Article Per Year (5 Year)
p-Index From 2020 - 2025
0.23
P-Index
This Author published in this journals
All Journal Journal of Technology Research in Information System and Engineering International Journal of Artificial Intelligence in Medical Issues
Ahmad Naswin
Unknown Affiliation
Computer Science & IT Dentistry Health Professions Medicine & Pharmacology Public Health

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

Found 2 Documents
Search

 1 
RANCANGAN TEXT EDITOR BERBASIS HURUF LONTARA Ahmad Naswin
JTRISTE Vol 4 No 1 (2017)
Publisher : STMIK KHARISMA Makassar

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (305.834 KB)

Abstract

Language that currently used in Indonesia is Bahasa. But in every province, city, district in Indonesia have their own traditional language. Traditional language in South Sulawesi Province also there are various languages, for example Bugis language, Makassar language, and others. In this study, a text editor application is created using lontara, which can be used to edit text documents, read text documents, etc., especially in Bugis / Makassar language. As well as one effort to preserve the traditional language of Bugis / Makassar.
Performance Analysis of the Decision Tree Classification Algorithm on the Pneumonia Dataset Ahmad Naswin; Adityo Permana Wibowo
International Journal of Artificial Intelligence in Medical Issues Vol. 1 No. 1 (2023): International Journal of Artificial Intelligence in Medical Issues
Publisher : Yocto Brain

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56705/ijaimi.v1i1.83

Abstract

The rapid advancements in machine learning have paved the way for innovative approaches in medical imaging diagnostics. In this context, this study explored the efficacy of the Decision Tree Classification Algorithm for distinguishing between normal and pneumonia-diagnosed X-ray images. We sourced our dataset from pediatric X-rays obtained from the Guangzhou Women and Children’s Medical Center. To enhance the classifier's performance, a methodical pre-processing strategy was adopted. This encompassed the application of the Canny segmentation technique, followed by feature extraction using humoments. The evaluation phase involved a 5-fold cross-validation, revealing a commendable average accuracy of 82.72%. These findings highlight not only the utility of Decision Trees in such specialized diagnostic tasks but also accentuate the pivotal role of systematic pre-processing in achieving optimal results. As medical diagnostics steadily move towards automation, this research provides valuable insights and benchmarks for future endeavors aiming to harness the power of machine learning in healthcare.
Co-Authors Adityo Permana Wibowo