| dc.description.abstract | Phishing is a type of social media crime that steals personal data from individuals or
industries. Phishing can be delivered in various forms, one of which is through text
messages, known as smishing. Smishing contains text messages that include email
addresses, phone numbers, or website links that attract the recipient without realizing
the crime. Such crimes can harm the recipient financially or compromise data security.
However, identifying smishing is still challenging because these messages must be
identified manually, a process that takes a long time. Therefore, an approach is
needed to detect phishing in text messages quickly and more accurately. This study
aims to identify phishing in text messages using the Support Vector Machine (SVM)
algorithm with Ensembled Bagging. The methodology includes collecting 1600
phishing and non-phishing texts obtained from previous research and the researcher's
message box, then preprocessing the data through cleaning, tokenization, stopwords
elimination, and stemming. The data is then divided into training and testing sets. The
SVM algorithm is used as the base model and optimized with Ensembled Bagging to
improve identification accuracy. The results show that the proposed approach
successfully identifies phishing with an accuracy of 95.2%, proving that the
combination of SVM and Ensembled Bagging is effective in processing text message
data for phishing identification. | en_US |
