<![CDATA[Abstract : The development of information technology and social media has made it easier for people to express their opinions on various issues, including the government’s responsiveness in handling flood disasters. The large number of comments generated on social media makes manual opinion analysis ineffective because it requires a long time and involves a large volume of data. Therefore, an automated method is needed to process and classify public sentiment. This study aims to implement text mining to analyze public sentiment toward the government’s responsiveness in flood disaster management using the K-Nearest Neighbour (KNN) algorithm. The research data were collected from social media platforms such as Instagram, TikTok, and X, consisting of public comments related to flood disasters in Aceh and North Sumatra. The research stages include data collection, text preprocessing, feature extraction, data splitting into training and testing sets, sentiment classification using the KNN method, and evaluation of classification results. The system was developed as a web-based application using PHP programming language and MySQL database. The results of this study are expected to provide an overview of public opinion tendencies in the form of positive, negative, and neutral sentiments and serve as evaluation material for the government to improve its response in flood disaster management. Keywords: Text Mining, Sentiment Analysis, K-Nearest Neighbour (KNN), Social Media, Flood Disaster, Government Responsiveness. Abstrak : Perkembangan teknologi informasi dan media sosial menyebabkan masyarakat semakin mudah menyampaikan pendapat terhadap berbagai isu, termasuk ketanggapan pemerintah dalam penanggulangan bencana banjir. Banyaknya komentar yang muncul di media sosial menjadikan analisis opini secara manual tidak efektif karena membutuhkan waktu yang lama dan jumlah data yang besar. Oleh karena itu, diperlukan metode otomatis untuk mengolah dan mengklasifikasikan sentimen masyarakat. Penelitian ini bertujuan untuk mengimplementasikan text mining dalam menganalisis sentimen masyarakat terhadap ketanggapan pemerintah dalam penanggulangan bencana banjir menggunakan algoritma K-Nearest Neighbour (KNN). Data penelitian diperoleh dari platform media sosial Instagram, TikTok, dan X yang berisi komentar masyarakat terkait bencana banjir di wilayah Aceh dan Sumatera Utara. Tahapan penelitian meliputi pengumpulan data, preprocessing teks, ekstraksi fitur, pembagian data training dan testing, proses klasifikasi menggunakan metode KNN, serta evaluasi hasil klasifikasi. Sistem dibangun berbasis web menggunakan bahasa pemrograman PHP dan database MySQL. Hasil penelitian diharapkan dapat memberikan gambaran kecenderungan opini masyarakat dalam bentuk sentimen positif, negatif, dan netral serta membantu sebagai bahan evaluasi bagi pemerintah dalam meningkatkan kualitas respon terhadap bencana banjir. Kata kunci: : Text Mining, Analisis Sentimen, K-Nearest Neighbour (KNN), Media Sosial, Bencana Banjir, Ketanggapan Pemerintah.]]>
