Sentimen Publik Terhadap Kebijakan Pemindahan Ibu Kota Indonesia di X Menggunakan Model BiLSTM-CNN

Wanda Nugraha (Institut Pertanian Bogor)
Mochamad Tito Julianto (Institut Pertanian Bogor)
Mohamad Khoirun Najib (Institut Pertanian Bogor)
Elis Khatizah (Institut Pertanian Bogor)

Jurnal Telematika
Vol. 20 No. 2 (2025)

The development of Indonesia's new capital city, Ibu Kota Nusantara (IKN), is an innovative government policy that has sparked diverse public responses. This study aims to explore sentiment trends on the social media platform X to understand public perceptions of the policy. Additionally, a sentiment classification model combining Bidirectional Long Short-Term Memory (BiLSTM) and Convolutional Neural Network (CNN) was developed and optimized through hyperparameter tuning. Exploratory analysis showed that positive sentiment dominated at 46%, followed by negative at 30% and neutral at 24%. The classification model achieved a test accuracy of 78% and an average accuracy of 81% across 10-fold cross-validation, with a standard deviation of 0.006. The achieved accuracy, together with the low cross-validation standard deviation, indicates that the BiLSTM-CNN model demonstrates stable and reliable performance.