Application of Nave Bayes Algorithm for SMS Spam Classification Using Orange - Nosiel Nosiel; Sigit Andriyanto; Muhammad Said Hasibuan (Utan Kayu Publishins, 2022)
Mobile phones have become a necessity for everyone. SMS is a communication service that is used to send and receive short messages in the form of text on mobile phones. Among all the advantages of SMS, there is a very annoying activity called spam (unsolicited commercial advertisements). Spam is the continuous use of electronic devices to send messages. called spammers. Spam messages are sent by advertisers with the lowest operating costs. Therefore, there are a lot of spammers and the number of messages requested is huge. Therefore, many aspects are harmed and disturbed. When SMS enters the user's mobile device, this study aims to classify spam and ham SMS. SMS classification adopts naive Bayes method. By looking at the contents of the SMS, the application of the naive Bayes method in data mining can distinguish unwanted SMS from non-spam. Results The classification accuracy rate is 0.999%. Based on the research that I have done, the Naive Bayes method can classify 1000 SMS spam data contained in the SMS spam data set file correctly.