SENTIMENT ANALYSIS OF PUBLIC OPINION ON SOCIAL MEDIA X TOWARDS ETHNIC ROHINGYA IN INDONESIA - Ayu Amalia; Damayanti Damayanti (JURTEKSI (Jurnal Teknologi dan Sistem Informasi), 2024)
Abstract: Rohingya refugees continue to arrive in Aceh by sea by boat. Based on data from the United Nations High Commissioner for Refugees (UNCHR), as of December 10 2023, 1,543 Rohingya refugees had landed in Aceh since mid-November 2023. The increasing number of refugees arriving has caused resistance from local residents. This rejection was the result of the bad experiences of Acehnese people with Rohingya refugees. The main problem of this research is that analyzing public opinion on Rohingya ethnicity in Indonesia is still done manually by looking at tweets one by one. The solution to overcome this is to analyze opinions using data crawling with the Naïve Bayes algorithm. The purpose of this research is to determine public opinion on Twitter media regarding refugee refugees. The method used in this research is the Naïve Bayes algorithm method. The results of the research show that the Naïve Bayes algorithm can classify public opinion sentiment on Twitter social media towards the Rohingya Ethnic in Indonesia into positive sentiment. and negative with a total accuracy of 70%. So, the words "Rohingya Ethnicity in Indonesia" tend to be accepted by the X community with the arrival of Rohingya refugees in Indonesia.            Keywords: rohingya; twitter; naïve bayes; opinion  Abstrak: Pengungsi Rohingya terus berdatangan ke Aceh melalui jalur laut dengan menggunakan perahu. Berdasarkan hasil data United Nations High Commissioner for Refugees (UNCHR), per 10 Desember 2023 sebanyak 1.543 pengungsi Rohingya datang ke dalam wilayah Aceh. Meningkatnya jumlah pengungsi yang datang menimbulkan perlawanan dari warga setempat. Penolakan ini imbas dari pengalaman buruk warga Aceh terhadap pengungsi Rohingya. Permasalahan utama penelitian ini yaitu menganalisis opini publik terhadap Etnis Rohingya di Indonesia masih secara manual dengan melihat tweet satu persatu. Solusi mengatasi hal tersebut maka analisis opini menggunakan crawling data dengan algoritma Naïve Bayes. Tujuan penelitian ini untuk mengetahui opini publik pada media twitter terkait pengungsi Rohingya. Metode yang digunakan dalam penelitian ini yaitu metode algoritma Naïve Bayes. Hasil penelitian menunjukan bahwa pada algoritma Naïve Bayes dapat mengklasifikasikan sentimen dengan total akurasi 70%. Maka “Etnis Rohingya di Indonesia” cenderung dapat diterima oleh masyarakat X dengan datangnya pengungsi Rohingya di Indonesia. Kata kunci: rohingnya; twitter; naïve bayes; opini