<![CDATA[Abstract: The development of disaster-related information in the digital space is very rapid and is often detected earlier through social media than through official government channels. This condition highlights the need for a system capable of detecting disaster-related issues quickly and dynamically across various digital platforms. This study aims to develop a dynamic disaster issue detection model based on IndoBERTweet with a Temporal Attention Mechanism using multiplatform big text data in Indonesia. The research methodology includes collecting textual data from various digital platforms such as Twitter, YouTube, TikTok, Quora, and Medium. The data are processed through preprocessing stages using Natural Language Processing (NLP) techniques, timestamp extraction to obtain temporal information, and the application of fine-grained labeling for more detailed classification of disaster-related issues. Subsequently, the IndoBERTweet model is trained with a Temporal Attention Mechanism to capture the relationship between textual context and temporal dynamics in the development of disaster-related issues. The expected results of this research are a model capable of dynamically detecting disaster-related issues by considering informal language contexts and temporal changes. This model is expected to support early warning systems and data-driven disaster management decision-making in Indonesia. Keywords: Disaster Issue Detection; Social Media Text Analysis; Multiplatform Big Data; IndoBERTweet; Temporal Attention. Abstrak: Perkembangan informasi kebencanaan di ruang digital berlangsung sangat cepat dan sering kali lebih dahulu terdeteksi melalui media sosial dibandingkan melalui kanal resmi pemerintah. Kondisi ini menunjukkan perlunya sistem yang mampu mendeteksi isu bencana secara cepat dan dinamis dari berbagai platform digital. Penelitian ini bertujuan mengembangkan model deteksi isu bencana dinamis berbasis IndoBERTweet dengan Temporal Attention Mechanism pada big data teks multiplatform di Indonesia. Metode penelitian meliputi pengumpulan data teks dari berbagai platform digital seperti Twitter, YouTube, TikTok, Quora, dan Medium. Data diproses melalui tahapan preprocessing menggunakan teknik Natural Language Processing (NLP), ekstraksi timestamp untuk memperoleh informasi temporal, serta penerapan fine-grained labeling untuk klasifikasi isu bencana yang lebih rinci. Selanjutnya, model IndoBERTweet dilatih dengan Temporal Attention Mechanism untuk menangkap hubungan antara konteks teks dan dinamika waktu dalam perkembangan isu bencana. Hasil penelitian diharapkan menghasilkan model yang mampu mendeteksi isu bencana secara dinamis dengan mempertimbangkan konteks bahasa informal dan perubahan waktu. Model ini diharapkan mendukung sistem peringatan dini dan pengambilan kebijakan kebencanaan berbasis data di Indonesia. Kata kunci: Deteksi Isu Bencana; Analisis Teks Media Sosial; Big Data Multiplatform; IndoBERTweet; Temporal Attention.]]>
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