Abstract—Nowadays, the types and products of cellular telecommunications services are very diverse, especially with the upcoming of 5G technology, which makes telecommunications service products such as voice, video, and text messages rely on data packages. Even though the digital era is rapidly growing, the Short Messaging Service (SMS) is still relevant and used as a telecommunication service despite so many sophisticated instant messaging services that rely on the internet. Smartphone users especially in Indonesia are often terrorized by spam messages with pretentious content. Moreover, the SMS came from an unknown number and contained a message or link to a fraudulent site. This study develops a Deep Learning model to predict whether a short text message (SMS) is important or spam. This research domain belongs to Natural Language Processing (NLP) for text processing. The models used are Dense Network, Long Short Term Memory (LSTM), and Bi-directional Long Short Term Memory (Bi-LSTM). Based on the evaluation of the Dense Network model, it produces a loss of 14.22% and an accuracy of 95.63%. The evaluation of the LSTM model is 19.89% loss and 94.76% accuracy. Finally, the evaluation of the Bi-LSTM model is 19.88% loss and 94.75% accuracy.

Index Terms—SMS spam, deep learning, NLP, dense network, LSTM, Bi-LSTM

Cite: Alfin Hikmaturokhman, Hanin Nafi'ah, Solichah Larasati, Ade Wahyudin, Galih Ariprawira, and Subuh Pramono, "Deep Learning Algorithm Models for Spam Identification on Cellular Short Message Service," Journal of Communications vol. 17, no. 9, pp. 769-776, September 2022. Doi: 10.12720/jcm.17.9.769-776