Setia Pramana
Politeknik Statistika STIS, Jakarta Indonesia

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Forecasting Number of Passengers of TransJakarta using Seasonal ARIMAX Method Maftukhatul Qomariyah Virati; Diory Paulus Pamanik; Setia Pramana
Journal of Data Science and Its Applications Vol 3 No 1 (2020): Journal of Data Science and Its Applications
Publisher : Telkom University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34818/jdsa.2020.3.45


TransJakarta is one of the most common public transportation modes used by the public in Jakarta. Every day there are more than 300.000 people who use TransJakarta . The number of TransJakarta buses is still limited, so to optimize services, we should know when the number of users in peak time and when the number of users in low time. In addition to providing comfort to customers, maintenance for TransJakarta buses can also be optimized, thereby reducing incident and unwanted events. This study investigates the pattern of the number of TransJakarta passengers differs on weekends, weekdays, and holidays. Also, this study predict how many TransJakarta passengers in the future, by using SARIMAX method, which is SARIMA method with X - factor. In the implementation, the study is conducted using R application with the addition of x-factor in the form of dummy variable for tap-in data in holiday period.The predicted result being produced is not too far away with the actual figure with the best model is SARIMA(0,0,0)(2,1,0)[7] with x-factor and the error analys is MSE = 162402173, MAPE = 2.6122 and MASE = 0.211698.
Sentiment Analysis on Overseas Tweets on the Impact of COVID-19 in Indonesia Tigor Nirman Simanjuntak; Setia Pramana
Indonesian Journal of Statistics and Applications Vol 5 No 2 (2021)
Publisher : Departemen Statistika, IPB University dengan Forum Perguruan Tinggi Statistika (FORSTAT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29244/ijsa.v5i2p304-313


This study aims to conduct analysis to determine the trend of sentiment on tweets about Covid-19 in Indonesia from the Twitter accounts overseas on big data perspective. The data was obtained from Twitter in the period of April 2020, with the word query "Indonesian Corona Virus" from foreign user accounts in English. The process of retrieving data comes from Twitter tweets by crawling the text using Twitter's API (Application Programming Interface) by employing Python programming language. Twitter was chosen because it is very fast and easy to spread through status updates from and among the user accounts. The number of tweets obtained was 8,740 in text format, with a total engagement of 217,316. The data was sorted from the tweets with the largest to smallest engagement, then cleaned from unnecessary fonts and symbols as well as typo words and abbreviations. The sentiment classification was carried out by analytical tools, extracting information with text mining, into positive, negative, and neutral polarity. To sharpen the analysis, the cleaned data was selected only with the largest engagement until those with 100 engagements; then was grouped into 30 sub-topics to be analyzed. The interesting facts are found that most tweets and sub-topics were dominated by the negative sentiment; and some unthinkable sub-topics were talked by many users.