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All Journal JOIV : International Journal on Informatics Visualization Indonesian Journal of Electrical Engineering and Computer Science
Noor Azah Samsudin, Noor Azah
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Computer Science & IT

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Job matching analysis by latent semantic indexing enhanced on multilingual word meanings Sukri, Sukri; Samsudin, Noor Azah; Fadzrin, Ezak; Ahmad Khalid, Shamsul Kamal; Trisnawati, Liza
Indonesian Journal of Electrical Engineering and Computer Science Vol 37, No 1: January 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v37.i1.pp434-442

Abstract

Job matching is a hiring process that involves a thorough understanding of the context and meaning of words in different languages. The updated and expanded latent semantic indexing (LSI) Framework seeks to improve the precision and relevance of job matching analysis of word meanings in multi-languages. Because they only compare related terms, conventional LSIs are often insufficient to address the complexity of context in job matching. Extending the LSI approach can improve the vector representation of words and help you understand the context and semantic relationships in the text. Improved LSI analyzes context more precisely by using word vector representation. Improved LSI focuses on understanding semantic relationships between words in many languages to produce more accurate and relevant job matches. This paper describes the steps involved in improving LSI, such as data collection, pre-processing, linguistic feature extraction, LSI model training, and evaluation of matching results. The results show that the examined classification model has much better performance in terms of word classification. Conventional LSI has an average prediction value of 79%, once the enhanced LSI can accurately predict about 84% of the entire word, it has a reasonable capacity to recognize the actual words in a natural context.
An Improved Okta-Net Convolutional Neural Network Framework for Automatic Batik Image Classification Elvitaria, Luluk; Ahmad, Ezak Fadzrin; Samsudin, Noor Azah; Ahmad Khalid, Shamsul Kamal; Salamun, -; Indra, Zul
JOIV : International Journal on Informatics Visualization Vol 9, No 1 (2025)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62527/joiv.9.1.2591

Abstract

Batik is one of Indonesia's most important cultural arts and has received recognition from UNESCO. Batik has high artistic and historical value with a variety of patterns. Currently, Indonesia has 5,849 batik motifs which are generally classified based on shape, color, motif and symbolic meaning. The diversity of batik motifs makes it difficult for ordinary people to fully recognize them. This paper intends to develop an automatic framework for classifying batik motifs as a solution to overcome this issue. To develop this classification automation framework, the paper proposes a new architecture based on deep learning, which is named Okta-net. The architecture consists of 8 convolutional layers with separate convolution operations (SeparableConv2D). The output of the last convolution block will be fed to the fully connected layer using global average pooling. Meanwhile, in developing a deep learning model to classify batik image patterns, a dataset of 5 batik classes (motifs) was organized, consisting of 4,284 batik images. Through a series of experiments carried out, the proposed Okta-Net architecture succeeded in achieving satisfactory results with a validation accuracy of 93.17%, Precision of 91.60%, Recall of 92.28%, F-1 Score of 91.54%, and a loss of just 0.12%. Thus, it can be concluded that Okta-Net architecture can help preserve Indonesia's batik cultural heritage by accurate batik motif’s classification. Apart from that, based on a comparison of research outcomes, Okta-Net outperformed most of earlier studies, the majority of which had an accuracy of below 90%.
Co-Authors - Salamun Ahmad Khalid, Shamsul Kamal Ahmad, Ezak Fadzrin Fadzrin, Ezak Indra, Zul Liza Trisnawati Luluk Elvitaria, Luluk sukri Sukri, sukri