Article Per Year (5 Year)
p-Index From 2020 - 2025
0
P-Index
This Author published in this journals
All Journal Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer
Danastri Ramya Mehaninda
Fakultas Ilmu Komputer, Universitas Brawijaya
Computer Science & IT Control & Systems Engineering Education Electrical & Electronics Engineering Engineering

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

Found 1 Documents
Search

 1 
Peramalan Persediaan Spare Part Sepeda Motor Menggunakan Algoritme Backpropagation Danastri Ramya Mehaninda; Imam Cholissodin; Sutrisno Sutrisno
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 2 No 12 (2018): Desember 2018
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (894.551 KB)

Abstract

Motorcycle are the most used roudways transportation because they are more affordable and more efficient. Motorcycle require good maintenance to keep comfortable uses and maintain motorcycle performance so as to minimize accidents. Motorcycle maintenance can be done by replacing spare parts regularly in the workshop. To support the maintenance of motorcycle, the workshop should provide the best care services including having spare part inventory to suffice customer who maintance of motorcycle. If the workshop has sufficient spare part, the workshop can minimize the cost of ordering and can minimize the damage caused by storage for too long. There are many workshops that provide spare part replacement service such as Yamaha Motor. At Yamaha Motor is having difficulty in determining the spare part inventory for the next month. Inventory forecasting can help to determine the supply of spare part on Yamaha Motor. This research uses backpropagation algorithm for forecasting spare part inventory. The best backpropagation architecture is 9-7-1, which mean 9 input nodes, 7 hidden nodes and 1 output node. The input used is the history of spare part sales the previous month. The average MSE (error value) obtained from the test result is 0.0094506 and the smallest MSE obtained is 0.0085305 with the average difference of the actual value with the forecasting result is 6. At the smallest MSE value, the forecasting result approaches the actual value and has a pattern that almost the same.
Co-Authors Imam Cholissodin Sutrisno Sutrisno