Implementasi Algoritma Support Vector Classifier (SVC) dengan Data Training Numerik dan Teks untuk Mengklasifikasi SMS Spam
Abstract
Short Message Service (SMS) is a service that resembles correspondence found on mobile phones. The reason why SMS is massively used is because of its low cost and instant. However, with the advancement of this technology, SMS is often misused by many people. Often people send messages that are meaningless. This message called “spam”. Many people deal with spam messages by blocking the sender of the message. However, this method is less effective. So the solution to the problem solving for spam messages is to classify messages that are categorized as spam and not spam (ham). In this research we use Support Vector Classifier (SVC) algorithm to classified spam, SMS spam was classified in two ways, one with training data in the form of numeric and the other with training data in the form of text. This research conclude that the classification of spam messages will have the highest accuracy if the training data is in the form of text rather than in the form of numeric.
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