Analisis Sentimen Diseminasi Produk Iklim Menggunakan Metode Recurrent Neural Network (RNN) dalam Klasifikasi dan Density-Based Spatial Clustering of Applications with Noise (DBSCAN) untuk Klasterisasi

Noris Mestika; Aji Supriyanto

doi:10.47065/bits.v7i4.9025

Noris Mestika Universitas Stikubank, Semarang, Indonesia
Aji Supriyanto * Universitas Stikubank, Semarang, Indonesia

(*) Corresponding Author

DOI: https://doi.org/10.47065/bits.v7i4.9025

Keywords: Sentiment Analysis; Climate Dissemination; RNN; DBSCAN; Social Media

Abstract

Climate change and extreme weather events have a significant impact on various sectors of life, making the accurate and timely dissemination of climate information crucial. Public sentiment can be an indicator of public assessment of climate dissemination. The implications of the sentiment analysis itself can be used as a communication strategy from information providers to the public. This study aims to analyze public sentiment toward the dissemination of climate products by the Central Java Climatology Station through social media platforms Instagram (@bmkgjateng) and X (@bmkg_semarang). The analysis was conducted using a hybrid framework integrating the Recurrent Neural Network (RNN) method for sentiment classification and the Density-Based Spatial Clustering of Applications with Noise (DBSCAN) for topic clustering and outlier identification. A total of 12,847 comments were collected via web scraping from 2020 to 2024. The RNN classification results revealed a dominance of neutral responses (76.41%), followed by negative (13.15%) and positive (10.44%) sentiments. The model achieved high performance with 96% accuracy and a weighted average F1-Score of 0.96. DBSCAN successfully identified 82 topic clusters and classified 74.5% of the data as noise, largely consisting of non-topical interactions or spam. The validity of the cluster structure was confirmed by a Silhouette Coefficient of 0.3675, a Davies-Bouldin Index of 0.504, and a Calinski-Harabasz Index of 191.395, indicating that the formed topic clusters possess a robust structure and are distinctly separated from one another. Integrative analysis revealed that negative sentiments were consistently focused on specific issue clusters such as floods and extreme heat, whereas positive sentiments were dispersed across service appreciation. These findings suggest the necessity of implementing an automatic filtration

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