Article Per Year (5 Year)
p-Index From 2020 - 2025
0.408
P-Index
This Author published in this journals
All Journal IJISTECH (International Journal Of Information System & Technology) IJISTECH
Ramdhani, M Ronal
Unknown Affiliation
Computer Science & IT Decision Sciences, Operations Research & Management Electrical & Electronics Engineering Engineering Social Sciences

Published : 2 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 2 Documents
Search

 1 
Application of Prediction of Motorcycle Spare Part Sales with the Trend Moment Method Septiani, Noer Azni; Ramdhani, M Ronal
IJISTECH (International Journal of Information System and Technology) Vol 8, No 1 (2024): The June edition
Publisher : Sekolah Tinggi Ilmu Komputer (STIKOM) Tunas Bangsa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30645/ijistech.v8i1.342

Abstract

One important aspect of running a successful motorcycle repair shop business is being able to control the inventory of motorcycle spare parts. Motorcycle spare parts in motorcycle workshops usually estimate stock by guessing only, which results in existing stock piling up unsold, because there is no calculation for the estimated sales stock of motorcycle spare parts for the next year, this is a constraint in the availability and need for spare parts stock. The author's purpose in this study is to determine the process of forecasting or forecasting the sales stock of motorcycle spare parts in a workshop, to design and make sales predictions in the workshop that are objective and measurable in accordance with past sales data, to apply sales predictions using the Trend Moment method in determining the estimated stock of spare part sales in each subsequent month. From the existing problems, the workshop will predict the sale of motorcycle spare parts. Application design that has been designed with a system that has been implemented using PHP programming language and database using MySQL, XAMPP using the process of forecasting motorcycle spare part sales using the Trend Moment method, starting from entering sales data in the previous period, to the forecasting process using the Trend Moment method to get results on how much stock of motorcycle spare part sales for the next month
Application of Prediction of Motorcycle Spare Part Sales with the Trend Moment Method Septiani, Noer Azni; Ramdhani, M Ronal
IJISTECH (International Journal of Information System and Technology) Vol 8, No 1 (2024): The June edition
Publisher : Sekolah Tinggi Ilmu Komputer (STIKOM) Tunas Bangsa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30645/ijistech.v8i1.342

Abstract

One important aspect of running a successful motorcycle repair shop business is being able to control the inventory of motorcycle spare parts. Motorcycle spare parts in motorcycle workshops usually estimate stock by guessing only, which results in existing stock piling up unsold, because there is no calculation for the estimated sales stock of motorcycle spare parts for the next year, this is a constraint in the availability and need for spare parts stock. The author's purpose in this study is to determine the process of forecasting or forecasting the sales stock of motorcycle spare parts in a workshop, to design and make sales predictions in the workshop that are objective and measurable in accordance with past sales data, to apply sales predictions using the Trend Moment method in determining the estimated stock of spare part sales in each subsequent month. From the existing problems, the workshop will predict the sale of motorcycle spare parts. Application design that has been designed with a system that has been implemented using PHP programming language and database using MySQL, XAMPP using the process of forecasting motorcycle spare part sales using the Trend Moment method, starting from entering sales data in the previous period, to the forecasting process using the Trend Moment method to get results on how much stock of motorcycle spare part sales for the next month
Co-Authors Noer Azni Septiani, Noer Azni