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All Journal Unri Conference Series: Community Engagement Jurnal Computer Science and Information Technology (CoSciTech) BIOS : Jurnal Teknologi Informasi dan Rekayasa Komputer Jurnal Sistim Informasi dan Teknologi
Sy, Yandiko Saputra
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Humanities Computer Science & IT Decision Sciences, Operations Research & Management

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Journal : Jurnal Computer Science and Information Technology (CoSciTech)

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Deep Learning untuk mendeteksi gangguan lambung melalui citra iris mata Mukhtar, Harun; Baidarus; Aryanto, Eggy; Saputra Sy, Yandiko
Computer Science and Information Technology Vol 4 No 3 (2023): Jurnal Computer Science and Information Technology (CoSciTech)
Publisher : Universitas Muhammadiyah Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37859/coscitech.v4i3.6392

Abstract

The stomach is one of the essential organs of the human digestive system. If the stomach organ cannot work typically, it will cause problems. This is a disease that occurs in the stomach organs. Gastric disease also occurs due to a lack of knowledge about stomach disease, so people ignore the symptoms that arise. Gastric disease is a disease that is considered very serious. If left alone, it can cause other diseases to occur. Generally, finding out the presence of stomach disease is still done manually, and several tests are carried out when stomach disease has recurred. Gastric disorders were classified using 360 iris images taken manually via a digital camera and a web database of iris images. The author used the Radial Basis Function Neural Network (RBFNN) method to classify iris images of patients with gastric disorders in this study. The results obtained from this research can organize the iris images of people with gastric disturbances. Classification of iris images of patients with gastric disorders achieved a training accuracy rate of 65.00%.
Klasifikasi Kebakaran Hutan Dan Lahan Dengan Algoritma You Only Learn One Representation Rizki, Yoze; Yogi Alfinaldo; Soni; Sy, Yandiko Saputra; Rahmad Firdaus
Computer Science and Information Technology Vol 4 No 3 (2023): Jurnal Computer Science and Information Technology (CoSciTech)
Publisher : Universitas Muhammadiyah Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37859/coscitech.v4i3.6434

Abstract

Forest areas have a function of storing carbon dioxide and producing oxygen from trees and plants. The function of forests is very important for life, so forests are highly protected. One solution that can be taken is to take preventive measures, namely monitoring fire hotspots in forest and land areas by air. This research was tested using the same dataset as the YOLO (You Only Look Once) algorithm against the You Only Learn One Representation (YOLOR) algorithm with a train data division model of 1188 image data and test data of 75 image data with mAP results of 66.36%. . So it can be confirmed that the YOLOR algorithm is better than the YOLO algorithm which gets an mAP value of 50.65%.
Co-Authors Aidil Fitriansyah Aryanto, Eggy Baidarus Finanta Okmayura Harun Mukhtar Husnah, Mirdatul Mahdiyah, Evfi Rahmad Firdaus Rahmat Hidayat Risanto, Joko Rizki, Yoze Roni Salambue Saputra, Haris Tri Soni Sukamto Sukamto Teguh Sujana Yogi Alfinaldo Zaiful Bahri