Article Per Year (5 Year)
p-Index From 2020 - 2025
0.408
P-Index
This Author published in this journals
All Journal Techno.Com: Jurnal Teknologi Informasi Ranah Research : Journal of Multidisciplinary Research and Development
Maghfur, Maula Aringga
Unknown Affiliation
Chemical Engineering, Chemistry & Bioengineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Economics, Econometrics & Finance Engineering Law, Crime, Criminology & Criminal Justice Public Health Social Sciences Transportation Other

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

Found 2 Documents
Search

 1 
Analysis of Forecasting Demand for Wheel Loader Unit Rental Using the Arima Method to Determine Safety Stock Inventory and Service Level at PT Petrokopindo Cipta Selaras Maghfur, Maula Aringga; Tranggono, Tranggono
Ranah Research : Journal of Multidisciplinary Research and Development Vol. 7 No. 3 (2025): Ranah Research : Journal Of Multidisciplinary Research and Development
Publisher : Dinasti Research

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.38035/rrj.v7i3.1368

Abstract

PT XYZ is a company engaged in the rental of heavy equipment such as excavators, forklifts, bulldozers, and Wheel Loaders. The problem faced is unpreparedness in dealing with fluctuations in demand, so there is often a backlog or excess inventory. This research aims to improve the accuracy of demand forecasting and determine safety stocks to anticipate these uncertainties. The research was conducted using historical data on Wheel Loader rental requests from September to November 2024. The data was processed using the ARIMA (Autoregressive Integrated Moving Average) method through several stages, namely stationarity testing, identification of ACF and PACF, model estimation, parameter testing, white noise test, and selection of the best model. The resulting significant model was ARIMA (3,1,1), with a MAPE error value of 21% (79% accuracy), an increase of 9% compared to the previous method with an error of 30%. The results of the calculation of safety stock to deal with fluctuations in demand at various service levels show that the need for 2,688 units at the 90% level, increased to 3,444 units at the 95% level, and reached 3,900 units at the 97% level. This study shows that the ARIMA method is able to improve the accuracy of forecasting and provide a better basis for determining safety stock in managing fluctuations in heavy equipment rental demand.
Utilization of Big Data For PPE Detection Using Convolutional Neural Network And Yolov8 Bisri, Hasan; Maghfur, Maula Aringga; Rahadian, Yanuar Rafi
Techno.Com Vol. 24 No. 3 (2025): Agustus 2025
Publisher : LPPM Universitas Dian Nuswantoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62411/tc.v24i3.13774

Abstract

Indonesia holds a strategic position in the global manufacturing sector, with a manufacturing output of USD 228.32 billion in 2021, ranking 10th worldwide. In 2023, it ranked 12th globally by manufacturing value added, according to the World Bank’s report. However, this growth is accompanied by 297,725 workplace accidents reported in Indonesia in 2022, marking a 27.03% increase from the previous year. This study aims to develop a Personal Protective Equipment (PPE) monitoring system using Big Data, employing Convolutional Neural Network (CNN) and You Only Look Once (YOLO) algorithms. The dataset consists of at least 1,000 images for each of four classes: Helmet, Vest, NoHelmet, and NoVest. Evaluation results show a mAP@50 of 83.1%, with the highest detection performance in Vest (0.90), followed by NoHelmet (0.88), Helmet (0.85), and NoVest (0.81). These findings demonstrate strong potential in supporting safety protocol compliance and reducing workplace accidents in high-risk industrial environments.   Keywords - Big Data, Convolutonal Neural Network, You Only Look Once
Co-Authors Hasan Bisri Rahadian, Yanuar Rafi Tranggono Tranggono