TY - JOUR
T1 - Detecting Online Gambling Promotions on Indonesian Twitter Using Text Mining Algorithm
AU - Perdana, Reza Bayu
AU - Ardin,
AU - Budi, Indra
AU - Santoso, Aris Budi
AU - Ramadiah, Amanah
AU - Putra, Prabu Kresna
N1 - Publisher Copyright:
© (2024), (Science and Information Organization). All rights reserved.
PY - 2024
Y1 - 2024
N2 - This study addresses the pressing challenge of detecting online gambling promotions on Indonesian Twitter using text mining algorithms for text classification and analytics. Amid limited research on this subject, especially in the Indonesian context, we aim to identify common textual features used in gambling promotions and determine the most effective classification models. By analyzing a dataset of 6038 tweets collected and using methods such as Random Forest, Logistic Regression, and Convolutional Neural Networks, complemented by a comparison analysis of text representation methods, we identified frequently occurring words such as ‘link’, ‘situs’, ‘prediksi’, ‘jackpot’, ‘maxwin’, and ‘togel’. The results indicate that the combination of TF-IDF and Random Forest is the most effective method for detecting online gambling promotion content on Indonesian Twitter, achieving a recall value of 0.958 and a precision value of 0.966. These findings can contribute to cybersecurity and support law enforcement in mitigating the negative effects of such promotions, particularly on the Twitter platform in Indonesia.
AB - This study addresses the pressing challenge of detecting online gambling promotions on Indonesian Twitter using text mining algorithms for text classification and analytics. Amid limited research on this subject, especially in the Indonesian context, we aim to identify common textual features used in gambling promotions and determine the most effective classification models. By analyzing a dataset of 6038 tweets collected and using methods such as Random Forest, Logistic Regression, and Convolutional Neural Networks, complemented by a comparison analysis of text representation methods, we identified frequently occurring words such as ‘link’, ‘situs’, ‘prediksi’, ‘jackpot’, ‘maxwin’, and ‘togel’. The results indicate that the combination of TF-IDF and Random Forest is the most effective method for detecting online gambling promotion content on Indonesian Twitter, achieving a recall value of 0.958 and a precision value of 0.966. These findings can contribute to cybersecurity and support law enforcement in mitigating the negative effects of such promotions, particularly on the Twitter platform in Indonesia.
KW - analytics
KW - intention classification
KW - online gambling
KW - Social media
UR - http://www.scopus.com/inward/record.url?scp=85202967906&partnerID=8YFLogxK
U2 - 10.14569/IJACSA.2024.0150893
DO - 10.14569/IJACSA.2024.0150893
M3 - Article
AN - SCOPUS:85202967906
SN - 2158-107X
VL - 15
SP - 942
EP - 949
JO - International Journal of Advanced Computer Science and Applications
JF - International Journal of Advanced Computer Science and Applications
IS - 8
ER -