Information dissemination can occur through any media, including social media. One of the social media that has become a forum for disseminating information is Twitter. Through user-uploaded tweets, not a few comments are positive (praise/support) or negative (blasphemy), depending on the tweet. This study chooses politics as a discussion. Data crawling was carried out to obtain a dataset and raise the topic of Joko Widodo as a President of Indonesia, whose work is considered poor by the public, so they want him to resign immediately. This makes it interesting because we can identify user behavior from tweets about the topic. The choice of this topic was based on a lot of users who discussed it, so it was trending on Twitter. Preprocessing stage aims to eliminate missing values. After that, it then goes through the feature extraction process. The agglomerative Hierarchical Clustering Algorithm of the clustering method is applied in this research. This algorithm can directly set how many clusters to facilitate the clustering process. The result obtained 3 clusters with different user behavior. Negative user behavior is found in cluster 1, while positive user behavior is found in cluster 2.
Penelitian ini mengkaji jaringan komunikasi di media sosial Twitter yang cepat menyebar dari sebuah tagar #KomnasHAM dan KKB Papua yang menjadi buah bibir seluruh masyarakat Indonesia untuk memberikan opini publik atau kritik kepada pejabat tinggi yang ada di Komnas HAM.Penelitian ini bertujuan untuk menganalisis opini atau perilaku pengguna media sosial tersebut, apakah menjadi sebuah kritikan yang membangun pada permasalahan ini atau tidak, menganalisis perannya jaringan sosial, dan mengidentifikasi pengguna media sosial Twitter yang berpengaruh pada tagar #KomnasHAM dan KKB Papua. Metode penelitian yang dilakukan pada penelitian ini adalah Social Network Analysis (SNA). Hasilnya memperlihatkan bahwa entitas user yang memiliki sentimen netral menjadi entitas dengan jumlah terbanyak dibandingkan entitas dengan sentimen positif maupun negatif.
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