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All Journal BAREKENG: Jurnal Ilmu Matematika dan Terapan Islamic Economics Methodology
Hakim, Bashir Ammar
Unknown Affiliation
Religion Computer Science & IT Control & Systems Engineering Economics, Econometrics & Finance Energy Engineering Library & Information Science Mathematics Mechanical Engineering Physics Transportation

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TIME SERIES MODEL FOR TRAIN PASSENGER FORECASTING Hakim, Bashir Ammar; Billy, Billy; Notodiputro, Khairil Anwar; Angraini, Yenni; Mualifah, Laily Nissa Atul
BAREKENG: Jurnal Ilmu Matematika dan Terapan Vol 19 No 2 (2025): BAREKENG: Journal of Mathematics and Its Application
Publisher : PATTIMURA UNIVERSITY

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30598/barekengvol19iss2pp755-766

Abstract

Trains as a means of public transportation have an important role in connecting various regions of Jabodetabek. Therefore, it is necessary to have a deep understanding of the trend of train passenger movements and predict the number of train passengers in the next period in order to optimize the management and service of train passengers properly. In this study, we examine two methods that can be used as forecasting methods for train passenger data sourced from the Central Statistics Agency (BPS), namely ARIMA and Prophet. This study demonstrates that the optimal ARIMA model is ARIMA (0,2,1), achieving a Mean Absolute Percentage Error (MAPE) of 4.91% and a Root Mean Square Error (RMSE) of 1754.970. In addition, the Prophet model, which is an additive regression model designed by Facebook for time series forecasting was also obtained with a MAPE of 0.04% and an RMSE of 1170.59. Considering the MAPE and RMSE values of the two models, the Prophet model emerges as the most suitable for forecasting the number of train passengers in the Jabodetabek region.
Islamic Crowdfunding: A Twitter Sentiment Analysis Hakim, Bashir Ammar
Islamic Economics Methodology Vol. 2 No. 2 (2023): Islamic Economics Methodology
Publisher : SMART Insight

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58968/iem.v2i2.354

Abstract

This research aims to measure public sentiment related to Islamic crowdfunding on the Twitter social media platform. The research method involves the extraction and classification of tweet data using a Python Library called VADER (Valence Aware Dictionary and Sentiment Reasoner). The research utilized tweet data posted in the past one year. The results showed fluctuations and decreases in the number of tweets discussing Islamic crowdfunding. Word cloud analysis on keywords shows that in positive sentiments, words such as "crowdfunding platform," "crowdfunding impact," "crowdfunding," "Islamic finance," and "inclusivity Islamic finance" dominate the visualization. Overall, the majority of tweets expressed a positive attitude towards Islamic crowdfunding, with 44.0% positive sentiment. A total of 47.2% of tweets showed neutral sentiment, while 8.7% showed negative sentiment. These results illustrate that people generally give positive support to the concept of Islamic crowdfunding, although there is still a small proportion of tweets that express less favorable views. This research provides valuable insights into people's perceptions and responses to Islamic crowdfunding in cyberspace.
Co-Authors Billy, Billy Mualifah, Laily Nissa Atul Yenni Angraini