Pembentukan Daftar Stopword Menggunakan Term Based Random Sampling Pada Analisis Sentimen Dengan Metode Naïve Bayes (Studi Kasus: Kuliah Daring Di Masa Pandemi) by Raditya Rinandyaswara, Yuita Arum Sari, Muhammad Tanzil Furqon

“…In Term Based Random Sampling, there are 3 parameters, namely Y for the number of random word retrieval repetitions, X for the lowest number of weights in Y repetitions, and L as the percentage of the number of stopwords you want to use. So this research is aimed at finding the best combination of these 3 parameters and comparing the Term Based Random Sampling stopword with the stopword tuning and without the stopword removal process in the analysis of tweet sentiment regarding online lectures using the Naïve Bayes method. …”
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Pembentukan Daftar Stopword Goffman Transition Point dengan Pembobotan Emoji pada Analisis Sentimen di Twitter by Rizky Maulana Iqbal, Yuita Arum Sari, Edy Santoso

“…Using a stopword Tala with the same parameters produces an accuracy of 73% with emoji-weighted and 71.9% without emoji-weighted. Based on these results it can be concluded that the formation of stopwords and weighting of emojis can improve accuracy. …”
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