COMPARISON OF ACCURACY OF VARIOUS TEXT CLASSIFICATION METHODS IN SENTIMENT ANALYSIS OF E-STAMPS AT X

Dimas Bagus Reynaldi; Ryan Randy Suryono

doi:10.52436/1.jutif.2025.6.1.3999

Dimas Bagus Reynaldi Universitas Teknokrat Indonesia
Ryan Randy Suryono Universitas Teknokrat Indonesia

DOI: https://doi.org/10.52436/1.jutif.2025.6.1.3999

Keywords: e-meterai, Analisis Sentimen, Modeling

Abstract

In the rapidly evolving digital era, technological innovations are applied in various fields, including law and administration, to improve the efficiency and effectiveness of processes. One of the latest innovations in Indonesia is the implementation of e-metals, which is designed to facilitate legal and secure electronic transactions, and meet the needs of a society that is increasingly dependent on digital technology. Although e-stamps aim to improve efficiency and security in transactions, there are still various perceptions from the public that reflect their views and experiences regarding the implementation of this technology. In this case, sentiment analysis is an effective method to evaluate public opinion generated from text data, such as user reviews and comments on social media. This research aims to analyze the sentiment towards e-metallocations in X app, using text classification methods to separate positive and negative sentiments. After collecting 3282 datasets and performing preprocessing that includes case folding, data cleaning, tokenizing, and stemming, the evaluation results show that the Naive Bayes (GNB) model achieves 96.65% accuracy on training data and 95.28% on testing data. On the other hand, the Support Vector Machine (SVM) model recorded an accuracy of 98.32% on training data and 96.80% on testing data. Meanwhile, the Random Forest model showed a perfect accuracy of 100% on training data and 99.09% on testing data, making it the highest performing model among the three methods tested.

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Author Biography

Ryan Randy Suryono, Universitas Teknokrat Indonesia

Ryan Randy Suryono is a lecturer and Head of the Research Department at the Universitas Teknokrat Indonesia. He graduated with a bachelor's degree in Informatics Engineering at STMIK Teknokrat Indonesia, a master's degree in Computer Science at the Institut Teknologi Sepuluh Nopember, and a doctoral degree in Computer Science at the Universitas Indonesia. His research interests include Information Systems, Financial Technology, Metaverse and Social Media Analytics. He can be contacted via email: ryan@teknokrat.ac.id

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