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All Journal Jurnal Teknologi Informasi dan Ilmu Komputer Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control MATRIK : Jurnal Manajemen, Teknik Informatika, dan Rekayasa Komputer Jurnal Teknologi Informasi, Komputer, dan Aplikasinya (JTIKA ) JATI (Jurnal Mahasiswa Teknik Informatika) Jurnal Abdi Insani JOURNAL OF INFORMATION SYSTEM RESEARCH (JOSH) Jurnal Teknik Informatika (JUTIF) Jurnal Begawe Teknologi Informasi AMPLITUDO: Journal of Science & Technology Innovation
Dwiyansaputra, Ramaditia
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Journal : Jurnal Teknik Informatika (JUTIF)

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CLASSIFICATION OF DENTAL CARIES DISEASE IN TOOTH IMAGES USING A COMPARISON OF EFFICIENTNET-B0, MOBILENETV2, RESNET-50, INCEPTIONV3 ARCHITECTURES Wahyuningsih, Wahyuningsih; Nugraha, Gibran Satya; Dwiyansaputra, Ramaditia
Jurnal Teknik Informatika (Jutif) Vol. 5 No. 4 (2024): JUTIF Volume 5, Number 4, August 2024 - SENIKO
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jutif.2024.5.4.2187

Abstract

Dental caries is a global metabolic disorder, influenced by complex interactions between the body and microbes, it's caused by prolonged exposure to a low pH environment, leading to demineralized carious lesions. If untreated, it can cause pain and eating difficulties, requiring emergency care and significantly impacting overall quality of life. Diagnosis process can be conducted through physical assessment and analyzing laboratory testing. Image-based artificial intelligence systems, particularly the EfficientNet-B0 model, is suggested as a resolution for classifying dental caries using tooth images. The study's goal is to assess EfficientNet-B0's performance in comparison to other CNN architectures and play a role in advancing medical image classification technology. The original dataset comprising 1554 images was initially collected. After augmentation, the dataset expanded to 6348 images. The data was then divided into three subsets of training, validation, and testing datasets with a distribution ratio of 70:15:15, respectively. From all the evaluated models, the EfficientNet-B0 demonstrated a quite commendable accuracy of 0.98% with overfitting tolerance of less than 2%. Having the same accuracy as the MobileNetV2 (0.98%). Despite its inability to exceed the accuracy achieved by ResNet-50 (0.99%), EfficientNet-B0 accomplished its accuracy level with roughly a quarter of the parameters than ResNet-50 and highger than InceptionV3 (0.97%), highlighting its efficiency in parameter utilization and computational resource management. These findings hold promise for enhancing models and guiding clinical decision-making.
Co-Authors A.M., Mursyidhan Ariefbillah Afwani, Royana Agitha, Nadiyasari Akhmad Saufi Akhyar, Halil Aranta, Arik Ario Yudo Husodo, Ario Yudo Budi Irmawati Darmawan, Muhammad Ilham Darmawan, Riski Dewi, Zaskia Elvina Dwi Ratnasari Ekaputra, Galang Prasetya Fadhilah, A. Nur Fitri Bimantoro Hadi, Risman Hamidi, Mohammad Zaenuddin Hanifah, Fairuz Hidayat, Lalu Ramdoni Hirkan, Muhamad Nurul I Gede Pasek Suta Wijaya Ika Murpratiwi, Santi Ita Selvia, Siska Kokong, Diah Anggreni Ratna Sari Kusuma, Fendi Putra Latifah, Nurun Manuaba, Ida Bagus Ryand Wirayana Maulana, Sutan Fajri Muhammad Dani Muhammad, Fathony Arroisy Nugraha, Gibran Satya Nurfauziyah, Nurfauziyah Nurhasiyah, Nurhasiyah Pahrul Irfan Paramarta, Muhammad Magistra Apta Rahayu, Sefani Cahyo Auliya Rassy, Regania Pasca Rizqullah, Muhammad Naufal Rohmawati, S. Antya Suhada, Destia Susi Rahayu Tiara, Baiq Najwa Tresna, I Made Agus Wahyuningsih Wahyuningsih Widiarta, I Putu Angga Purnama Widiyanti, Astrini