JURIKOM (Jurnal Riset Komputer), Vol. 9 No. 3, Juni 2022 e-ISSN 2715-7393 (Media Online), p-ISSN 2407-389X (Media Cetak) DOI 10.30865/jurikom.v9i3.4143 Hal 543−549 http://ejurnal.stmik-budidarma.ac.id/index.php/jurikom Copyright © 2022 Arya Pratama Anugerah, Page 543 JURIKOM is licensed under a Creative Commons Attribution 4.0 International License Sentiment Analysis Of Development Jakarta-Bandung High-Speed Train Using Twitter Social Media With BNN Method Arya Pratama Anugerah, Yuliant Sibaroni * Faculty of Informatics, Informatics, Telkom University, Bandung, Indonesia Email: 1 aryapar@student.telkomuniversity.ac.id, 2,* yuliant@telkomuniversity.ac.id Email Penulis Korespondensi: yuliant@telkomuniversity.ac.id Submitted 19-05-2022; Accepted 29-05-2022; Published 30-06-2022 Abstract The Jakarta-Bandung high-speed train is one of the infrastructure development projects currently being carried out by the Indonesian government. The project is a large project that requires a long processing time and very large costs. Therefore, infrastructure development has reaped a lot of public opinions, both positive and negative. The purpose of writing this Final Project is to analyze sentiment on public opinion about the construction of the Jakarta-Bandung high-speed train. With data sourced from Twitter social media, the data will be analyzed in three classes, namely positive, negative, and neutral classes where the weighting will use the TF- IDF. The classification method used in this study is the Backpropagation Neural Network method. The best results were obtained in this study using a hyper tuning scenario with an accuracy of 74.56%. Keywords: Jakarta-Bandung High-Speed Train; Sentiment Analysis; Twitter; TF-IDF; Backpropagation Neural Network 1. INTRODUCTION High-speed trains consist of two components, namely high-speed trains and special high-speed lines. The combination of the two will result in fast trains that are twice as fast as driving by car and more comfortable than short-haul flights [1]. The Jakarta-Bandung high-speed train is a high-speed rail project in Indonesia that connects the capital city of Jakarta and the city of Bandung with 4 stopping stations Halim, Karawang, Padalarang, and Tegalluar [2][3]. The Jakarta-Bandung high-speed rail project has attracted a lot of attention and opinions from the public. For example, the opinion of this project will cause the subsidy budget to increase when the project is completed because there will be a transportation subsidy budget for KAI operations at the beginning of the high-speed train operation and high- speed train tickets later [4]. Or opinions about the many opportunities created by the presence of existing stations such as bringing in a lot of new investment and the development of supporting industries that will open up many new jobs [5]. The public also expressed their opinion about the Jakarta-Bandung high-speed rail project on social media such as Twitter. Therefore, the author wants to analyze how public sentiment is on the Jakarta-Bandung high-speed rail project, especially on Twitter. Sentiment analysis also called opinion mining, is a study that analyzes how a person's opinions, sentiments or feelings, behavior, and emotions towards an entity are expressed through writing [6]. In research [7], in the field of infrastructure, public opinion is indirectly involved in the feasibility study, construction, and post-evaluation of the project. The results of this study were that 47.7% of the messages were negative sentiments toward large hydro infrastructure projects using the lexicon-based classification method, an accuracy of 88.1% was obtained. Another research in the field of infrastructure is research [8], examining the sentiment analysis of the people of Surabaya towards Mrs. Tri Rismaharini's policy as the mayor of Surabaya regarding infrastructure issues, the results show that most of the people of Surabaya express negative sentiments with a focus on topics regarding electricity and toll roads as highways. Research [9] raises the issue of President Jokowi's leadership which will be analyzed by sentiment on Twitter. As a result, 51% negative sentiment, 31% positive sentiment, and 18% neutral sentiment towards President Jokowi's leadership using the CNN classification method and also the expansion of the word2vec obtained 57% accuracy. Another research on sentiment analysis is on covid-19 using the Backpropagation Neural Network method, which obtained f1-score results of positive 0.77, negative 0.75, and neutral 0.5 using 100 hidden layers obtained 70% accuracy [10]. Therefore, the writer in this Final Project raises the issue regarding the construction of the Jakarta-Bandung high- speed train because there are still few studies that address this issue. By using the classification method, namely Backpropagation Neural Network. The advantage is that Backpropagation Neural Network can more easily formulate experiences and predictions and is very flexible in changing prediction rules and learning by adjusting the weights of each perceptron network [11]. Backpropagation Neural Network also has better performance than other classifications that do not include NN such as SVM [8]. The author also uses TF-IDF feature extraction because, in research [7], more detailed function features are needed to analyze data so that classification performance is better. To measure the performance of the method, the author uses a confusion matrix to find the accuracy and f1-score value of the model made. 2. RESEARCH METHODOLOGY 2.1 Research Stage