Ultimatics : Jurnal Teknik Informatika, Vol. 14, No. 1 | June 2022 1 ISSN 2085-4552 Sentiment Analysis of An Internet Provider Company Based on Twitter Using Support Vector Machine and Naïve Bayes Method Farhan Hashfi 1 , Dedy Sugiarto 2 , Is Mardianto 3 1,3 Program Studi Teknik Informatika, Fakultas Teknologi Industri, Universitas Trisakti 2 Program Studi Sistem Informasi, Fakultas Teknologi Industri, Universitas Trisakti 1 farhan0640017000008@trisakti.ac.id, 2 dedy@trisakti.ac.id, 3 mardianto@trisakti.ac.id Accepted 12 December 2021 Approved 20 February 2022 Abstract— Tweets from users in the form of opinions about a product can be used as a company evaluation of the product. To obtain this evaluation, the method that can be used is sentiment analysis to divide opinions into positive and negative opinions. This study uses 1000 data from Twitter related to an internet service provider company where the data is divided into two classes, namely 692 positive classes and 308 negative classes. In the Tweet there are still many words that are not standard. Therefore, previously carried out the initial process or preprocessing to filter out non-standard words. Before doing the classification, the data needs to be divided into training data and test data with a ratio of 90:10, then processed using the Support Vector Machine and Naïve Bayes techniques to get the results of the classification of positive opinions and negative opinions. The level of accuracy in the classification using the Support Vector Machine is 84% and using Naïve Bayes is 82%. Index Terms— Internet Provider; Naïve Bayes; Sentiment Analysis; Support Vector Machine; Twitter. I. INTRODUCTION The internet has developed very rapidly to date in influencing media and communication. One of the factors supporting the success of the Internet in Indonesia is that infrastructure development has reached remote areas in Indonesia [1]. This can be proven by the increasing use of social media. Social Media is an Internet service most commonly used by Indonesian citizens. One of them is Twitter. Twitter is used for various things such as sharing personal things, using it to sell, to reporting an opinion to a brand or company. Information shared on Twitter is typically 140 characters long [2]. In general, a company uses social media to gather information about the goods or services they offer. The most common use of social media by companies is to use social media for marketing activities and social media for customer service [3]. Therefore, the opinion group of Twitter users will be influenced by the emotions (emotions) that are classified in order to determine their polarization, namely positive opinions or negative opinions. Sentiment analysis is the process of using text analysis to derive various data sources from the Internet and various other social media platforms. One of the purposes of sentiment analysis is to get someone's opinion on a company service and then classify that opinion into positive opinions and negative sentiments [4]. In conducting sentiment analysis, the technique used to retrieve data from Twitter uses the Crawling technique which requires API permission from the platform itself. Furthermore, the techniques for classifying the tweet data are Support Vector Machine, KNearest Neighbor, and Naïve Bayes [5]. By using this method, it produces a classification between two categories, namely positive opinions and negative opinions, where the results can be useful for observers of the company's data to determine the next marketing strategy. II. THEORY A. Sentiment Analysis Sentiment analysis is one of the methods used to identify an opinion or sentiment expressed using a text or document and how that opinion is categorized as positive opinion and negative opinion. Basically, sentiment analysis tries to assess a different aspect of the standard language in order to help an agency or company to get positive opinions as well as negative opinions about the products they offer [6]. Sentiment itself can be interpreted as an emerging concept in which everyone's different emotions are determined from the content of the text, so that it can be processed to extract the opinions and sentiments of many people. In sentiment analysis, there are 3 opinions that can be a reference for agencies or companies to obtain information on the quality of the products offered, namely positive opinions, negative opinions, and neutral opinions [7]. Sentiment refers to several topics, opinions on certain topics have different meanings from other opinions that are the same on other subjects. Analyst sentiment is usually used to determine the quality of services or the quality of a product from an