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All Journal Bulletin of Electrical Engineering and Informatics Sinkron : Jurnal dan Penelitian Teknik Informatika
Kwok, Shane Christian
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Computer Science & IT Electrical & Electronics Engineering

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Fingerprint Identification for Attendance Using Euclidean Distance and Manhattan Distance Putra, Adya Zizwan; Yek, Sallyana; Kwok, Shane Christian; Tarigan, Elovani; Sego, William Frans
Sinkron : jurnal dan penelitian teknik informatika Vol. 7 No. 4 (2023): Article Research Volume 7 Issue 4, October 2023
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/sinkron.v8i4.12844

Abstract

Attendance is an action to confirm that someone is present at the office, school, or event. The use of attendance in an agency or company is really important as it can improve the level of discipline and productivity. However, the traditional way of doing attendance is considered less effective, less secure, and more difficult to organize. Therefore, a modern attendance system that utilizes fingerprints can be the right solution, especially because every fingerprint is unique. In this research, we focus on designing a fingerprint identification system for attendance purposes by using two distance measure methods, namely Euclidean Distance and Manhattan Distance. The dataset used in the research contains 111 fingerprint images with 90 images for training the designed fingerprint identification system and the remaining 21 images for testing the system. Each fingerprint image has undergone image pre-processing stage before being used. We compare Euclidean Distance and Manhattan Distance based on their performances in identifying fingerprint. From the test results, the fingerprint identification accuracy obtained using Euclidean Distance is 76.19%, while the accuracy obtained using Manhattan Distance is 71.43%. In general, both algorithms succeed in providing the correct identification results. This proves that Euclidean Distance and Manhattan Distance can be utilized for fingerprint identification purposes.
Skin cancer classification using EfficientNet architecture Harahap, Mawaddah; Husein, Amir Mahmud; Kwok, Shane Christian; Wizley, Vincent; Leonardi, Jocelyn; Ong, Derrick Kenji; Ginting, Deskianta; Silitonga, Benny Art
Bulletin of Electrical Engineering and Informatics Vol 13, No 4: August 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v13i4.7159

Abstract

Skin cancer is one of the most common deadly diseases worldwide. Hence, skin cancer classification is becoming increasingly important because treatment in the early stages of skin cancer is much more effective and efficient. This study focuses on the classification of three common types of skin cancer, namely basal cell carcinoma (BCC), squamous cell carcinoma (SCC), and melanoma using EfficientNet architecture. The dataset is preprocessed and each image in the dataset is resized to 256×256 pixels prior to incorporation in later stages. We then train all types of EfficientNet starting from EfficientNet-B0 to EfficientNet-B7 and compare their performances. Based on the test results, all trained EfficientNet models are capable of producing good accuracy, precision, recall, and F1-score in skin cancer classification. Particularly, our designed EfficientNet-B4 model achieves 79.69% accuracy, 81.67% precision, 76.56% recall, and 79.03% F1-score as the highest among others. These results confirm that EfficientNet architecture can be utilized to classify skin cancer properly.
Co-Authors Amir Mahmud Husein, Amir Mahmud Ginting, Deskianta Leonardi, Jocelyn Mawaddah Harahap, Mawaddah Ong, Derrick Kenji Putra, Adya Zizwan Sego, William Frans Silitonga, Benny Art Tarigan, Elovani Wizley, Vincent Yek, Sallyana