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All Journal Jurnal Ilmiah Teknik Elektro Komputer dan Informatika (JITEKI) PROtek : Jurnal Ilmiah Teknik Elektro JEEMECS (Journal of Electrical Engineering, Mechatronic and Computer Science) Techne : Jurnal Ilmiah Elektroteknika CYCLOTRON Prosiding Seminar Nasional Sains dan Teknologi Terapan JEECAE (Journal of Electrical, Electronics, Control, and Automotive Engineering) JASIEK (Jurnal Aplikasi Sains, Informasi, Elektronika dan Komputer) Abdimas: Jurnal Pengabdian Masyarakat Universitas Merdeka Malang JEEE-U (Journal of Electrical and Electronic Engineering-UMSIDA) Jurnal Fortech SinarFe7 Jurnal Penelitian Journal of Mechanical and Electrical Technology Blend Sains Jurnal Teknik Prosiding Seminar Nasional Pengabdian Masyarakat Emitor: Jurnal Teknik Elektro Uranus: Jurnal Ilmiah Teknik Elektro, Sains dan Informatika Jurnal FORTECH
Arifuddin, Rahman
Program Studi Teknik Elektro Universitas Merdeka Malang
Arts Humanities Automotive Engineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Electrical & Electronics Engineering Energy Engineering Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Mathematics Mechanical Engineering Medicine & Pharmacology Social Sciences

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Journal : Techne : Jurnal Ilmiah Elektroteknika

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Klasifikasi Penyakit Pneumonia menggunakan Metode Convolutional Neural Network (CNN) Office, Irfan Handy; Arifuddin, Rahman; Hidayatulail, Basitha Febrinda
Techné : Jurnal Ilmiah Elektroteknika Vol. 23 No. 2 (2024)
Publisher : Fakultas Teknik Elektronika dan Komputer Universitas Kristen Satya Wacana

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31358/techne.v23i2.491

Abstract

Pneumonia is an acute lung infection that affects lung tissue. The disease can be caused by various pathogens such as viruses, bacteria, fungi, and others. COVID-19 pneumonia is a serious condition that requires special attention because of its contagious nature and its severe symptoms, including high fever, difficulty breathing, and lack of oxygen. Diagnosis usually depends on clinical symptoms and imaging techniques such as chest x-rays. With automatic classification technology, pneumonia detection becomes more efficient. The study used Convolutional Neural Network (CNN) with ResNet50 architecture to classify types of pneumonia, including viral pneumony and COVID-19, from chest x-rays. Research methods include literature reviews, data collection, pre-processing, modeling, training, testing, and evaluation using metrics such as accuracy, precision, recall, and F1 scores. Experiments with different epochs yield 99% accurate training data, 81% accurate validation data, and a lack of learning on models that influence accurability on validation.
Co-Authors Abd Rabi Abd. Rabi’ Abdur Rabi Affan Bachri Agus Suprapmoko Akio Kitagawa Anggara Trisna Nugraha Aries Boedi Setiawan Arik Syaiful Rasid Arinda, Putri Surya Arya Sobhita, Rama Bylla Anggraini, Feby Salsa Dwi Arman Prasetya Dwi Arman Prasetya Dyah Setyawati E, Nachrowie Elta Sonalitha Febriyanti, Liyyana Hidayatulail, Basitha Febrinda Indriyanti , Fenti Dwi Iqbal Maulana, Fairuz Irawan, Moh. Sugeng Irfan Mujahidin Jehamat, Caecilia E. Alfita Krishna Tiwikrama Machmud Effendy Moh Sugeng Irawan Moh. Sugeng Irawan Moh. Sugeng Irawan Mutadafiq, Fiqhi Syahdani Nachrowie . Office, Irfan Handy Puput Dani Prasetyo Adi Putri Intan Hafsari1, Rizky Putri Surya Arinda Renyaan, Edward Hariyanto Bidu Resi Dwi Jayanti Kartika Sari Respati Wikantiyoso Ridlo, Muhammad Ali Sari, Resi Dwi Jayanti Kartika Sinatra, Yanuar Stanislaus Yoseph Subairi Subairi Sumarahinsih, Andrijani Suprapmoko, Agus Susanto, Hanum Vemmi Tito Ahmad Fauzan Wahyu Dirgantara wangge, ferdinandus Wardhana, Rama Yovi Afriansyah Yunaldi Adang Yunus Novrianto Ziliwu, Alsaro Natanael