TY - GEN
T1 - Sentiment Analysis of Jakarta Bus Rapid Transportation Services using Support Vector Machine
AU - Nurthohari, Zayyana
AU - Sensuse, Dana Indra
AU - Lusa, Sofian
N1 - Publisher Copyright:
© 2022 IEEE.
PY - 2022
Y1 - 2022
N2 - Jakarta Bus Rapid Transportation is state-own company which have services in public transportation. On October 2021, Jakarta Bus Rapid Transportation was recently trending on Twitter. Twitter public views might be utilized for the company as a decision support system for enhance and evaluate the services of the company. A sentiment analysis method may be used to examine public opinion especialy users of Jakarta Bus Rapid Transportation on Twitter. The goal of this research is to better understand Jakarta's public opinion trends about services. The researchers manually classified tweets from the Tweepy collection as Informasi, Apresiasi, Saran, or Komplain. Professionals will classify the sentiment as favorable, negative, or neutral. The data was then pre-processed to eliminate duplicates and extraneous information. The sentiment of fresh data will then be predicted using machine learning. The machine learning algorithms were then examined using a number of tests to discover which kernels and features provided the best accuracy. The result of this method shows of 92.00 percent of accuracy, 91.00 percent of precision, 92.00 percent of recall, and 2123 of support. The majority of Jakartans, according to the data, have an unfavorable impression of bus rapid transit. The majority of customers were disappointed with the services.
AB - Jakarta Bus Rapid Transportation is state-own company which have services in public transportation. On October 2021, Jakarta Bus Rapid Transportation was recently trending on Twitter. Twitter public views might be utilized for the company as a decision support system for enhance and evaluate the services of the company. A sentiment analysis method may be used to examine public opinion especialy users of Jakarta Bus Rapid Transportation on Twitter. The goal of this research is to better understand Jakarta's public opinion trends about services. The researchers manually classified tweets from the Tweepy collection as Informasi, Apresiasi, Saran, or Komplain. Professionals will classify the sentiment as favorable, negative, or neutral. The data was then pre-processed to eliminate duplicates and extraneous information. The sentiment of fresh data will then be predicted using machine learning. The machine learning algorithms were then examined using a number of tests to discover which kernels and features provided the best accuracy. The result of this method shows of 92.00 percent of accuracy, 91.00 percent of precision, 92.00 percent of recall, and 2123 of support. The majority of Jakartans, according to the data, have an unfavorable impression of bus rapid transit. The majority of customers were disappointed with the services.
KW - public transportation
KW - sentiment analysis
KW - SVM classifier
KW - TF-IDF
UR - http://www.scopus.com/inward/record.url?scp=85137945472&partnerID=8YFLogxK
U2 - 10.1109/ICoDSA55874.2022.9862903
DO - 10.1109/ICoDSA55874.2022.9862903
M3 - Conference contribution
AN - SCOPUS:85137945472
T3 - 2022 International Conference on Data Science and Its Applications, ICoDSA 2022
SP - 171
EP - 176
BT - 2022 International Conference on Data Science and Its Applications, ICoDSA 2022
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2022 International Conference on Data Science and Its Applications, ICoDSA 2022
Y2 - 6 July 2022 through 7 July 2022
ER -