Article Per Year (5 Year)
p-Index From 2020 - 2025
0.23
P-Index
This Author published in this journals
All Journal RISTEC : Research in Information Systems and Technology
Mrs Rahmawati
Program Studi Sistem Informasi, Fakultas Ilmu Terapan Dan Sains, Institut Pendidikan Indonesia
Computer Science & IT

Published : 1 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 1 Documents
Search

 1 
Implementation of the Support Vector Regression Algorithm And Particle Swarm Optimization In Sales Forecasting Mrs Rahmawati; Riyad Sabilul Muminin; Rizi Ramdhani; Irdam Denni
RISTEC : Research in Information Systems and Technology Vol 1, No 1 (2020): Implementation of Manager Performance Decision Support System Using the Simple A
Publisher : Institut Pendidikan Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (461.678 KB) | DOI: 10.31980/ristec.v1i1.1187

Abstract

Abstract: Sales forecasting is an important element in planning business strategies that have the possibility of happening in the future. But how to improve the accuracy of the current forecasting process is still a big question mark for companies. This study uses the Support Vector Regression algorithm because it is one of the forecasting techniques that is categorized as good compared to other techniques. The Particle Swarm Optimization algorithm is integrated for attribute optimization so that forecasting accuracy can be better. Based on the test results, it was found that SVR-PSO produced an RMSE value of 9.40.Keywords: sales forecasting; support vector regression; particle swarm optimization.Abstrak: Peramalan penjualan merupakan elemen penting dalam perencanaan strategi bisnis yang memiliki kemungkinan terjadi di masa depan. Tetapi bagaimana meningkatkan ketepatan proses peramalan saat ini masih menjadi tanda tanya besar bagi perusahaan. Penelitian ini menggunakan algoritme Support Vector Regression karena merupakan salah satu teknik peramalan yang dikategorikan baik dibandingkan dengan teknik yang lainnya. Algoritme Particle Swarm Optimization diintegrasikan untuk optimasi atribut agar akurasi peramalan dapat lebih baik. Berdasarkan hasil pengujian, didapatkan SVR-PSO menghasilkan nilai RMSE 9.40.Kata kunci : peramalan penjualan; support vector regression; particle swarm optimization.
Co-Authors Irdam Denni Riyad Sabilul Muminin Rizi Ramdhani