A text classification program based on Weka that is able to identify spam messages.

The program uses a Naive Bayes learning algorithm by Weka. Using supervised training, the SpamAssassin will learn which messages are spam and which ones are not. The algorithm would analyze these messages word by word, weighing each of them with a probability value. The value would later be used to determine whether the analyzed message is a spam.

The data set for training is stored in data/ with arff format. Currently, only one training set is included in the folder – and it is for Indonesian spam messages. However, the Spam Assassin could classify messages from other languages too, given the appropriate training set. The data set should be in arff format with the same attributes as data/sms.arff.

There are two attributes in the arff file, verdict and content. Verdict contains information whether the content is PositiveSpam or NegativeSpam. While the content is the message to be evaluated. In training phase, the Spam Assassin would learn through these two attributes. Individual values will be assigned to each words in the contents; each corresponding to its verdict.

In evaluation phase, the Spam Assassin would use the previously-gained information in training phase. It would then give its own verdict towards the processed message.

Clone the bin folder, and then run RunWekaSpamAssassin.bat. It should open a new terminal window with a shell interface. Type 'h' or 'help' for further instructions. Further explanation about the program and its commands could be found in doc/Bagian_B.pdf.

A pre-existing model called zero is saved in model/. It has been trained with data set data/sms.arff, which is in Indonesian. For a quick start, do the following:

• Clone the repo
• Run RunWekaSpamAssassin.bat
• Type initialize zero
• Type evaluate followed with an Indonesian message.

• help – show details about available commands.
• initialize – specify which model to use. A warning will be shown if the specified model does not exist.
• train – select which data set to use for training and start training process. The model would be saved for future uses.
• evaluate – determine the category of the message that follows the keyword.
• quit – self explanatory.