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All Journal Jurnal Teknologi dan Manajemen Informatika TELKOMNIKA (Telecommunication Computing Electronics and Control) Bulletin of Electrical Engineering and Informatics Jurnal sistem informasi, Teknologi informasi dan komputer Jurnal Edukasi dan Penelitian Informatika (JEPIN) Proceeding of the Electrical Engineering Computer Science and Informatics JOIN (Jurnal Online Informatika) JOIV : International Journal on Informatics Visualization Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) SemanTIK : Teknik Informasi JIKO (Jurnal Informatika dan Komputer) Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control SINTECH (Science and Information Technology) Journal JURNAL INSTEK (Informatika Sains dan Teknologi) DoubleClick : Journal of Computer and Information Technology CYBERNETICS Astonjadro J-SAKTI (Jurnal Sains Komputer dan Informatika) JIPI (Jurnal Ilmiah Penelitian dan Pembelajaran Informatika) PEDULI: Jurnal Imiah Pengabdian Pada Masyarakat Jutisi: Jurnal Ilmiah Teknik Informatika dan Sistem Informasi bit-Tech Jurnal Teknologi Dan Sistem Informasi Bisnis Jurnal Repositor JTIULM (Jurnal Teknologi Informasi Universitas Lambung Mangkurat) JIKA (Jurnal Informatika) Jurasik (Jurnal Riset Sistem Informasi dan Teknik Informatika) J-SAKTI (Jurnal Sains Komputer dan Informatika) Jurnal Perempuan & Anak Joutica : Journal of Informatic Unisla Makara Journal of Technology SmartComp INOVTEK Polbeng - Seri Informatika
Wildan Suharso
Universitas Muhammadiyah Malang
Humanities Chemical Engineering, Chemistry & Bioengineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Education Electrical & Electronics Engineering Energy Engineering Environmental Science Law, Crime, Criminology & Criminal Justice Library & Information Science Materials Science & Nanotechnology Mathematics Mechanical Engineering Social Sciences Other

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Journal : JOIV : International Journal on Informatics Visualization

 1 
Classification of Diabetic Retinopathy Based on Fundus Image Using InceptionV3 Minarno, Agus Eko; Bagaskara, Andhika Dwija; Bimantoro, Fitri; Suharso, Wildan
JOIV : International Journal on Informatics Visualization Vol 9, No 1 (2025)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62527/joiv.9.1.2155

