Deep Learning Model for Identification of Indonesian National Figure Entities on Social Media Using LSTM Architecture

In the era of rapid digital communication, social media has become a dominant medium for information exchange and public discourse, particularly in Indonesia. Despite this growth, automatic identification of national figures within social media texts remains a significant challenge due to the informal nature of language, frequent abbreviations, and inconsistent spelling patterns. Addressing this gap, this study aims to develop a Deep Learning model based on Long Short-Term Memory (LSTM) networks to identify Indonesian national figures from social media texts. The research utilizes 1,109 tweets collected from X (formerly Twitter) through the X API, encompassing names of well-known figures from politics, sports, entertainment, and social activism. The research process includes dataset crawling, preprocessing, labeling using the spaCy library, dividing training and test data, and training an LSTM model. The evaluation results show that the proposed model achieves a high level of performance, achieving 97.8% accuracy, 96% precision, 93% recall, and an F1-score of 92% on the validation data, demonstrating the LSTM model's ability to make accurate and reliable predictions. Word cloud analysis shows that the model is able to consistently recognize person entities such as "Prabowo", "Sri Mulyani", and "Agnez Mo". However, the model still experiences limitations in detecting unfamiliar or rarely appearing entities. Overall, this study shows that the combination of spaCy and LSTM is effective for NER tasks on Indonesian social media texts and has the potential for further development with increased data variety and improvements to the labeling process.

Keywords: Named Entity Recognition, LSTM, social media, national figures, NLP