Android-Based Short Message Service Filtering using Long Short-Term Memory Classification Model

M. Laylul Mustagfirin(1*), Giri Wahyu Wiriasto(2), I Made Budi Suksmadana(3), Indira Puteri Kinasih(4),

(1) Universitas Mataram
(2) Universitas Mataram
(3) Universitas Mataram
(4) Universitas Negeri Mataram
(*) Corresponding Author
DOI: https://doi.org/10.23917/khif.v8i2.17995

Abstract

Short Message Service (SMS) is a technology for sending messages in text format between two mobile phones that support such a facility. Despite the emergence of many mobile text messaging applications, SMS still finds its use in communication among people and broadcasting messages by governments and mobile providers. SMS users often receive messages from parties, particularly for marketing and business purposes, advertisements, or elements of fraud. Many of those messages are irrelevant and fraudulent spam. This research aims at developing android-based applications that enable the filtering of SMS in Bahasa Indonesia. We investigate 1469 SMS text data and classify them into three categories: Normal, Fraudulent, and Advertisement. The classification or filtering method is the long short-term memory (LSTM) model from TensorFlow. The LSTM model is suitable because it has cell states in the architecture that are useful for storing previous information. The feature is applicable for use on sequential data such as SMS texts because every word in the texts constructs a sequential form to complete a sentence. The observation results show that the classification accuracy level is 95%. This model is then integrated into an Android-based mobile application to execute a real-time classification.

Keywords

text filtering; recurrent neural network; long short term memory

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