CLASSIFICATION OF SMS SPAM WITH N-GRAM AND PEARSON CORRELATION BASED USING MACHINE LEARNING TECHNIQUES

Nova Tri Romadloni; Nisa Dwi Septiyanti; Cucut Hariz Pratomo; Wakhid Kurniawan; Rauhulloh Ayatulloh Khomeini Noor Bintang

doi:10.55681/sentri.v3i2.2252

Authors

Nova Tri Romadloni Universitas Muhammadiyah Karanganyar
Nisa Dwi Septiyanti Universitas Muhammadiyah Karanganyar
Cucut Hariz Pratomo Universitas Muhammadiyah Karanganyar
Wakhid Kurniawan Universitas Muhammadiyah Karanganyar
Rauhulloh Ayatulloh Khomeini Noor Bintang Universitas Muhammadiyah Karanganyar

DOI:

https://doi.org/10.55681/sentri.v3i2.2252

Keywords:

Feature Selection, Machine Learning, Ngram, Pearson Correlation, SMS Classification

Abstract

The Short Message Service (SMS) has garnered widespread popularity due to its simplicity, reliability, and ubiquitous accessibility.This study aims to enhance the efficacy of SMS classification by refining the classification process itself. Specifically, it strives to streamline the process by diminishing feature dimensions and eliminating inconsequential attributes. The textual data undergoes preprocessing, which involves employing the N-Gram technique for feature representation, followed by meticulous feature selection utilizing Pearson Correlation. The study employs 5 of classification algorithms. Notably, the findings underscore that the optimal outcomes emerge from the fusion of the N-Gram methodology with feature selection through Pearson Correlation. Among these, the Support Vector Machine methodology stands out, exhibiting a remarkable 91.41% enhancement in accuracy without feature selection, a further improvement to 91.96% through N-Gram utilization, and a final performance of 70.80% following the inclusion of weighted correlation. However, it is imperative to acknowledge the limitations inherent in the model's generalizability, primarily stemming from the utilization of a relatively modest dataset. Despite the efficacy of Pearson correlation and N-gram-based feature selection in curbing data dimensionality and enhancing processing efficiency, certain pertinent features may have been overlooked, or the chosen attributes might not be optimally suited for specific classifications.

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