| dc.description.abstract |
The use of Short Message Service (SMS) media for communication is still very much. This is due to several factors, such as low rates, bonuses provided and ease of use. However, these factors make SMS services used to commit criminal acts, one of which is SMS fraud. To overcome this, we need a system that can classify SMS as spam or not spam (ham). In this study, the SMS dataset used is the Indonesian language SMS dataset. For text weighting, use the TF-IDF method and Cosine Similarity for the distance calculation method. The results of this study are an application that is able to classify Indonesian SMS Spam using the K-Nearest Neighbor Algorithm.
Keyword: SMS Spam, TF-IDF, K-Nearest Neighbor, Cosine Similarity
| en_US |
