Using the three classifiers, we obtained the best result using Random Forest with 80.29% accuracy and 74.12% for F-measure as can be seen in Table  2. The imbalanced dataset made most of the **data** to be classified into the majority class, which is the   In order to handle the imbalance **data** problem, we also try to apply two resampling techniques, Synthetic Minority Over-sampling Technique (SMOTE) for increasing the minority classes and Random Under-sampling (RUS) for decreasing the majority classes in training data. By applying the two resampling techniques, we manage to increase the ability of the classifiers in detecting deception. However, it also decreases the ability in detecting truth as well. This causes the F-measure score for each classifier to decrease as can be seen in Table  3.