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All Journal MATRIK : Jurnal Manajemen, Teknik Informatika, dan Rekayasa Komputer BIOS: Jurnal Informatika dan Sains
Christina Purnama Yanti
Institut Bisnis dan Teknologi Indonesia, Denpasar, Indonesia
Computer Science & IT

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Evaluation Analysis of the Necessity of Stemming and Lemmatization in Text Classification Ni Wayan Sumartini Saraswati; Christina Purnama Yanti; I Dewa Made Krishna Muku; Dewa Ayu Putu Rasmika Dewi
MATRIK : Jurnal Manajemen, Teknik Informatika dan Rekayasa Komputer Vol. 24 No. 2 (2025)
Publisher : Universitas Bumigora

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30812/matrik.v24i2.4833

Abstract

Stemming and lemmatization are text preprocessing methods that aim to convert words into their root and to the canonical or dictionary form. Some previous studies state that using stemming and lemmatization worsens the performance of text classification models. However, some other studies report the positive impact of using stemming and lemmatization in supporting the performance of text classification models. This study aims to analyze the impact of stemming and lemmatization in text classification work using the support vector machine method, in this case, devoted to English text datasets and Indonesian text datasets, and analyze when this method should be used. The analysis of the experimental results shows that the use of stemming will generally degrade the performance of the text classification model, especially on large and unbalanced datasets. The research process consisted of several stages: text preprocessing using stemming and lemmatization, feature extraction with Term Frequency-Inverse Document Frequency (TF-IDF), classification using SVM, and model evaluation with 4 experiment scenarios. Stemming performed the best computation time, completing in 4 hours, 51 minutes, and 41.3 seconds on the largest dataset. While lemmatization positively impacts classification performance on small datasets, achieving 91.075% accuracy results in the worst computation time, especially for large datasets, which take 5 hours, 10 minutes, and 25.2 seconds. The Experimental results also show that stemming from the Indonesian balanced dataset yields a better text classification model performance, reaching 82.080% accuracy.
Building the Virtual World: A Literature Review on the Integration of Metaverse and Blockchain Technology I Gede Iwan Sudipa; Putu Wirayudi Aditama; Christina Purnama Yanti
BIOS: Jurnal Informatika dan Sains Vol. 2 No. 01 (2024): BIOS: Jurnal Informatika dan Sains, April 2024
Publisher : Sean Institute

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Abstract

The present study explores the incorporation of blockchain technology within the metaverse, uncovering the potential for mutually beneficial outcomes that may revolutionise virtual environments, transaction security, and digital interaction. This study examines the recent advancements, obstacles, and uses of integrating blockchain and metaverse technologies through a literature review. It emphasises the critical role that AR/VR technology plays in the creation of immersive experiences. The findings indicate that the integration of blockchain technology and the metaverse presents a viable avenue towards enhanced virtual experiences that are interactive, decentralised, and secure. This has far-reaching implications across various sectors, including education, entertainment, culture, healthcare, and more. This study highlights the significance of additional progress and interdisciplinary cooperation in order to fully exploit the capabilities of this integrated digital ecosystem.
Co-Authors Dewa Ayu Putu Rasmika Dewi I Dewa Made Krishna Muku I Gede Iwan Sudipa Putu Wirayudi Aditama Saraswati, Ni Wayan Sumartini