Sentimen Analisis Pengguna Jasa Layanan Kereta Api dengan Menggunakan Metode CNN (Convolutional Neural Network) - Zidan Alfikri; Ari Muzakir; Susan Dian Purnamasari; Rahayu Amalia (Building of Informatics, Technology and Science (BITS), 2026)
Train services are a popular mode of transportation in Indonesia, especially in the Greater Jakarta area. However, the quality of train services is often debated among users. This study aims to analyze the sentiment of train service users using the Convolutional Neural Network (CNN) method with a focus on the DAOP 1 Jakarta area. The data used are reviews or comments of train users taken from Indonesian Railways social media. The results of the study show that the CNN method can classify user sentiment analysis with accurate results or high accuracy. This sentiment analysis shows that train users in DAOP 1 Jakarta have positive sentiments towards aspects such as punctuality, service, comfort and safety. The results of this study can help the railway to understand user needs and complaints so that they can improve service quality with a final value of 89.29% accuracy, 88.73% precision, 90.00% recall, and 89.36% F1-score.