Article Per Year (5 Year)
p-Index From 2021 - 2026
0.408
P-Index
This Author published in this journals
All Journal Journal of Electronics, Electromedical Engineering, and Medical Informatics Proceedings Series of Educational Studies
Triwiyanto, T
Unknown Affiliation
Computer Science & IT Control & Systems Engineering Education Electrical & Electronics Engineering Engineering

Published : 2 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 2 Documents
Search

 1 
QCML: Qualified Contrastive Machine Learning methodology for infectious disease diagnosis in CT images Chandrika, G.Naga; Karpagam, J.; Richard, Titus; Shadrach, Finney Daniel; Triwiyanto, T
Journal of Electronics, Electromedical Engineering, and Medical Informatics Vol 6 No 2 (2024): April
Publisher : Department of Electromedical Engineering, POLTEKKES KEMENKES SURABAYA

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35882/jeeemi.v6i2.391

Abstract

The COVID-19 pandemic has had a terrible effect on human health, and computer-aided diagnostic (CAD) systems for chest computed tomography have emerged as a potential alternative for COVID-19 diagnosis. Yet, since the cost of data annotation may be excessively costly in the medical area, there is a shortage of data that has been annotated. A considerable quantity of labelled data is required in order to train a CAD system to a high level of accuracy. The study aims to describe an automatic and precise COVID-19 diagnostic method that utilizes a restricted amount of labelled CT images to solve this problem. The framework of the system is known as Qualified Contrastive Machine Learning (QCML), and the improvements that we have made may be summed up as follows: 1) In order to make use of all of the image's characteristics, we combine features with a two-dimensional discrete wavelet transform. 2) We employ the COVID-Net encoder with a redesign that focuses on the efficiency of learning and the task specificity of the data. 3) In order to strengthen our capacity to generalize, we have implemented a novel pertaining technique that is based on Qualified Contrastive Machine Learning. 4) In order to get better categorization results, we have included an extra auxiliary work. The application of Qualified Contrastive Machine Learning methodology for infectious disease diagnosis in CT images offers an accuracy of 93.55%, a recall of 91.59%, a precision of 96.92%, and an F1-score of 94.18%, demonstrating the potential for accurate and efficient COVID-19 diagnosis with limited labelled data.
The Importance of Analyzing the Needs of School Administration Personnel in Managing Human Resources Faradiba, Nabil Aurora; Burhanuddin, Burhanuddin; Triwiyanto, T
Proceedings Series of Educational Studies 2024: Proceedings of the International Seminar Universitas Negeri Malang Indonesia – Universiti Mala
Publisher : Universitas Negeri Malang

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

Abstract

This research focuses on the importance of analyzing the needs of school administration personnel in managing human resources to support the success of the educational process. Through literature studies, this study analyzes various aspects related to the management of school administration personnel ranging from needs analysis, planning, recruitment, competency development, to performance evaluation. Currently, there is still a shortage of school administration personnel and even their performance competence. Therefore, it requires effective and efficient management of school administration personnel, including needs analysis, careful planning, and continuous competency development. The optimal performance of school administration personnel can contribute significantly to improving the quality of education in schools. This study uses a descriptive qualitative method with a literature study approach. Data is obtained from various sources, such as scientific journals and articles. This article provides guidance for schools in improving the competence and management of school administration personnel through a planned and systematic approach.
Co-Authors Burhanuddin Burhanuddin Chandrika, G.Naga Faradiba, Nabil Aurora Karpagam, J. Richard, Titus Shadrach, Finney Daniel