ANALISIS TOPIK KELUHAN MASYARAKAT TERHADAP PENANGANAN BANJIR DI MEDIA SOSIAL MENGGUNAKAN BERTopic
Abstract
Banjir merupakan salah satu bencana yang menimbulkan berbagai permasalahan dalam penanganannya, yang kemudian mendorong masyarakat menyampaikan keluhan, kritik, dan tanggapan melalui media sosial. Data komentar pada media sosial memiliki jumlah yang besar, bersifat tidak terstruktur, dan menggunakan bahasa yang beragam sehingga sulit dianalisis secara manual. Penelitian ini bertujuan mengidentifikasi dan memetakan topik-topik keluhan masyarakat terhadap penanganan banjir melalui media sosial YouTube dan TikTok menggunakan metode BERTopic. Data dikumpulkan dari enam sumber konten pada kedua platform dan diproses melalui tahapan preprocessing yang meliputi case folding, cleaning, normalization, tokenization, dan stopword removal. Selanjutnya, pemodelan topik dilakukan menggunakan Sentence-BERT dengan model paraphrase-multilingual-MiniLM-L12-v2, UMAP, HDBSCAN, dan c-TF-IDF. Dari 18.447 komentar hasil preprocessing, BERTopic menghasilkan 268 topik yang kemudian diinterpretasikan dan dipilih 10 topik yang paling relevan, yaitu Sebaran Banjir, Kebutuhan Air Bersih, Permintaan Bantuan, Relawan, Masyarakat Terdampak, Akses Bantuan, Penyaluran Bantuan, Kecepatan Penanganan, Status Bencana, dan Kritik Pemerintah. Evaluasi menggunakan Normalized Pointwise Mutual Information (NPMI) menghasilkan nilai 0,1962. Hasil penelitian menunjukkan bahwa BERTopic dapat digunakan untuk memetakan berbagai isu keluhan masyarakat secara lebih terstruktur, dengan isu bantuan, kondisi bencana, kebutuhan air bersih, relawan, dan kritik terhadap penanganan pemerintah menjadi bagian penting dalam pembentukan topik.
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