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All Journal Jurnal Sisfokom (Sistem Informasi dan Komputer)
Toriko, Liu
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Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management

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Clustering Model for OKU Timur Script Images Toriko, Liu; Purnamasari, Susan Dian; Kunang, Yesi Novaria; Yadi, Ilman Zuhri; Andri, Andri
Jurnal Sisfokom (Sistem Informasi dan Komputer) Vol. 14 No. 1 (2025): JANUARY
Publisher : ISB Atma Luhur

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Abstract

Abstract— The OKU Timur is a regency located in South Sumatra Province. In the OKU Timur region there are many historical heritage sites, one of which is the script. In general, script is a system of symbols for writing language. The OKU Timur script is a writing system that is usually used by the local community. This writing system is characterized by its unique characters and has high historical and aesthetic value for the local community. The OKU Timur script is used in daily communication, traditional ceremonies, historical documents, and various other cultural contexts. This research aims to develop a clustering model that is used to efficiently and accurately group Of OKU Timur script images based on certain characteristics. By using techniques in the field of clustering such as the K-Means algorithm this model is developed so that the clustering of OKU Timur script images is made automatically in order to save time and effort. The study employs the K-Means algorithm to divide the data into several clusters, grouping data with similar characteristics into one cluster and data with different characteristics into another. This research is also expected to contribute to preserving digital culture so that the development of OKU Timur characters can be passed on to future generations.Keywords— OKU Timur Script, Clustering, K-Means
Clustering OKU Timur Script Images using VGG Feature extraction and K-Means Toriko, Liu; Purnamasari, Susan Dian; Kunang, Yesi Novaria; Yadi, Ilman Zuhri; Andri, Andri
Jurnal Sisfokom (Sistem Informasi dan Komputer) Vol. 14 No. 1 (2025): JANUARY
Publisher : ISB Atma Luhur

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32736/sisfokom.v14i1.2292

Abstract

This study focuses on the utilization of clustering models to group manuscript images from the OKU Timur region based on specific characteristics. OKU Timur is rich in cultural heritage, including a unique writing system known as the OKU Timur script. The development of intelligent systems technology can be employed to recognize the OKU Timur script. For this purpose, a dataset of OKU Timur script is needed, which will later be used for classifying script images. One of the challenges in preparing the dataset is grouping a large number of script image samples according to the number of characters. A proposed solution in this research is to automatically group script images by applying the K-Means algorithm. The dataset comprises 2,280 images, representing 19 characters and 228 variations with different diacritics. Features are extracted using the VGG16 model, which are then clustered with the K-Means algorithm. Clustering performance is evaluated based on the percentage of correctly grouped characters. For 19 groups (character count), the model achieves an accuracy of 82.6%. For 228 groups (variations and diacritics), it correctly groups 48.16% of characters. Despite the challenges, the results demonstrate the model’s potential for further refinement. This study’s contribution lies in introducing an efficient clustering approach for cultural manuscripts, supporting digital preservation, and advancing automatic recognition of the OKU Timur script. These efforts aim to preserve the script for future generations.
Co-Authors Andri Andri Ilman Zuhri Yadi Susan Dian Purnamasari, Susan Dian Yesi Novaria Kunang