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All Journal Jurnal Buana Informatika Indonesian Journal of Information System Majalah Anestesia & Critical Care (MACC) JoDENS: Journal of Digital Ecosystem for Natural Sustainability
Yodi
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Computer Science & IT Decision Sciences, Operations Research & Management Medicine & Pharmacology

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Risiko Perdarahan Spontan pada Pasien COVID-19 dengan Terapi Antikoagulan - Serial Kasus Putra, Marco Manza Adi; Yodi; Dalimunthe, Aldi Hafiz
Majalah Anestesia & Critical Care Vol 40 No 2 (2022): Juni
Publisher : Perhimpunan Dokter Spesialis Anestesiologi dan Terapi Intensif (PERDATIN) / The Indonesian Society of Anesthesiology and Intensive Care (INSAIC)

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (733.853 KB) | DOI: 10.55497/majanestcricar.v40i2.246

Abstract

Pandemi COVID-19 merupakan masalah kesehatan global dan menjadi tantangan tertinggi diseluruh dunia saat ini. Manifestasi klinisnya dapat bersifat ringan, sedang dan berat. Pada kasus sedang-berat, infeksi COVID-19 menyebabkan peningkatan d-Dimer dan degradasi produk fibrinogen. Hal ini disebabkan oleh keadaan hiperkoagulasi yang berhubungan dengan inflamasi dan trombosis. Keadaan hiperkoagulasi dapat menyebabkan kejadian tromboemboli pada vena dan arteri yang berhubungan dengan prognosis buruk pada pasien. World Health Organization (WHO) merekomendasikan penggunan antikoagulan profilaksis untuk mencegah terjadinya tromboemboli pada pasien COVID-19. Penggunaan antikoagulan profilaksis diharapkan dapat mengontrol bekuan darah (blood cloting) dan mengurangi pembentukan mikrotrombus. Namun, penggunaan antikoagulan juga dapat meningkatkan resiko terjadinya perdarahan. Pada serial kasus ini akan dibahas kejadian perdarahan spontan pada pasien COVID 19 yang diberikan antikoagulan sebagai profilaksis.
Mango and Banana Ripeness Detection based on Lightweight YOLOv8 Saragih, Raymond Erz; Purnajaya, Akhmad Rezki; Syafrinal, Ilwan; Pernando, Yonky; Yodi
Jurnal Buana Informatika Vol. 15 No. 2 (2024): Jurnal Buana Informatika, Volume 15, Nomor 02, Oktober 2024
Publisher : Universitas Atma Jaya Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

Fruits like bananas and mangoes are harvested after reaching a specific ripeness stage. Traditionally, farmers rely on manual inspection to determine ripeness, a process that can be tedious, time-consuming, expensive, and subjective. This work proposes an automatic bananas and mangoes ripeness detector utilizing computer vision technology. The detected bananas and mangoes fall into two classes: ripe and unripe. The state-of-the-art YOLOv8 architecture serves as the core of the detector. Three YOLOv8 variants, YOLOv8n, YOLOv8s, and YOLOv8m, were investigated for their performance. Results show that YOLOv8s achieved the highest overall performance, 0.9991 recall, and a mean Average Precision (mAP) of 0.8897. While YOLOv8m achieved the highest precision of 0.9995, YOLOv8n is the most miniature model, making it suitable for deployment on devices with limited resources.
Coral Detection based on Optimised Lightweight YOLO Model Saragih, Raymond Erz; Husin, Husna Sarirah; Mursalim, Muhammad Khairul Naim; Yodi
Indonesian Journal of Information Systems Vol. 8 No. 1 (2025): August 2025
Publisher : Program Studi Sistem Informasi Universitas Atma Jaya Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24002/ijis.v8i1.11628

Abstract

Coral reefs are essential marine ecosystems that face significant threats due to climate change, pollution, and overfishing. Effective monitoring is crucial for conservation efforts, but traditional methods are labor-intensive and inefficient. This study proposes a deep learning-based coral detection model built based on the YOLOv8 architecture, specifically for nano and small. In addition, the Ghost modules and Ghost bottlenecks were utilized to modify the original YOLOv8 small. The proposed model was trained on an underwater coral dataset and evaluated in terms of precision, recall, and mean Average Precision (mAP) metrics. Experimental results demonstrate that the YOLOv8 small model and YOLOv8 small model with Ghost modules achieved a mAP of 53.675% and 55.88%, respectively, while maintaining a compact model size. This work contributes to developing efficient and lightweight coral detection systems to support conservation efforts.
Predicting English Language Learners’ Proficiency Level Using EnglishScore Android Application Anton, Oey; Yodi
Journal of Digital Ecosystem for Natural Sustainability Vol 1 No 2 (2021): Desember 2021
Publisher : Fakultas Komputer - Universitas Universal

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.63643/jodens.v1i2.61

Abstract

Teaching and learning the English language need to focus on the learners; therefore, as teachers, giving the range of materials and creating the study programs must be based on learners' ability to receive the learning progress in accordance. So, by revealing learners' abilities, teachers can organize their syllabus, material levels, quizzes, and other programs properly. CEFR is a British Council assessment used to know learners' abilities in several languages, including English. In this paper, we use EnglishScore android application to assess the learners' abilities based on CEFR scales. There are five categories of CEFR level scales that the learners of Universitas Universal academic year 2021-2022 provided: C1, B2, B1, A2, A1- using EnglishScore android application for assessment. Most learners acquire A2 level and B1 level with 40 and 28 percentages for overall skills. A2 and B1 with 40 and 28 percentages for grammar achievement, while B1, B2, and A2 for vocabulary achievement with 33, 27, and 26 percentages. A2 and B1 levels for reading and listening skills, with 42 and 23 percentages for reading skills and 39 and 22 for listening practice.
Mango and Banana Ripeness Detection based on Lightweight YOLOv8 Saragih, Raymond Erz; Purnajaya, Akhmad Rezki; Syafrinal, Ilwan; Pernando, Yonky; Yodi
Jurnal Buana Informatika Vol. 15 No. 2 (2024): Jurnal Buana Informatika, Volume 15, Nomor 02, Oktober 2024
Publisher : Universitas Atma Jaya Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

Fruits like bananas and mangoes are harvested after reaching a specific ripeness stage. Traditionally, farmers rely on manual inspection to determine ripeness, a process that can be tedious, time-consuming, expensive, and subjective. This work proposes an automatic bananas and mangoes ripeness detector utilizing computer vision technology. The detected bananas and mangoes fall into two classes: ripe and unripe. The state-of-the-art YOLOv8 architecture serves as the core of the detector. Three YOLOv8 variants, YOLOv8n, YOLOv8s, and YOLOv8m, were investigated for their performance. Results show that YOLOv8s achieved the highest overall performance, 0.9991 recall, and a mean Average Precision (mAP) of 0.8897. While YOLOv8m achieved the highest precision of 0.9995, YOLOv8n is the most miniature model, making it suitable for deployment on devices with limited resources.
Co-Authors Anton, Oey Dalimunthe, Aldi Hafiz Husin, Husna Sarirah Ilwan Syafrinal MUHAMMAD KHAERUL NAIM MURSALIM Pernando, Yonky Purnajaya, Akhmad Rezki Putra, Marco Manza Adi Saragih, Raymond Erz