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All Journal PROSIDING SEMINAR NASIONAL Pendidikan Matematika JURNAL PENDIDIKAN TAMBUSAI Pelita Eksakta Journal of Mathematics UNP UNP Journal of Statistics and Data Science
Atus Amadi Putra
Departemen Statistika, Universitas Negeri Padang
Biochemistry, Genetics & Molecular Biology Chemistry Computer Science & IT Decision Sciences, Operations Research & Management Education Mathematics Physics Social Sciences Other

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Comparison Of Extreme Learning Machine And Holt Winter’s Exponential Smoothing Methods In Railway Passenger Forecasting Azma, Meil Sri Dian; Dony Permana; Fadhilah Fitri; Atus Amadi Putra
UNP Journal of Statistics and Data Science Vol. 2 No. 3 (2024): UNP Journal of Statistics and Data Science
Publisher : Departemen Statistika Universitas Negeri Padang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24036/ujsds/vol2-iss3/211

Abstract

Forecasting the number of passengers on the Pariaman Express train is an activity that is considered to have the potential to help PT KAI in maximizing passenger service facilities and comfort. It is estimated that the number of train passengers in Indonesia will always increase along with the increasing population of Indonesia. The high interest of users of this mode of transportation can be seen from historical data that continues to increase every year. PT KAI (Persero) as a single train transportation provider company needs to have several strategies in providing and meeting passenger needs every day. In the study of forecasting the number of passengers on the Pariaman Express train using the Holt Winters exponential smoothing method and one of the artificial neural network methods, namely the extreme learning machine. The purpose of this study was to determine the comparison of the accuracy values ​​of the forecast results produced by the two methods, and to find out which method is good to use in this forecast. The data used is data on the number of Pariaman Express train passengers from 2021-2023. The results of the study show that the comparison of the accuracy values ​​of the forecasting of the number of train passengers shows that the Holt Winter's and ELM methods have error values ​​above 10%, meaning that the Holt Winter's and ELM methods are good at forecasting for 4 periods. Holt Winter's has a MAPE value of 17.10% and ELM has a MAPE value of 20%.
Co-Authors Admi Salma Azma, Meil Sri Dian defal aditya defran Dina Fitria Dinda Fitriza Dodi Vionanda Dony Permana Elsa Oktaviani Elvina Catria Erni Syofna Fadhilah Fitri Fanni Rahma Sari Febiola Putri, Febi Findri Wara Putri Futiah Salsabila Hana Rahma Trifanni Hasnatul Huda Helma Helma Mardhiatul Azmi Melda Elfiyanti Melin Wanike Ketrin Muhammad Alif Yustin Nilda Yanti Nonong Amalita Nurhamidah Nurhamidah Okia Dinda Kelana Puti Utari Maharani Rahmadani Iswat Rahmawita Rahmawita Rina Febriana Rini Hartati Rizqia Salsabila Siskha Maulana Basrul Sofia Edriati Syifa Amelia Tessy Octavia Mukhti Villia Anggraini Vina Delfi Viola Yuniza Widia Handa Riska Yenni Kurniawati Yerizon Yerizon Yolian Widya Ningsih Yulyanti Harisman Zamahsary Martha Zilrahmi, Zilrahmi