Article Per Year (5 Year)
p-Index From 2021 - 2026
0.702
P-Index
This Author published in this journals
All Journal Informatics and Computer Engineering Journal Internet of Things and Artificial Intelligence Journal Proceedings of The International Conference on Computer Science, Engineering, Social Sciences, and Multidisciplinary Studies
Siregar, Kalfida Eka Wati
Unknown Affiliation
Religion Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Education Electrical & Electronics Engineering Engineering

Published : 4 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 1 Documents
Search

 1 
Implementation of the Naive Bayes Algorithm in Spam Detection in SMS Messages Fadil, Ulfi Muzayyanah; Siregar, Kalfida Eka Wati; Ramadani, Wily Supi
Proceedings of The International Conference on Computer Science, Engineering, Social Science, and Multi-Disciplinary Studies Vol. 1 (2025)
Publisher : CV Raskha Media Group

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.64803/cessmuds.v1.26

Abstract

This study discusses the application of the Naive Bayes algorithm to detect spam messages in Short Message Service (SMS) services. The background of this study is the increasing spread of spam messages containing advertisements, fraud, and malicious content, which necessitates an automated system to distinguish spam from non-spam. The methods used in this study include collecting labeled SMS data, preprocessing (text cleaning, tokenization, stopword removal, and stemming), and feature extraction using the Term Frequency-Inverse Document Frequency (TF-IDF) technique. The Naive Bayes model was trained on a Kaggle dataset and tested in Google Colab to evaluate classification performance using accuracy, precision, and recall metrics. The results showed that the Multinomial Naive Bayes model achieved an accuracy of 96.86%, with a strong ability to recognize ham (non-spam) messages and exemplary performance in detecting spam messages. These findings demonstrate that the Naive Bayes algorithm is effective and efficient at classifying Indonesian-language text messages, making it a suitable basis for developing a more innovative, faster automatic SMS spam detection system.
Co-Authors Armansyah Armansyah Fadil, Ulfi Muzayyanah Mhd Furqan Ramadani, Wily Supi Sitepu, Anggi Jelita