Spam Message Classification Using the Naïve Bayes Algorithm Based on RapidMiner

Muhamad Yusup; Mochamad Isham Fadillah; Rifky Adinanta Fauzanie; Risca Lusiana Pratiwi; Rani Irma Handayani; Euis Widanengsih

doi:10.59934/jaiea.v5i2.1811

Authors

Muhamad Yusup Universitas Bina Sarana Informatika
Mochamad Isham Fadillah Universitas Bina Sarana Informatika
Rifky Adinanta Fauzanie Universitas Bina Sarana Informatika
Risca Lusiana Pratiwi Universitas Bina Sarana Informatika
Rani Irma Handayani Universitas Bina Sarana Informatika
Euis Widanengsih Universitas Bina Sarana Informatika

DOI:

https://doi.org/10.59934/jaiea.v5i2.1811

Keywords:

Naïve Bayes, RapidMiner, Spam Classification, Text Mining, Machine Learning, Natural Language Processing

Abstract

This study implements the Naïve Bayes algorithm for classifying spam and non-spam (ham) messages using the RapidMiner Studio platform. The dataset used was obtained from the SMS Spam Collection Dataset on the Kaggle platform, which consists of 5,759 messages with a distribution of 4,075 ham messages and 1,291 spam messages. The research stages included text pre-processing, model training, and performance evaluation using accuracy, precision, recall, and F1-score metrics. The experimental results showed that the Naïve Bayes model achieved an accuracy of 89.64% with a precision of 56.93%, a recall of 100%, and an F1-score of 72.56%. The research findings indicate that the Naïve Bayes algorithm is effective in detecting spam messages with adequate accuracy, and prove that RapidMiner is an efficient tool for implementing machine learning methods in text classification.

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