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Journal : Applied Technology and Computing Science Journal

Classifying Dental Caries Types Using Panoramic Dental Images Using Watershed Method and Multiclass Support Vector Machine Putra, S.Pd., M.T., Rangga Pahlevi Putra; Rahman, Aviv Yuniar
TEKNOLOGI DITERAPKAN DAN JURNAL SAINS KOMPUTER Vol 6 No 2 (2023): December
Publisher : Universitas Nahdlatul Ulama Surabaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33086/atcsj.v6i2.5910

Abstract

Teeth are one of the calcified and hard structures found in the human mouth. One of the tooth defects that often appears and is experienced by several people in the world is damage caused by dental caries. Diseases that can arise from dental caries include swelling of the gums and fever in the body. To classify and determine the level of damage in dental disease, dentists usually utilise examinations through dental panoramic images. Dental panoramic images are digital images of x-rays that can help provide a lot of information about teeth such as cavities or tooth structure. However, the problem that occurs sometimes to identify or classify the type of caries is still found to be a mismatch of analysis so that technological aids are needed to provide analysis or decision support. Therefore, by applying digital image processing technology by applying methods in image processing, namely Watershed segmentation and the Multiclass Support Vector Machine method, it is possible to classify the type of caries using dental panoramic images. From the results of the research conducted, it can be explained that the results of segmentation of dental panoramic images using the Watershed method can show the detected caries area spots. Meanwhile, the use of the Multiclass SVM method for the classification method shows accuracy results reaching 88%.
Disease Segmentation in Purple Sweet Potato Images Using Yolov7 khusniyatul latifah; Aviv Yuniar Rahman; Istiadi
TEKNOLOGI DITERAPKAN DAN JURNAL SAINS KOMPUTER Vol 7 No 1 (2024): June
Publisher : Universitas Nahdlatul Ulama Surabaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33086/atcsj.v7i1.6023

Abstract

Purple sweet potato is a very important plant in many parts of the world and a major crop in tropical and subtropical climates. Its cultivation can significantly increase production and consumption, and it is beneficial for the nutritional status of people in both rural and urban areas. However, purple sweet potatoes are susceptible to disease outbreaks, which can cause substantial losses to the agricultural industry. To prevent the spread of these diseases and minimize financial losses, it is crucial for farmers to identify purple sweet potato diseases as early as possible. Utilizing deep learning technology to separate areas of purple sweet potatoes marked with disease can effectively address this problem. In this study, researchers employed a segmentation method using the YOLOv7 algorithm. The study's results demonstrated a mean Average Precision (mAP) value of 98.6% from a dataset of 1500 images, divided into two classes: healthy sweet potatoes and diseased sweet potatoes with tuber rot. The mAP value for healthy sweet potatoes was 96.1%, while the mAP for diseased sweet potatoes with tuber rot was 98.6%. The YOLOv7 method, therefore, produces high accuracy values for the segmentation of purple sweet potato diseases. This research significantly contributes to agriculture by enhancing the productivity and quality of sweet potato harvests and can assist farmers in improving the efficiency and sustainability of purple sweet potato production.