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All Journal EDUMATIC: Jurnal Pendidikan Informatika Building of Informatics, Technology and Science
Muttaqin, Almas Najiib Imam
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Computer Science & IT Education

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Komparasi Deteksi Single Shot Detector (SSD) Dengan YouLook (Yolov8) Menggunakan GhostFaceNet Untuk Pengenalan Wajah Pada Dataset Terbatas Salsabila, Pramesya Mutia; Luthfiarta, Ardytha; Nugraha, Adhitya; Muttaqin, Almas Najiib Imam; Zarifa, Yasmine
Building of Informatics, Technology and Science (BITS) Vol 6 No 3 (2024): December 2024
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v6i3.6225

Abstract

Face recognition has become a crucial topic in image processing and computer vision, particularly in university environments. This study explores the use of GhostFaceNet and YOLOv8 models to address the challenges of face recognition with a limited dataset, consisting of only one formal photo per individual. By applying image augmentation techniques, we improved the system's accuracy to 85%. GhostFaceNet excels in generating precise and detailed face embeddings, which are essential for accurate recognition. Meanwhile, YOLOv8 demonstrates superior speed in detecting faces under various lighting conditions and angles. Comparative results reveal that YOLOv8 achieves an accuracy of 81%, outperforming SSD, which only reaches 76%. Despite challenges related to the low quality of original images, the findings highlight the significant potential of deep learning-based face recognition systems. This research aims to compare SSD and YOLOv8 detection models using GhostFaceNet and contribute to the development of more effective and reliable face recognition methods in academic settings.
DiabTrack: Sistem Prediksi Dini Diabetes Melitus Tipe 2 berbasis Web menggunakan Algoritma K-Nearest Neighbors Pangestu, Aditya Gilang; Winarno, Sri; Nugraha, Adhitya; Muttaqin, Almas Najiib Imam
Jurnal Pendidikan Informatika (EDUMATIC) Vol 9 No 1 (2025): Edumatic: Jurnal Pendidikan Informatika
Publisher : Universitas Hamzanwadi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29408/edumatic.v9i1.29691

Abstract

Type 2 diabetes mellitus is a chronic disease that is often not detected early enough, increasing the risk of serious complications. Based on this, early detection of this disease is very important to reduce its negative impact. This research aims to develop the DiabTrack system, a web-based prediction system using the K-Nearest Neighbors (KNN) algorithm. This type of research is development research using the Rapid Application Development (RAD) model, including the requirements planning, design workshop, and implementation stages. The dataset used comes from Kaggle, containing 53,000 samples and 8 features. The model is trained using the KNN algorithm and the SMOTE technique to balance the data. Evaluation results show that the KNN model achieves an accuracy of 99.17%, a recall of 100%, and an F1-score of 94%, making it the chosen algorithm for the DiabTrack website. Additionally, Black Box testing results indicate that all features in the DiabTrack system function as expected, helping the public monitor their health conditions while serving as an initial analysis tool for medical professionals.
Co-Authors Adhitya Nugraha Ardytha Luthfiarta Pangestu, Aditya Gilang Salsabila, Pramesya Mutia Sri Winarno Zarifa, Yasmine