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All Journal International Journal Of Computer, Network Security and Information System (IJCONSIST) Literasi Nusantara
Karim, Mohammad Daniel Sulthonul
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Arts Humanities Chemistry Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Earth & Planetary Sciences Education Energy Engineering Environmental Science Languange, Linguistic, Communication & Media Library & Information Science Mathematics Neuroscience Physics Social Sciences Other

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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.
Enhancing Guest Security in Smart Hospitality: Face Recognition-Based Hotel Room Verification Using Haar Cascade Algorithm Putra, Adzanil Rachmadhi; Prayoga, Aji; Gumiwang, Zacky Yaser Malik; Karim, Mohammad Daniel Sulthonul; Wicaksono, Muhammad Galang Satrio; Faishol, Olive Khoirul Lukluil Maknun Al; Prisyanti, Affifiana
IJCONSIST JOURNALS Vol 7 No 1 (2025): September
Publisher : International Journal of Computer, Network Security and Information System

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33005/ijconsist.v7i1.163

Abstract

This study aims to design and implement a hotel room verification system based on facial recognition using the Haar Cascade algorithm. The research was motivated by the growing need to enhance both security and service efficiency in the modern hospitality industry. The study was conducted through several stages, including facial image data collection using a webcam, preprocessing (RGB to grayscale conversion, image resizing, and cropping), model training, and real-time face recognition testing. The Haar Cascade algorithm was employed to detect facial features by utilizing Haar-like features combined with the Adaboost method to accelerate classification. The experimental results showed a recognition accuracy of 55% under varying lighting conditions and viewing angles. These findings indicate that the Haar Cascade algorithm performs adequately in detecting faces under ideal conditions, although further optimization is required to handle lighting variations and facial stability. This research contributes to the application of artificial intelligence technology in hotel security systems, with potential future improvement through the integration of deep learning methods to enhance accuracy and reliability in face verification. Keywords: face recognition, Haar Cascade, hotel room verification, facial detection, digital security.
Co-Authors Adzanil Rachmadhi Putra Affifiana Prisyanti Diyasa, I Gede Susrama Mas Eva Yulia Puspaningrum Faishol, Olive Khoirul Lukluil Maknun Al Gumiwang, Zacky Yaser Malik Prayoga, Aji Wicaksono, Muhammad Galang Satrio