IMPLEMENTASI METODE RANDOM FOREST PADA TEXT MINING UNTUK KLASIFIKASI SMS SPAM MENGGUNAKAN PYTHON

Idir Fitriyanto, Teuku Radillah, Leonard Tambunan, Atika Fauziyyah

Abstract


The use of the Random Forest method in the field of text mining has become the focus of research in efforts to combat Short Message Service (SMS) spam. This research proposes a Random Forest approach to SMS spam classification using the Python programming language. For the training process, datasets that have been labelled for SMS types, namely 0 = normal SMS, 1 = fraudulent SMS (spam), 2 = promo SMS, are used as training and test datasets to measure the performance of the Random Forest method. The first step to identify SMS spam is to collect SMS data from different sources to build a representative dataset. The Random Forest model was implemented using the scikit-learn library in the Python environment. The model training process involves dividing the data into a training set and a validation set to measure the performance of the method. Various evaluation metrics such as accuracy, precision, recall and F1 score are used to evaluate the performance of the method. The research results show that the Random Forest method is able to provide a good classification in distinguishing between spam SMS, normal SMS and promo SMS with an accuracy rate of 89%, with precision calculation results = 0.97%, recall = 0.83% and F1 score = 0.89%. The implementation of the random forest method shows a satisfactory level of accuracy and is able to identify most spam SMS with a low error rate.


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References


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DOI: http://dx.doi.org/10.36723/juri.v17i1.742

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