zamah Siti Norhabibah Sofyan Arifianto Sofyan Arifianto Sumadi, Fauzi Dwi Setiawan Syaifuddin Syaifuddin Taufan Reza Achmadi Tino Firmansyah Ubay, Sabrina Nurul Vivi Andriyani Vizza Dwi Vitanti Wahyu Andhyka Kusuma Wicaksono, Galih Wasis Wildan Suharso Wildan Suharso Wildan Suharso Yuda Munarko Yufis Azhar Zamah Sari Zidni Lubis