Analisis Sentimen Kebijakan Pemindahan Ibukota Republik Indonesia dengan Menggunakan Algoritme Term-Based Random Sampling dan Metode Klasifikasi Naive Bayes - Akhmad Sa'rony; Putra Pandu Adikara; Randy Cahya Wihandika (Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer, 2019)
The capital city relocation policy of the Republic of Indonesia that was announced by President Joko Widodo last August caused many pros and cons in the community, especially in the social media environment. In this study, sentiment analysis of the policy is done using data obtained from Twitter. The system development process includes data scraping, preprocessing, Raw Term Frequency calculation and classification using the Naive Bayes method. In preprocessing, the filtering process is done using the Term-Based Random Sampling algorithm to create a stoplist. The testing process is done by 2 methods, parameter testing and multiclass confusion matrix testing. Parameter testing is done by changing the percentage of term of the training data used as a stoplist, ranging from 0 percent to 60 percent, while the confusion matrix is ​​used to calculate the value of accuracy, precision, recall, and f-measure. Based on the confusion matrix test results, the system gets the best macroaverage value in the classification with a stoplist of 20 percent with an accuracy macroaverage value of 0,94, precision macroaverage value of 0,945, recall macroaverage value of 0,94, and f-measure macroaverage value of 0,938.