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All Journal Jurnal Teknik Industri INOTEKS : Jurnal Inovasi Ilmu Pengetahuan, Teknologi, dan Seni J@TI (TEKNIK INDUSTRI) Jurnal Simetris Prosiding Seminar Nasional Sains Dan Teknologi Fakultas Teknik JURNAL PASTI (PENELITIAN DAN APLIKASI SISTEM DAN TEKNIK INDUSTRI) Jurnal Optimasi Sistem Industri Jurnal Ilmiah Teknik Industri Benefit: Jurnal Manajemen dan Bisnis Tekinfo | Scientific Journal of Industrial and Information Engineering PERFORMA : Media Ilmiah Teknik Industri Warta Penelitian Perhubungan SPEKTA (Jurnal Pengabdian Kepada Masyarakat : Teknologi dan Aplikasi) JENIUS: Jurnal Terapan Teknik Industri Syntax: Journal of Software Engineering, Computer Science and Information Technology Media Publikasi Promosi Kesehatan Indonesia (MPPKI) PELS (Procedia of Engineering and Life Science) International Journal of Applied Sciences in Tourism and Events Prosiding Simposium Nasional Rekayasa Aplikasi Perancangan dan Industri Applied Research in Science and Technology Jurnal Pengabdian Masyarakat dan aplikasi Teknologi Engagement: Jurnal Pengabdian Kepada Masyarakat Asian Journal of Social and Humanities Spektrum Industri Jurnal Penelitian Transportasi Darat Warta Penelitian Perhubungan
Bambang Suhardi
Jurusan Teknik Industri, Universitas Sebelas Maret, Jl. Ir. Sutami No. 36 A, Surakarta 57126
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EKSPLORASI PADA PEMETAAN KLASIFIKASI RADIOGRAF TORAKS PENYAKIT PARU-PARU MENGGUNAKAN CONVOLUTIONAL NEURAL NETWORK (CNN) Zai, Andreas Rezeki; Suhardi, Bambang; Nowo, Surya Tri; Rosnelly, Rika; Setiawan, Adil
Syntax : Journal of Software Engineering, Computer Science and Information Technology Vol 6, No 2 (2025): Desember 2025
Publisher : Universitas Dharmawangsa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.46576/syntax.v6i2.7296

Abstract

ABSTRAKAbstrak— Radiograf toraks (CXR) merupakan alat penting dalam diagnosis penyakit paru, namun interpretasinya memerlukan keahlian khusus dan berpotensi menimbulkan bias. Penelitian ini bertujuan mengeksplorasi kinerja lima arsitektur Convolutional Neural Network (CNN) berbasis transfer learning, yaitu VGG16, ResNet50, EfficientNetB0, DenseNet121, dan MobileNetV2, dalam mengklasifikasikan lima kelas penyakit paru-paru: bacterial pneumonia, COVID-19, tuberculosis, viral pneumonia, dan normal. Dataset yang digunakan dilengkapi dengan preprocessing CLAHE-RGB, augmentasi data, serta penanganan ketidakseimbangan kelas menggunakan class weighting. Evaluasi dilakukan dengan empat skenario epoch (5, 10, 15, dan 30), serta menggunakan metrik akurasi, precision, recall, F1-score, dan confusion matrix. Hasil menunjukkan bahwa model VGG16 pada epoch ke-15 memberikan performa terbaik dengan akurasi 93,95% dan F1-score 0,94. Penelitian ini menunjukkan bahwa kombinasi preprocessing yang tepat dan arsitektur CNN yang sesuai mampu meningkatkan akurasi klasifikasi penyakit paru secara signifikan. Kata Kunci— Convolutional Neural Network, Citra CXR, VGG16, Transfer Learning, CLAHE, Penyakit Paru. ABSTRACTAbstract— Chest radiography (CXR) is a vital tool in diagnosing pulmonary diseases, yet its interpretation often requires expert analysis and may involve subjectivity. This study explores the performance of five Convolutional Neural Network (CNN) architectures: VGG16, ResNet50, EfficientNetB0, DenseNet121, and MobileNetV2 for classifying five categories of lung conditions: bacterial pneumonia, COVID-19, tuberculosis, viral pneumonia, and normal. The dataset underwent preprocessing using CLAHE-RGB enhancement, data augmentation, and class balancing with class weighting. Each model was trained using four epoch scenarios (5, 10, 15, and 30) and evaluated based on accuracy, precision, recall, F1-score, and confusion matrix. The results indicate that VGG16 with 15 epochs achieved the best performance, reaching 93.95% accuracy and 0.94 F1-score. This study demonstrates that combining appropriate preprocessing techniques with suitable CNN architectures significantly enhances classification performance for pulmonary disease detection. Keywords— Convolutional Neural Network, CXR images, VGG16, Transfer Learning, CLAHE, Lung Disease.
Co-Authors Adiworo, Praditya Afif Hakim Afriezal Muslim Ahmad Ikhwan Setiawan, Ahmad Ikhwan Anastasia Febiyani Asturi, Rahmaniyah Dwi Bela Sindy Amelinda Budiana, Eko Prasetya Cahyo Utomo Chandra Pratiwi, Putri Daeng Masiki, Andri Dian Pambayung Dwi Aries Himawanto Eko Liquiddanu Eko Pujianto Frisheila Sely Apriliana Galda Nadhira, Afra Heru Prastawa Irwan Irwan Kadita, Maria Laksono, Widyo Larasaty, Perwita Aura Lulu Elvira MAIRAWATI, PUTY Mindandi, Zati Hulwani Mirwan Ushada Nadhira, Afra Galda Nidya Yutie Pramesti Nowo, Surya Tri Nur Hayati Priadythama, Ilham Prija T., D. Danardono D. Pringgo Widyo Laksono Pudjiantoro, Fathin Kusumo Pramesti Pujiyanto, Eko purwadi joko widodo Putri, Ade Aisyah Arifna Putri, Nabilla Indah PUTY MAIRAWATI Rahmaniyah Dwi Astuti Rahmaniyah Dwi Astuti Rahmaniyah Dwi Astuti Rahmaniyah Dwi Astuti Ramadhanty, Alfina Diva Rika Rosnelly, Rika Safitri, Devianty Salsabila, Unik Hanifah Sari, Fita Permata Sari, Riska Permana Setiawan, Adil Setiono, Adrian Kwanadi Suletra, Wayan Sulistiarini, Emma Budi Sulistiarini Sulistiyowati, Rina Syahiliantina, Annisa Syahliantina, Annisa Tri Nowo, Suryandika Tua, Timothy Gunung Wakhid Ahmad Jauhari Wardani, Serlita Vidinia Yoseph Tri Minarto Yoseph Tri Minarto, Yoseph Tri Zai, Andreas Rezeki