Article Per Year (5 Year)
p-Index From 2020 - 2025
0.817
P-Index
This Author published in this journals
All Journal Maksimum : Media Akuntansi Universitas Muhammadiyah Semarang Bulletin of Electrical Engineering and Informatics Resona : Jurnal Ilmiah Pengabdian Masyarakat ASSET: Jurnal Manajemen dan Bisnis JAPP : Jurnal Akuntansi, Perpajakan dan Portofolio
Hardiwinoto Hardiwinoto
Universitas Muhammadiyah Semarang
Arts Humanities Economics, Econometrics & Finance Electrical & Electronics Engineering

Published : 6 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 1 Documents
Search
Journal : Bulletin of Electrical Engineering and Informatics

 1 
A machine learning approach in Python is used to forecast the number of train passengers using a fuzzy time series model Solikhin Solikhin; Septia Lutfi; Purnomo Purnomo; Hardiwinoto Hardiwinoto
Bulletin of Electrical Engineering and Informatics Vol 11, No 5: October 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v11i5.3518

Abstract

Train passenger forecasting assists in planning, resource use, and system management. forecasts rail ridership. Train passenger predictions help prevent stranded passengers and empty seats. Simulating rail transport requires a low-error model. We developed a fuzzy time series forecasting model. Using historical data was the goal. This concept predicts future railway passengers using Holt's double exponential smoothing (DES) and a fuzzy time series technique based on a rate-of-change algorithm. Holt's DES predicts the next period using a fuzzy time series and the rate of change. This method improves prediction accuracy by using event discretization. positive, since changing dynamics reveal trends and seasonality. It uses event discretization and machine-learning-optimized frequency partitioning. The suggested method is compared to existing train passenger forecasting methods. This study has a low average forecasting error and a mean squared error.
Co-Authors Diva Ivana Mohammad Ridwan Nafisa Ischabita Nurcahyono Nurcahyono Prizka Rizmawati Arum Purnomo . Septia Lutfi Solikhin Solikhin Sriyono Sriyono Sukamto Sukamto