ANALISIS SENTIMEN TERHADAP KEBIJAKAN ELECTRONIC TRAFFIC LAW ENFORCEMENT (ETLE) DKI JAKARTA MENGGUNAKAN ALGORITMA NAIVE BAYES DAN SELEKSI FITUR CHI-SQUARE - Adla Anugrah Abbas (2023)
One of the major sources of problems in a big city is congestion and each government has its own way of solving these problems, including the policy plan to be taken by the DKI Jakarta provincial government, namely Electronic Traffic Law Enforcement (ETLE). In an era like today, the growth of social media has grown rapidly so that it can provide various opinion information from other people. One of them is Twitter, which is one of the most popular social media among internet users. Naïve Bayes is one of the most effective and efficient inductive learning algorithms for machine learning and data mining. Based on this, the author wants to conduct research on sentiment analysis of twitter users regarding the Electronic Traffic Law Enforcement (ETLE) policy taken by the DKI Jakarta provincial government. The algorithm that will be used in this research is the Naïve Bayes algorithm to be able to classify positive sentiment or negative sentiment towards the Electronic Traffic Law Enforcement (ETLE) policy