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All Journal Jurnal Simetris Bulletin of Electrical Engineering and Informatics International Journal of Advances in Intelligent Informatics ELKOMIKA: Jurnal Teknik Energi Elektrik, Teknik Telekomunikasi, & Teknik Elektronika JETT (Jurnal Elektro dan Telekomunikasi Terapan) TEKTRIKA - Jurnal Penelitian dan Pengembangan Telekomunikasi, Kendali, Komputer, Elektrik, dan Elektronika Journal of Applied Data Sciences Jurnal Pengabdian Masyarakat Indonesia Prosiding Konferensi Nasional PKM-CSR SinarFe7 eProceedings of Applied Science eProceedings of Engineering Jurnal Rekayasa elektrika Jurnal INFOTEL
YULI SUN HARIYANI
Telkom University
Humanities Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Environmental Science Industrial & Manufacturing Engineering Nursing Public Health Social Sciences

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CS-based Lung Covid-Affected X-Ray Image Disorders Classification using Convolutional Neural Network Biyantika Emili Triasari; Gelar Budiman; Siti Sarah Maidin; M. Izham Jaya; Yuli Sun Hariyani; Indrarini Dyah Irawati; Zhong Zhao
Journal of Applied Data Sciences Vol 5, No 4: DECEMBER 2024
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v5i4.371

Abstract

Lung diseases, such as pneumonia, significantly affect the respiratory system, especially the lungs. This condition causes various degrees of lung damage in patients of all age groups, including the elderly, adults, and children. Even after treatment and recovery, diagnosing lung damage remains important, which can be done using rapid tests, clinical evaluations, CT scans, or X-rays. This study focuses on the classification of X-ray images of lungs affected by pneumonia and normal lungs, using the Convolutional Neural Network method based on Compressive Sensing (CS) simulated using MatLab. The purpose of the study is to determine the performance by calculating the confusion matrix value. The number of datasets used for normal lungs and those affected by pneumonia is 300 X-ray images from several different sources, with 60% training data, 30% validation, and 10% testing. The addition of the compression process causes a decrease in image quality, expressed in PSNR, as well as a decrease in classification parameters such as accuracy. Compared with previous research, the system without compression produces the highest accuracy. The results of the study can help classify lungs affected by pneumonia.
Co-Authors A.A. Ketut Agung Cahyawan W Achmad Rizal Adiwijaya, Agustinus Aldian Aulia Ayu Dyah Lestari Aulya Ellanda Ayu Warni Pertiwi Bambang Hidayat Biyantika Emili Triasari Chandraditya Aridela Cucu Fitri Devira Anggi Maharani Dinda Rizki Taningrum DWI ANDI NURMANTRIS Farrel Fahrozi FATURRAHMAN, RAIHAN Fauziah, Siti Elna Gelar Budiman Gelar Budiman Hafidh, Mochammad Fakhrizal Hasanah Putri Hurianti Vidyaningtyas Indrarini Dyah Irawati Inung Wijayanto Iqbal Eshar Dwi Pourindra Javani Sekar Larasati Jaya, M. Izham Kartika Oktaviani M. Bahrul Ilmi M. Izham Jaya Maidin, Siti Sarah Mochamad Erkki Svante Nyfors Mochammad Fakhrizal Hafidh Muhammad Raihan Nadya Putri Yuliani Nadya Putri Yuliani Naufal Juhaidi Jafal Nydia Amelinda Putri Pertiwi, Ayu Warni Raditiana Patmasari Randi Yusuf Nasution Ratna Mayasari Rayyan Budhiarta Ridha Muldina Negara ROHMAT TULLOH Satrio Nur Adhi Gyat Siti Sarah Maidin Suci Aulia Sugondo Hadiyoso Thomhert Suprapto Siadari Triasari, Biyantika Emili Zhao, Zhong Zhong Zhao