Article Per Year (5 Year)
p-Index From 2021 - 2026
0.408
P-Index
This Author published in this journals
All Journal EDUMATIC: Jurnal Pendidikan Informatika Infotek : Jurnal Informatika dan Teknologi
Suci Putri Widyani
Politeknik Negeri Sriwijaya
Computer Science & IT Control & Systems Engineering Education Engineering

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

Found 2 Documents
Search

 1 
Evaluasi Kinerja Algoritma K-Means dan K-Medoids untuk Klasterisasi Destinasi Wisata Bali Aryanti; Dian Anjani; Muhammad Gian Azzra Ramadhan; Suci Putri Widyani; Adinda Kamilasari
Infotek: Jurnal Informatika dan Teknologi Vol. 9 No. 2 (2026): Infotek : Jurnal Informatika dan Teknologi
Publisher : Fakultas Teknik Universitas Hamzanwadi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29408/jit.v9i2.34958

Abstract

This study aims to compare the performance of the K-Means and K-Medoids algorithms in clustering tourist destinations in Bali Province based on multidimensional characteristics, including rating, number of reviews, latitude, longitude, category, and district/city. The dataset used comes from Kaggle with 1,000 tourist destination data that have gone through a preprocessing process in the form of data cleaning, feature selection, normalization, and logarithmic transformation. The optimal number of clusters is determined using the Elbow Method in the range of K = 2 to K = 10, while the quality of the clusters is evaluated using the Silhouette Score, Davies-Bouldin Index (DBI), and Mean Squared Error (MSE). The results show that the optimal number of clusters is K = 10. K-Means produces a Silhouette Score of 0.2078, a DBI of 1.2714, and an MSE of 2.2764, thus showing better performance than K-Medoids. This study contributes in the form of a comparative evaluation of the two algorithms and recommendations for clustering methods that are more suitable for Bali tourist destination data. The research results can also support decision making in the management and development of the tourism sector.
Real-Time Student Attendance Recognition Using a Centroid Based MTCNN–ArcFace Framework Suci Putri Widyani; Suroso Suroso; Ahmad Taqwa
Jurnal Pendidikan Informatika (EDUMATIC) Vol 10 No 2 (2026): Edumatic: Jurnal Pendidikan Informatika (IN PRESS)
Publisher : Universitas Hamzanwadi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29408/edumatic.v10i2.35448

Abstract

Student attendance remains susceptible to proxy attendance and operational inefficiencies, while many face recognition systems are evaluated only on benchmark datasets and rarely investigate identity representation strategies under real-world educational conditions. This study evaluates a hybrid MTCNN–ArcFace framework incorporating centroid-based identity representation, application-specific threshold calibration, and real-time validation for automated student attendance. A quantitative experimental design was conducted using a locally collected dataset from a secondary school. MTCNN was employed for face detection and alignment, whereas ArcFace with a ResNet-50 backbone generated facial embeddings that were aggregated into centroid templates for identity matching. The framework was assessed through offline performance evaluation and operational deployment. The proposed approach achieved 92.86% accuracy, 97.22% precision, 92.86% recall, and a 93.49% F1-score, with a 4.76% false acceptance rate and 2.38% false rejection rate. In addition, centroid representation reduced template storage requirements and supported efficient real-time recognition using limited enrollment samples. These findings demonstrate that centroid-based identity representation enhances the practicality of deep face recognition for educational attendance systems by improving computational efficiency while maintaining reliable recognition performance in authentic school environments.
Co-Authors Adinda Kamilasari Ahmad Taqwa Aryanti Dian Anjani Muhammad Gian Azzra Ramadhan Suroso Suroso