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All Journal Indonesian Journal of Electronics, Electromedical Engineering, and Medical Informatics Jurnal Pengabdian Rekayasa dan Wirausaha
Yufnanda, Muhammad Aditya
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Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Electrical & Electronics Engineering Engineering Health Professions Materials Science & Nanotechnology

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The Role of U-Net Segmentation for Enhancing Deep Learning-based Dental Caries Classification Yassar, Muhammad Keysha Al; Fitria, Maya; Oktiana, Maulisa; Yufnanda, Muhammad Aditya; Saddami, Khairun; Muchtar, Kahlil; Isma, Teuku Reza Auliandra
Indonesian Journal of Electronics, Electromedical Engineering, and Medical Informatics Vol. 7 No. 2 (2025): May
Publisher : Jurusan Teknik Elektromedik, Politeknik Kesehatan Kemenkes Surabaya, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35882/ijeeemi.v7i2.75

Abstract

Dental caries, one of the most prevalent oral diseases, can lead to severe complications if left untreated. Early detection is crucial for effective intervention, reducing treatment costs, and preventing further deterioration. Recent advancements in deep learning have enabled automated caries detection based on clinical images; however, most existing approaches rely on raw or minimally processed images, which may include irrelevant structures and noise, such as the tongue, lips, and gums, potentially affecting diagnostic accuracy. This research introduces a U-Net-based tooth segmentation model, which is applied to enhance the performance of dental caries classification using ResNet-50, InceptionV3, and ResNeXt-50 architectures. The methodology involves training the teeth segmentation model using transfer learning from backbone architectures ResNet-50, VGG19, and InceptionV3, and evaluating its performance using IoU and Dice Score. Subsequently, the classification model is trained separately with and without segmentation using the same hyperparameters for each model with transfer learning, and their performance is compared using a confusion matrix and confidence interval. Additionally, Grad-CAM visualization was performed to analyze the model's attention and decision-making process. Experimental results show a consistent performance improvement across all models with the application of segmentation. ResNeXt-50 achieved the highest accuracy on segmented data, reaching 79.17%, outperforming ResNet-50 and InceptionV3. Grad-CAM visualization further confirms that segmentation plays a crucial role in directing the model’s focus to relevant tooth areas, improving classification accuracy and reliability by reducing background noise. These findings highlight the significance of incorporating tooth segmentation into deep learning models for caries detection, offering a more precise and reliable diagnostic tool. However, the confidence interval analysis indicates that despite consistent improvements across all metrics, the observed differences may not be statistically significant.
Penerapan Sistem Pengelolaan Air Sisa Wudhu Otomatis Berbasis IoT di Mushalla Darul Faizin, Desa Kopelma Darussalam Amanda, Silviani; Yufnanda, Muhammad Aditya; Rizky, Muharratul Mina; Ramadhana, Rizka; Aulia, Niza; Dawood, Rahmad; Leo, Hendrik
Jurnal Pengabdian Rekayasa dan Wirausaha Vol 2, No 2 (2025)
Publisher : Universitas Syiah Kuala

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24815/jprw.v2i2.50390

Abstract

Kegiatan pengabdian kepada masyarakat ini dilaksanakan di Mushalla Darul Faizin, Banda Aceh, yang menghadapi permasalahan tingginya konsumsi air bersih akibat belum adanya sistem pengelolaan air wudhu yang efisien. Air sisa wudhu yang masih tergolong layak digunakan kembali selama ini langsung dibuang ke saluran pembuangan tanpa pemanfaatan lebih lanjut. Melalui kegiatan ini, tim pengabdian mengembangkan dan menerapkan sistem daur ulang air wudhu otomatis berbasis Internet of Things (IoT) yang berfungsi untuk mendeteksi, mengumpulkan, menyaring, serta mendistribusikan kembali air sisa wudhu untuk keperluan non-konsumsi seperti pembersihan lantai dan penyiraman tanaman. Sistem ini menggunakan mikrokontroler ESP32, sensor ultrasonik untuk pemantauan level air secara real-time, serta integrasi platform Telegram bot untuk memudahkan pengurus mushalla dalam melakukan monitoring dan kontrol jarak jauh. Hasil implementasi menunjukkan bahwa sistem beroperasi stabil dengan reliabilitas mencapai 98,2% dan mampu menghemat penggunaan air bersih hingga sekitar 30%. Selain menghasilkan solusi teknologis yang efisien, kegiatan ini juga meningkatkan kesadaran dan kapasitas pengurus mushalla dalam pengelolaan sumber daya air secara mandiri dan berkelanjutan.
Co-Authors Amanda, Silviani Aulia, Niza Isma, Teuku Reza Auliandra Leo, Hendrik Maya Fitria Muchtar, Kahlil Oktiana, Maulisa Rahmad Dawood Ramadhana, Rizka Rizky, Muharratul Mina Saddami, Khairun Yassar, Muhammad Keysha Al