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Journal : Literasi Nusantara

Identification of Abnormal Spermatozoa Motility Using the SVM Algorithm Karim, Mohammad Daniel Sulthonul; Puspaningrum, Eva Yulia; Diyasa, I Gede Susrama Mas
Literasi Nusantara Vol. 5 No. 1 (2025): Literasi Nusantara: November 2024- February 2025
Publisher : Yayasan Citra Dharma Cindekia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56480/jln.v5i1.1324

Abstract

Spermatozoa motility is one of the key indicators in determining male fertility quality. Manual assessment of motility abnormalities often requires significant time and effort, thus necessitating a more efficient and accurate automated approach. This study aims to identify abnormalities in spermatozoa motility using the Support Vector Machine (SVM) algorithm, utilizing microscopic video data analyzed through TrackPy for spermatozoa trajectory tracking. The analysis process involves data acquisition, spermatozoa detection in each frame, sperm trajectory construction, and trajectory classification into normal or abnormal categories. The SVM model was trained using a dataset derived from spermatozoa trajectories classified based on parameters such as average velocity and trajectory linearity. The results show that the method achieved the highest accuracy of 89 percent in identifying spermatozoa motility abnormalities in HD resolution videos with a frame rate of 30 fps.