Article Per Year (5 Year)
p-Index From 2021 - 2026
0.23
P-Index
This Author published in this journals
All Journal Jurnal Teknologi dan Manajemen Informatika
Nurul Hidayah
Universitas PGRI Ronggolawe (Jl. Manunggal No.61, Wire, Gedongombo, Kec. Semanding, Kabupaten Tuban, Jawa Timur 62391)
Computer Science & IT Decision Sciences, Operations Research & Management Engineering

Published : 1 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 1 Documents
Search

 1 
Application of VGG16 Deep Learning Architecture and K-Means for PCOS Severity Clustering Based on Ovarian Ultrasound Images Nurul Hidayah; Asfan Muqtadir; Andik Adi Suryanto
Jurnal Teknologi dan Manajemen Informatika Vol. 12 No. 1 (2026): Juni 2026
Publisher : Universitas Merdeka Malang

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

Abstract

Polycystic Ovary Syndrome (PCOS) is a complex hormonal disorder that significantly impacts the reproductive and metabolic health of women. A primary challenge in PCOS analysis is the reliance on subjective manual observations of ultrasound (USG) images, which are prone to inconsistency due to the lack of objective standards. This study proposes an automated, unsupervised framework to stratify the morphological characteristics of PCOS ultrasound images without predefined clinical labels. The method integrates feature extraction using the VGG16 deep learning architecture with K-Means clustering. The structural integrity of the computational clusters was quantitatively evaluated, achieving a Silhouette Score of 0.4248 and a Davies-Bouldin Index (DBI) of 0.9175, indicating the successful formation of distinct morphological groupings. Furthermore, an evaluation using a Support Vector Machine (SVM) to assess the internal consistency and linear separability of the extracted features yielded a test accuracy of 97.54%. The results demonstrate that the integration of VGG16 and K-Means effectively partitions PCOS ultrasound images into objective, computationally stable morphological groups. This approach is designed to function as a standardized clinical decision support tool, mitigating visual subjectivity and providing a measurable baseline for women's reproductive health management.
Co-Authors Andik Adi Suryanto Asfan Muqtadir