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All Journal (JELIKU) Jurnal Elektronik Ilmu Komputer Udayana Sinkron : Jurnal dan Penelitian Teknik Informatika
Negara, I Made Wahyu Guna
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Computer Science & IT

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Basic Word Extraction Algorithm Based on Morphological Rules for Balinese Texts Negara, I Made Wahyu Guna; Sanjaya ER, Ngurah Agus
JELIKU (Jurnal Elektronik Ilmu Komputer Udayana) Vol 8 No 4 (2020): JELIKU Volume 8 No 4, Mei 2020
Publisher : Informatics Department, Faculty of Mathematics and Natural Sciences, Udayana University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24843/JLK.2020.v08.i04.p06

Abstract

Stemming is the process of extracting the root word of an affixed word. The process is intended to reduce the variations in the word. In this research, we are interested in applying stemming on Balinese language. Previous works on stemming of the Balinese language applied rule-based method but only prefix and suffix were considered. Moreover, the rules were constructed without providing much attention to the morphology of the Balinese language. Rule-based method can be verified and validated with ease on simple problem but fail to do so on problems with high complexity such as Balinese language. To overcome the weaknesses of rule-based stemming on Balinese language, we propose a method that reduce all variations of affix on Balinese language by combining the rule- based approach and the Balinese language morphology. Based on experiments carried out, our proposed method obtained an average stemming accuracy of 99% which is better than 96.67% achieved by the previous method. Keywords: Stemming, Balinese language, Rule-based
Enhancing EEG-Based Stress Detection Using ICA, Relative Difference, and Convolutional Neural Networks Negara, I Made Wahyu Guna; Wirawan, I Made Agus; Sunarya, I Made Gede
Sinkron : jurnal dan penelitian teknik informatika Vol. 9 No. 3 (2025): Article Research July 2025
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/sinkron.v9i3.14777

Abstract

: EEG-based stress detection is crucial for early mental health monitoring, but signal quality is often degraded by artifacts and baseline variability. This study proposes an optimized preprocessing method combining Independent Component Analysis (ICA) for artifact removal and Relative Difference for baseline reduction. Using the SAM-40 EEG dataset, features were extracted with Differential Entropy and structured into a 3D EEG cube to preserve spatial-frequency information. A Convolutional Neural Network (CNN) classified stress levels into low and high categories. The proposed approach achieved 94.44% accuracy, with 100% precision for the high stress class and 81.82% recall. These results highlight the effectiveness of combining ICA and baseline reduction to enhance deep learning-based EEG signal processing for stress detection.
Co-Authors I Made Gede Sunarya M.Cs S.Kom I Made Agus Wirawan . Ngurah Agus Sanjaya ER