Abstract

Diabetic Retinopathy (DR) is a progressive eye condition that can lead to blindness, particularly affecting individuals with diabetes. It is commonly diagnosed through the examination of digital retinal images, with fundus photography being recognized as a reliable method for identifying abnormalities in the retina of diabetic patients. However, manual diagnosis based on these images is time-consuming and labor-intensive, necessitating the development of automated systems to enhance both accuracy and efficiency. Recent advancements in machine learning, particularly image classification systems, provide a promising avenue for streamlining the diagnostic process. This study aims to classify DR using Convolutional Neural Networks (CNN), explicitly employing the InceptionV3 architecture to optimize performance. This research also explores the impact of different preprocessing and data augmentation techniques on classification accuracy, focusing on the APTOS 2019 Blindness Detection dataset. Data preprocessing and augmentation are crucial steps in deep learning to enhance model generalization and mitigate overfitting. The study uses preprocessing and data augmentation to train the InceptionV3 model. Results indicate that the model achieves 86.5% accuracy on training data and 82.73% accuracy on test data, significantly improving performance compared to models trained without data augmentation. Additionally, the findings demonstrate that the absence of data augmentation leads to overfitting, as evidenced by performance graphs that show a marked decline in test accuracy relative to training accuracy. This research highlights the importance of tailored preprocessing and augmentation techniques in improving CNN models' robustness and predictive capability for DR detection. 
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 Abdullah Faqih Septiyanto Abims Fardiansa Abu Hanifah Achmadi, Taufan Reza Adam Pamungkas Ramadhan Ageng Widjaya Saputra Agus Eko Minarno Ahmad Ridhani Aini Alifatin Akbi, Denar Regata Andi Mochlis Rachmanu Andriyani, Vivi Anhar Anisatu Thoyyibah Ardiansyah, Frendy Ardiansyah Arif Rahmadhani Aristy Indana Arrasyid, Muhammad Zidan Ashari, Anzilludin Avola, Ahmad Tiova Ian Az-Zahra, Feisyah Azmi, Fawwaz A’yun, Ainul Fithrotul Bagaskara, Andhika Dwija Bashor Fauzan Muthohirin Benny Indriawan Priatmaja Brima Helpiono Catur Rahmadani Nuari Chandranegara, Didih Rizki Christian Sri Kusuma Aditya Damar Arya Pradhipta Dani Harmanto Dani Harmanto Darfian Ardiansyah Darmawan, Gilang Dwi Daroe Iswatiningsih Dewantoro, Muhammad Bagas Dharma Surya Pradana Dini Kurniawati Dwi Anggraini Puspita Rahayu Ekanita Rakhmah Eko Budi Cahyono Evi Dwi Wahyuni Fachrunnisa Firdausi Fahmi Dwi Arianto Arianto Faridho Fajar Rozaqi Fathoni, Muhammad Asrar Fathony, Izza Ihsan Fidiyanto, Akhsanul Fiqri Abdillah Firdausi, Fachrunnisa Firman Firman Firmansyah, Tino Fitri Bimantoro Frendy Ardiansyah Ardiansyah Gita Indah Marthasari Gita Marthasari Hanif, Lathifah Hardianto Wibowo Harmanto, Dani Heni Pujiastuti herlambang, pandu Hermawan, Mohammad Luthfi Hussin Agung Wijaya Ika Winda Kusumawardani Imron, Moch Jahtra Hidayatullah Jonathan, Sendy Karyati, Rina Khasanah, Rahayu Nurul Kiki Ratna Sari L. Yasril Imam LAILATUL FITRIAH Lailatul Hasanah Lailatul Husniah Lailis Syafa'ah Lathifah Hanif Lilhaq, Muhammad Zulfiqor Mahar Faiqurahman Marthasari, Gita Maskur Maskur Maulana, Noordin Prasetyo Moch Imron Mualim, Damar Arya Pradhipta Muhammad Amin Muhammad Asrar Fathoni Muhammad Iqbal Ramadhan Nashrullah, Aditya Nur Annisa Nur Aulia, Adiva Nur Hayatin Nurwisnu Warasih Al-Kahfie Nuryasin, Ilyas Okta Raditya Novidianto Raffi Ainul Afif Rakhmah, Ekanita Ramadha, Firdatul Nurul Ramadhany, Naufal Raihan Riki Rizki Nur Shidiq Rina Karyati Risca Amellia Risdianto Risdianto Riswandi Meifi Mardani Rizaldi, Muhammad Ikhwananda Rizky Githa Hidayat Rizky Irwan Saputra Rohman, Moh. Ainur Bahtiar Romadhana, Muhammad Restu Adjie S, Vinna Rahmayanti Sahnaz Faradiba Taslim Sandy Young Sari, Zamah Shanty Kusuma Dewi Sofyan Arifianto Sri Juniyanti Syahid Widyanto, Azis Nur Syaifuddin Syaifuddin Taufan Reza Achmadi Thathit Manon Andini Tino Firmansyah Tsani, Mutiara Vitanti, Vizza Dwi Vivi Andriyani Wahyu Andhyka Kusuma Warvana, Ulil Fikri Wicaksono, Galih Wasis Wiyono, Briansyah Setio Yannu Indra Kusuma Yuda Munarko Yudadharma, Muhammad Vijar Yudhistira, Brian Yudi Ananta Prasetya Yudiansyah, Muhammad Wahyu Yufis Azhar Yurizal Rizqon Rifani Yusuf, Elsa Annas Sonia Zakki, Ahmad Kevin Adhira Zamah Sari Zata Dini Astuti