Article Per Year (5 Year)
p-Index From 2021 - 2026
2.462
P-Index
This Author published in this journals
All Journal Bulletin of Electrical Engineering and Informatics Indonesian Journal of Tropical and Infectious Disease JOIN (Jurnal Online Informatika) Fountain of Informatics Journal Jurnal ELTIKOM : Jurnal Teknik Elektro, Teknologi Informasi dan Komputer JOIV : International Journal on Informatics Visualization Paediatrica Indonesiana Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control JRST (Jurnal Riset Sains dan Teknologi) Dinamisia: Jurnal Pengabdian Kepada Masyarakat JSiI (Jurnal Sistem Informasi) JPPM (Jurnal Pengabdian dan Pemberdayaan Masyarakat) Jurnal Gema Ngabdi Jurnal Repositor Indian Journal of Forensic Medicine & Toxicology AL-UMRON: Jurnal Pengabdian kepada Masyarakat Khazanah Informatika : Jurnal Ilmu Komputer dan Informatika
Basuki, Setio
Universitas Muhammadiyah Malang
Aerospace Engineering Agriculture, Biological Sciences & Forestry Humanities Chemical Engineering, Chemistry & Bioengineering Chemistry Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Earth & Planetary Sciences Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Environmental Science Health Professions Medicine & Pharmacology Public Health Social Sciences Other

Published : 40 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 2 Documents
Search
Journal : JOIV : International Journal on Informatics Visualization

 1 
Classification of Malaria Using Convolutional Neural Network Method on Microscopic Image of Blood Smear Minarno, Agus Eko; Izzah, Tsabita Nurul; Munarko, Yuda; Basuki, Setio
JOIV : International Journal on Informatics Visualization Vol 8, No 3 (2024)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62527/joiv.8.3.2154

Abstract

Malaria, a critical global health issue, can lead to severe complications and mortality if not treated promptly. The conventional diagnostic method, involving a microscopic examination of blood smears, is time-consuming and requires extensive expertise. To address these challenges, computer-assisted diagnostic methods have been explored. Among these, Convolutional Neural Networks (CNN), a deep learning technique, has shown considerable promise for image classification tasks, including the analysis of microscopic blood smear images. In this study, we employ the NIH Malaria dataset, which consists of 27,558 images, to train a CNN model. The dataset is divided into parasitized (malaria-infected) and uninfected. The CNN architecture designed for this study includes three convolutional layers and two fully connected layers. We compare the performance of this model with that of a pre-trained VGG-16 model to determine the most effective approach for malaria diagnosis. The proposed CNN model demonstrates high accuracy, achieving a value of 96.81%. Furthermore, it records a recall of 0.97, a precision of 0.97, and an F1-score of 0.97. These metrics indicate a robust performance, outperforming previous studies and highlighting the model's potential for accurate malaria diagnosis. This study underscores the potential of CNN in medical image classification and supports its implementation in clinical settings to enhance diagnostic accuracy and efficiency. The findings suggest that with further refinement and validation, such models could significantly improve the speed and reliability of malaria diagnostics, ultimately aiding in better disease management and patient outcomes.
Named Entity Recognition in Medical Domain: A systematic Literature Review Kusuma, Selvia Ferdiana; Wibowo, Prasetyo; Abdillah, Abid Famasya; Basuki, Setio
JOIV : International Journal on Informatics Visualization Vol 8, No 4 (2024)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62527/joiv.8.4.3111

Abstract

Biomedical Named Entity Recognition (BioNER) is essential to bioinformatics because it identifies and classifies biological entities in biomedical texts. With the increasing number of biomedical literature and the rapid progress of the BioNER approach, it is essential to conduct a systematic literature review (SLR) on BioNER. This SLR consolidates existing information and provides directions for future studies in the BioNER field. This review systematically explores scientific journals and conferences published from 2019 to 2024. This research uses PubMed and Scholar as reference search databases because of their affiliation with other well-known publishers such as IEEE, Elsevier, and Springer. The results show a transition from conventional machine learning to deep learning. Neural networks and transformers show better performance in deep learning methods. The datasets often used in BioNER development are BC2GM, BC5CDR, and NCBI-Disease. Precision, Recall, and F1-Score are used in most papers to evaluate model performance. The performance of these models mostly depends on the availability of big annotated datasets and significant computational tools. Therefore, it is vital for future research to address the issues of annotated data and resource availability to build accurate models. Researchers should investigate the creation of ideal designs that lower computing complexity without compromising performance. Overall, this SLR offers a thorough overview of the latest research on BioNER. It provides significant insights for academics and practitioners in bioinformatics and medical research, helping them understand the innovative aspects of BioNER research.
Co-Authors Abdachul Charim Abdillah, Abid Famasya Agus Eko Minarno Akbi, Denar Regata Akmal Shahib, Maulana Alfira Rizky Alimuddin Hasan Al Kabir Aminudin Aminudin Amrul Faruq Aulia Arif, Wardhana Baiduri, Senja Bangkit Putrawan Charim, Abdachul Diany Yogiantoro Dini Tri Purwaningsih Dominicus Husada Dwiyanti Puspitasari, Dwiyanti Edo Ardhiansyah Effendy, Nico Ardia Faiqurrahman, Mahar Faizun Nuril Hikmah Gita Indah Marthasari Haq, Arini Hariyady Hariyady Hendra Saputra Hilman Hilman Hilwana, Lutifta Husada, Dominicius Indrabayu, Rizky Irfan, Muhammad irma fitriani Irwanto Irwanto Ismoedijanto Izzah, Tsabita Nurul Kartina, Leny Khoirir Rosikin Kusuma, Selvia Ferdiana Lina Dwi Yulianti Mahar Faiqurahman Mauridhi Hery Purnomo Mizwar Mizwar Muhammad Fadliansyah Muhammad Nasrul Tsalatsa Putra Muhammad Rizki Muhammad Yusuf Mustikasari, Rahma Ira Novita Daian Marlissa Nugraha, Muhammad Daffa Nur Hayatin Putri, Valencia Sefiana Rima Mediana Mashita Risa Etika, Risa Rizky Sulaiman Rizky, Alfira S, Vinna Rahmayanti Sari, Zamah Shafiyah, Rahajeng Febri Siti Maghfiroh Soegeng Soegiyanto Sumadi, Fauzi Dwi Setiawan Syafaah, Lailis Titin Eka Puspitawati Tsuchiya, Masatoshi Wibowo, Prasetyo Wicaksono, Galih Wasis Wisnujono Soewono Yuda Munarko Yufis Azhar Zachra, Fatimatus Zakiyah Rakhmawati Zamah Sari