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Muhammad Munadi
Universitas Islam Raden Mas Said Surakarta
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Journal : Journal of Computer Science and Information Technology

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Penerapan Algoritma Deep Learning untuk Deteksi Dini Penyakit dari Citra Medis Munadi, Muhammad
Journal of Computer Science and Information Technology Vol. 1 No. 2 (2025): Journal of Computer Science and Information Technology, September 2025
Publisher : Lembaga Publikasi Ilmiah Nusantara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70716/jocsit.v1i2.259

Abstract

Advances in artificial intelligence technology, particularly in the field of deep learning, have made significant contributions to medical image processing for the early detection of various diseases. This study aims to apply deep learning algorithms, specifically Convolutional Neural Networks (CNNs), in the process of classifying and identifying diseases through the analysis of medical images such as X-rays, MRIs, and CT scans. The use of CNNs enables automatic and efficient feature extraction, thereby improving the accuracy of disease detection compared to conventional methods. The dataset used consists of thousands of medical images that have been manually classified by medical professionals. The model training process was carried out using transfer learning techniques using pre-trained architectures such as VGG16 and ResNet50. Performance evaluation was carried out by measuring the values of accuracy, precision, recall, and F1-score. The results showed that the developed CNN model was able to achieve a detection accuracy level of up to 95.3% on the test dataset. The application of this technology is expected to support computer-based diagnostic systems (CDI) as an aid for medical personnel in clinical decision-making. In addition, this system has the potential to accelerate the screening process and reduce the risk of misdiagnosis. These findings indicate that deep learning technology has enormous potential for improving the quality of healthcare services, particularly in the areas of disease prevention and early detection more effectively and efficiently.
Co-Authors A.A. Ketut Agung Cahyawan W Abdul Matin Abdul Matin Afif, Muslihul Afriyanto, Juwandi Ahmad Juhaidi Ahmad Umar Ahmad Umar Ahmad Umar Akhmad Anwar Dani Aminuddin Aminuddin anastasia Andika, Aryan Anggraini, Novianni Ariana, Erina Friesca Arief Hendra Kurniawan Az Zahra, Khofifah Baidi Bainah, Bainah Burhan Ibnu Mubtadi Chusniyah, Atin Dwi Ratnawati, Yunie Fachrudin Abdul Ganie Fajar Mahfiroh Fauzi Annur Fetty Ernawati Fitri Ana Ika Dewi Hakim, Tio Imam Hakiman Hamid Abdillah Hanik Afidatur Rofiah Hariningsih, Hariningsih Hastuti, Tri Yuni Hasyim Asyari Ida Royani Irma Solikhah, Putri Iskandar, Riki Istilamah Laili Junanto, Subar Khofifah Umi Rahmawati Khuriyah . Kusyaeni Mighfar, Shokhibul Mudofir Muhamad Nur Zaman Muhammad Fajar Tri Santoso Muslimah Susilayati Mustauda, Amy Mutadi Mutadi Nadia Nur Ngalimun Ngalimun Noor Alawiyah Noor Alwiyah Nugrahani, Mita Nur Wachid Supriyadi Nurdin, Akhmad Nurul Maya Astuti Nurul Rahmawati Purwanto Puspitasari, Veronicca Rahayu, Watik Raysita Gusti Purbaningrum Retno Wahyuningsih Ridwan Afandi RISWAN, DEDY Riyanto, Nafiah Lintang Rosalia Sari Rosyid Al Hassany, Ibnu Rustam Ibrahim Rustam Sidiq Saiddaeni Shokhibul Mighfar Sidiq Nugroho Sidiq, Rustam Siti Choiriyah Siti Maratush Sholihah Sukarno Sukarno Sulistiyono, Tato Priyo Suparni Suparni Suprapti Suprapti Supriyadi, Nur Wahid Supriyanto Supriyanto Susilo Surahman Susiloningsih, Susiloningsih Sutiyoko Sutiyoko Sutiyoko Sutiyoko Sutiyoko, Sutiyoko Suwarta Suwarta Syamsudin, Arif Tajudin, Anas Tato Priyo Sulistyono Tugino Tugino Umami, Lailia Hanif Umar, Ahmad Uwais Chesoh Wahyuningsih, Tuti Yuliati - YUNIAWAN, EKO Zaenal Muttaqin Zahruddin Zahruddin, Zahruddin Zulfa, Izzah