Zainal K.Sulaiman N.F.Jali M.Z.2024-05-272024-05-272015--(IJCSIS) International Journal of Computer Science and Information Security,Vol. 13, No. 3, March 20151947-5500429-13https://sites.google.com/site/ijcsis/all-volumes-issues/vol-13-no-3-mar-2015https://oarep.usim.edu.my/handle/123456789/4167This paper reported and summarized findings of spam management for Short Message Service (SMS) which consists of classification and clustering of spam using two different tools, namely RapidMiner and Weka. By using the same dataset, which is downloaded from UCI, Machine Learning Repository, various algorithms used in classification and clustering in this simulation has been analysed comparatively. From the simulation, both tools giving the similar results that the same classifiers are the best for SMS spam classification and clustering which are outperformed than other algorithms. . Keywords- SMS spam; RapidMiner; Weka; Naïve Bayesian (NB); Support Vector Machine (SVM); k-Nearest Neighbour (kNN); K-Mean; Cobweb; Hierarchical clustering; spam classification; spam clustering. .enSMS spam;RapidMiner;Weka;Naïve Bayesian (NB);Support Vector Machine (SVM);k-Nearest Neighbour (kNN);K-Mean; Cobweb;Hierarchical clustering;spam classification;spam clustering. .Article6674133