Sentiment Analysis Of Indihome Service Based On Geo Location Using The Bert Model On Platform X - Siregar, Robiatul Adawiyah; Fitriyani, Fitriyani; Darfiansa, Lazuardy Syahrul (Informatika, Universitas Jenderal Soedirman, 2025)
The rapid growth of internet usage in Indonesia has led more people to express their feelings, whether positive or negative, about online services, including IndiHome, through social media platforms such as X (formerly Twitter). This study aims to analyze public sentiment toward IndiHome services based on geographic location using the IndoBERT natural language processing model. The data consists of 3.307 Indonesian tweets that are geo-tagged and categorized into three sentiment groups: good, okay, and bad. The research process involves collecting the data, cleaning it (organizing and splitting words), and testing the IndoBERT model with a confusion matrix and classification scores. The findings reveal that negative feelings are more prevalent in most locations, especially in Java. The IndoBERT model achieved its highest accuracy of 80% in detecting negative sentiment. However, there is still room for improvement in distinguishing between positive and neutral sentiments, possibly due to data imbalance. The study shows how combining sentiment analysis with geo-location can provide strategic insights to service providers. In practical terms, these insights can help IndiHome prioritize infrastructure upgrades, improve customer support in areas with high dissatisfaction, and assist policymakers in promoting fairer digital access across regions. Beyond these practical implications, this study also contributes to the field of informatics by demonstrating the application of a transformer-based NLP model (IndoBERT) combined with geo-tagged data for regional sentiment mapping- a relatively unexplored approach in the Indonesian context. The integration of geospatial analysis with sentiment classification offers methodological advances for NLP-based service evaluation beyond business applications.