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All Journal Jurnal Cakrawala Informasi (JCI) Jurnal Pengabdian Kepada Masyarakat Alkhidmah: Jurnal Pengabdian dan Kemitraan Masyarakat
Kristiawan Nurdianto
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Agriculture, Biological Sciences & Forestry Humanities Computer Science & IT Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Health Professions Industrial & Manufacturing Engineering Law, Crime, Criminology & Criminal Justice Library & Information Science Social Sciences Other

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OPTIMASI PENGENALAN AKSARA JAWA MENGGUNAKAN COSINE SIMILARITY, K-NEAREST NEIGHBOR DAN SUPPORT VECTOR MACHINE Priyambodo, Aji; Martius Apun Heses; Kristiawan Nurdianto
Jurnal Cakrawala Informasi Vol 5 No 1 (2025): Juni : Jurnal Cakrawala Informasi
Publisher : Lembaga Penelitian dan Pengabdian Masyarakat (LPPM) - Institut Teknologi dan Bisnis Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.54066/jci.v5i1.597

Abstract

This study investigates the use of cosine similarity as a distance metric to enhance Javanese script (Hanacaraka) recognition. A dataset of 4,385 images representing 20 classes was processed using Histogram of Oriented Gradients (HOG) for feature extraction and classified using K-Nearest Neighbor (KNN) and Support Vector Machine (SVM) optimized via GridSearchCV. The SVM with cosine similarity achieved an accuracy of 99.84%, outperforming traditional metrics like Euclidean and Minkowski distances, as well as prior methods such as CNN-SVM and LBP-SVM. Cosine similarity's emphasis on angular relationships, rather than magnitude, made it effective for handling high-dimensional and intricate patterns. ANOVA analysis confirmed significant differences among distance metrics, validating cosine similarity’s advantages. This research supports cultural preservation and advances pattern recognition technologies. Future studies should explore integrating cosine similarity with deep learning models like CNNs or Transformers to improve scalability and adapt to broader datasets, including other traditional scripts.
Co-Authors Aji Priyambodo Andreas Tigor Oktaga, Andreas Ika Susanti Marsiska Ariesta Putri Martinus Apun Heses Martius Apun Heses Prihati Prihati Prihati Rutna Anggaraeni