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All Journal Media Statistika SAINSMAT CAUCHY: Jurnal Matematika Murni dan Aplikasi JURNAL PETERNAKAN BAREKENG: Jurnal Ilmu Matematika dan Terapan JOURNAL OF APPLIED INFORMATICS AND COMPUTING JURNAL ILMIAH PETERNAKAN TERPADU IJID (International Journal on Informatics for Development) Jambura Journal of Mathematics Euler : Jurnal Ilmiah Matematika, Sains dan Teknologi SAINSMAT: Jurnal Ilmiah Ilmu Pengetahuan Alam Jurnal Veteriner dan Biomedis
Mualifah, Laily Nissa Atul
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Agriculture, Biological Sciences & Forestry Biochemistry, Genetics & Molecular Biology Chemistry Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Earth & Planetary Sciences Economics, Econometrics & Finance Education Energy Engineering Immunology & microbiology Mathematics Mechanical Engineering Medicine & Pharmacology Physics Transportation Veterinary Other

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Time Series Intervention Analysis With Gradual Impact Function A Case Study Of Railway Passenger Volume In Java Island Zulhijrah, Zulhijrah; Isnaini, Mardatunnisa; Angraini, Yenni; Notodiputro, Khairil Anwar; Mualifah, Laily Nissa Atul
Sainsmat : Jurnal Ilmiah Ilmu Pengetahuan Alam Vol 14, No 2 (2025): September
Publisher : Fakultas Matematika dan Ilmu Pengetahuan Alam Universitas Negeri Makassar

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35580/sainsmat142774742025

Abstract

Java Island has been significantly impacted by the COVID-19 pandemic, which started in March 2020. This study aims to analyze the impact of the pandemic on the volume of railway passengers’ volume with a time series approach using an interventional ARIMA model. The data used is the number of monthly passengers from 2015 to 2024. Initial modeling on data before the pandemic produced the best model, namely ARIMA (0,2,1). To measure the impact of the pandemic, a gradual step intervention function is used which represents the gradual effect of the event. The estimation results show that the ARIMA (0,2,1) model with a gradual step intervention function is able to provide more accurate forecasting results, with a MAPE value of 18.39%. This model effectively captures changes in mobility patterns due to the pandemic, especially in the post-intervention recovery phase. The findings make an important contribution to transportation policy evaluation and future strategic planningKeywords: Time Series, ARIMA  Intervention, Gradual Function, Railway 
Co-Authors Ahdiat, Keyzha Mutiara Al Maida, Mahda Alahmad, Ali Omar Amaliya, Sri Amanda, Nabila Tri Amatullah, Fida Fariha Billy, Billy Cici Suhaeni Cintani, Meavi Fachry Abda El Rahman Fitrianti, Dwi Ghiffary, Ghardapaty Ghaly Hakim, Bashir Ammar Hari Wijayanto Hasanah, Lailatul Hasanah, Mauizatun Ilma, Meisyatul Ilmani, Erdanisa Aghnia Indahwati Isnaini, Mardatunnisa Karunia, Nia Kefi Amtiran, Chandraone Putra Kurnadipare, Aleytha Ilahnugrah Magfirrah, Indah Maulana, Muhammad Fahrezi MY, Hadyanti Utami Nensi, Andi Illa Erviani Nizar, Yeky Abil Nugraha, Adhiyatma Nurdin, Achmad Raihan Pangestika, Adelia Putri Pratiwi, Windy Ayu Putri, Mega Ramatika Rahmawati, Elke Frida Rasyid, Baharun Riska Yulianti, Riska Riyanto, Indra Mahib Zuhair Rizki, Anwar Fajar Sachnaz Desta Oktarina Setyowati, Silfiana Lis Siregar, Indra Rivaldi Sri Wahyuni Syam, Ummul Auliyah Ulfia, Ratu Risha Yanuari, Eka Dicky Darmawan Yenni Angraini Zulhijrah, Zulhijrah