<![CDATA[The e-Tilang system is a solution to disciplining motorized vehicle drivers from committing many traffic violations. The existence of e-Tilang is also a solution to preventing law enforcer delinquency from illegal levies, peaceful terms in place, to accountability for fines. The effectiveness and efficiency of the e-Tilang system raise various comments from the public. Lately, a technique has become popular for extracting information from piles of data, especially on Twitter, namely Text Mining or often also called sentiment analysis. Twitter is a type of social media that is quite popular and in demand by the whole world community, including Indonesia, which provides various information. Support Vector Machine (SVM) is a set of guided learning methods that analyze data and recognize patterns, is used for classification and regression analysis, and is considered a relatively new method. The purpose of this study was to analyze the sentiments of Twitter users regarding the implementation of the e-Tilang system using the Support Vector Machine (SVM) algorithm by calculating the value of the tweet data which has a yield of 74.20%, precision of 83.33% and recall of 5.28%. Sentiment results on social media Twitter regarding the implementation of e-tickets are classified as neutral. From the results of research using the Support Vector Machine (SVM) algorithm the results of sentiment on social media Twitter regarding the implementation of e-Tilang are classified as neutral.]]>
