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All Journal JURNAL MAHASISWA BK AN-NUR : BERBEDA, BERMAKNA, MULIA Nation State : Journal of International Studies METIK JURNAL YASIN: Jurnal Pendidikan dan Sosial Budaya
Jasmine, Putri
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Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Earth & Planetary Sciences Education Electrical & Electronics Engineering Social Sciences Other

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Journal : METIK JURNAL

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Deteksi Tipe Sidik Jari Untuk Mengenali Kepribadian Menggunakan Metode Support Vector Machine Jasmine, Putri; Pratiwi, Nunik
METIK JURNAL (AKREDITASI SINTA 3) Vol. 9 No. 2 (2025): METIK Jurnal
Publisher : LPPM Universitas Mulia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47002/x7msax09

Abstract

This study discusses the development of a fingerprint type classification system based on digital image processing using the Support Vector Machine (SVM) method. The system is designed to recognize three main fingerprint patterns: arch, loop, and whorl. The data processing stages include binarization of the fingerprint image and feature extraction using the Histogram of Oriented Gradients (HOG) method. Once the features are extracted, classification is performed using the SVM algorithm with a Radial Basis Function (RBF) kernel to improve separation performance between classes. The dataset used in this study was obtained from the Kaggle platform, and the system was implemented using MATLAB software, complete with a graphical user interface (GUI) to facilitate user interaction. The system’s performance was evaluated by dividing the dataset into 80% training data and 20% testing data. The results show that the model is capable of classifying fingerprint patterns with an accuracy of 89.25%. These findings indicate that the SVM method is effective and can serve as an initial solution for automatic fingerprint-based identification systems.  
Co-Authors Adzani, Alifah Bagasta, Galang Dewi, Chandra Faturrahman, M. Padhil Nurmala, Cindy Nurullah, Titi Latifah Pratiwi, Nunik Putri, Alvela Rahmi, Iftita Salma, Riana Syawalia, Shalsa