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All Journal IAES International Journal of Artificial Intelligence (IJ-AI) Jurnal Teknologi Informasi dan Ilmu Komputer Jurnal Edukasi dan Penelitian Informatika (JEPIN) Jurnal Informatika dan Teknik Elektro Terapan Panrita Abdi - Jurnal Pengabdian pada Masyarakat JOIV : International Journal on Informatics Visualization Sinkron : Jurnal dan Penelitian Teknik Informatika Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Journal of Information Technology and Computer Science (JOINTECS) Jurnal Ilmiah FIFO JURNAL TEKNIK INFORMATIKA DAN SISTEM INFORMASI ILKOM Jurnal Ilmiah Jiko (Jurnal Informatika dan komputer) Conference on Innovation and Application of Science and Technology (CIASTECH) JOURNAL OF SCIENCE AND APPLIED ENGINEERING Applied Technology and Computing Science Journal Jurnal Aplikasi Dan Inovasi Ipteks SOLIDITAS Systematics Buana Information Technology and Computer Sciences (BIT and CS) Jurnal Mnemonic JATI (Jurnal Mahasiswa Teknik Informatika) Jurnal Pengabdian Masyarakat Multidisiplin Jurnal Teknik Informatika (JUTIF) Journal of Applied Data Sciences Jurnal FASILKOM (teknologi inFormASi dan ILmu KOMputer) International Conference on Industrial Revolution for Polytechnic Education International Journal of Community Service (IJCS) Prosidia Widya Saintek Jurnal Abdimas Mahakam
Aviv Yuniar Rahman
Universitas Widyagama Malang
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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.
Co-Authors Adi Saputra, Deni Adya Hermawati Affi Nizar Suksmawati Ahmad Farhan, Ahmad Ahmad Fauzi Anderias Bai Seran April Hananto April Lia Hananto arrozi, lalu muhamad alawi Bagus Setyawan Budiawan, Renaldi Widi Chauliah Fatma Putri Da Costa, Zania Abuk Daiman, Claudia Nila Dio Amin Putra Eko Sediyono Fauzi Ahmad Muda Firman Nurdiansyah Firman Nurdiyansyah Firman Nurdiyansyah, Firman Hananto, Agustia Hendry Hero Diogenes Adoe Huda, Baenil Ilhamsyah Ilhamsyah Indra, Jamaludin Istiadi Istiadi Istiadi Joko Purwanto khusniyatul latifah Kristianingrum Kristianingrum, Kristianingrum Luruk, Maria Ovalia Ma'rifani Fitri Arisa Mamba’us Sa’adah Maria Alfanita Bhia, Meisya Muhammad Romadloni Putra Muslim, Shinta Nilam Sari Napulun, Kanisius Neno, Adi nisti, Melita Novalia, Elfina Nudiyansyah, Firman Nusman, Bayu Pahlevi Putra, Rangga Paryono, Tukino Priyandoko Gigih Priyatna, Bayu Putera, Rangga Pahlevi Putra, Rangga Pahlevi Putra, S.Pd., M.T., Rangga Pahlevi Putra Ratna Hidayati Rudy Joegijantoro, Rudy Rukmanta Jayawiguna Sabar Setiawidayat Setiawan, Feddy Wanditya Sidi, Husri Suhada, Karya Suksmawati, Affi Nizar Syah, Adryan Syahroni Wahyu Iriananda, Syahroni Wahyu Ursaputra Pratama, Lionardi Wisnu, Alif Dio Raka Yuliana Rachmawati