Article Per Year (5 Year)
p-Index From 2020 - 2025
0.659
P-Index
This Author published in this journals
All Journal Jurnal Informasi dan Teknologi Journal of Engineering and Technological Sciences
Indrapraja, Rachmadi
Unknown Affiliation
Aerospace Engineering Automotive Engineering Chemical Engineering, Chemistry & Bioengineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Electrical & Electronics Engineering

Published : 3 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 3 Documents
Search

 1 
The Influence of QRIS Digitalization, Technology and Digitalization Lifestyle, Digital Financial Literacy, and Financial Inclusion On Bank Customers Decision Mahrizal, Mahrizal; Judijanto, Loso; Indrapraja, Rachmadi; Alfiana; Pujianto, Defi
Jurnal Informasi dan Teknologi 2023, Vol. 5, No. 4
Publisher : SEULANGA SYSTEM PUBLISHER

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.60083/jidt.v5i4.426

Abstract

This study looks at how consumer decisions are impacted by the digitalization of QRIS, financial literacy, and financial inclusion through lifestyle. The participants in this study are clients of Sharia Bank. Non-probability sampling combined with a purposeful sampling approach was the sample strategy employed in this study. A sample of one hundred participants was used by the researchers. This study included both primary and secondary data. In order to acquire the primary data required by the researchers, the data collection strategy used in this study is the distribution of questionnaires. In this quantitative study, the Partial Least Squares (PLS) method of moderated regression analysis (MRA) and descriptive statistical analysis were employed as data analysis techniques. Based on the test results and discussions that have been described, the following conclusions can be drawn: The digitalization of QRIS has a direct influence on customers' decisions to make transactions using QRIS. Financial literacy has a direct influence on customer decisions. Decisions made by customers are directly impacted by financial inclusion. Decisions about transactions are directly influenced by lifestyle. The digitalization of QRIS does not directly impact lifestyle. Lifestyle is not immediately impacted by financial knowledge. There is no direct correlation between financial inclusion and lifestyle. Lifestyle cannot moderate the relationship between QRIS digitization and consumer decisions, nor does it have an indirect effect. Lifestyle cannot moderate the relationship between financial literacy and client decisions, nor does it have an indirect influence. Lifestyle cannot moderate the relationship between financial inclusion and customers' decisions to transact via QRIS, nor does it have an indirect influence.
Application of Dynamic Structural Model to Identify Factors That Influence Capital Adjustments in The National Manufacturing Industry Harsono, Iwan; Jusatria; Indrapraja, Rachmadi; Henri Kusnadi, Iwan; Rohman, Saeful
Jurnal Informasi dan Teknologi 2024, Vol. 6, No. 2
Publisher : SEULANGA SYSTEM PUBLISHER

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.60083/jidt.v6i2.526

Abstract

This research aims to determine the costs of capital adjustments and company dynamics. Company panels comprise the database. The main advantage of analyzing the nature of adjustment costs at the plant level is that the data contain information for purchases and sales of capital goods. Panel data, including information from many plants and long periods, allows for a more comprehensive analysis of firms' investment behavior in the face of capital adjustment costs. The estimation results allow for the recovery of companies' frictions in adjusting their capital stock. The estimation results for capital adjustment costs are consistent with other studies using similar methodologies. Estimates show that there are significant, unchangeable fixed costs, as well as moderate quadratic costs. Researchers then use the estimated parameters in a counterfactual simulation to analyze the impact of a decline in average firm profitability on the labor market. The results show a significant labor market response to the shock, with the transition to the new stable state being slow and taking several years to complete the adjustment. The simulations highlight the importance of not only modeling capital mobility but also considering and estimating its frictions to evaluate the impact of policies or shocks on the economy. The mobility and capital adjustment costs influence the speed of the economy's adjustment to shocks and their effects on factor allocation and remuneration in the short and long term.
Adaptive Ensemble Learning for Enhancing Building Energy Consumption Prediction: Insights from COVID-19 Pandemic Energy Consumption Dynamics Handre Kertha Utama, Putu; Leksono, Edi; Nashirul Haq, Irsyad; Indrapraja, Rachmadi; Mahesa Nanda, Rezky; Friansa, Koko; Fauzi Iskandar, Reza; Pradipta, Justin
Journal of Engineering and Technological Sciences Vol. 57 No. 2 (2025): Vol. 57 No. 2 (2025): April
Publisher : Directorate for Research and Community Services, Institut Teknologi Bandung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.5614/j.eng.technol.sci.2025.57.2.2

Abstract

Buildings account for approximately 40% of the total global energy consumption. Therefore, accurate prediction of building energy consumption is necessary to optimize resource allocation and promote sustainable energy usage. A key challenge in developing building energy consumption models is their adaptability to abrupt changes in consumption patterns owing to extraordinary events, such as the COVID-19 pandemic. Therefore, a two-layer ensemble-learning (EL) model incorporating sliding windows as input features is proposed. The model is a two-layer stacking EL consisting of two base learning methods: (1) support vector regression (SVR), and (2) random forest (RF). Temperature and humidity are included to account for the influence of weather conditions on energy consumption. The proposed model is deployed to forecast building energy consumption both before (November 2019) and during (May – October 2020) the COVID-19 pandemic and is compared with a single machine learning model. The results demonstrate that the EL model outperforms the SVR and RF methods, providing excellent prediction accuracy even during the pandemic when significant changes in energy consumption patterns occurred. The findings also highlight the effectiveness of sliding windows as input features for improving model adaptability. Additionally, the analysis reveals that temperature is more prominent than humidity for improving prediction accuracy.
Co-Authors Alfiana Defi Pujianto Edi Leksono Friansa, Koko Handre Kertha Utama, Putu Iwan Harsono Judijanto, Loso Jusatria Justin Pradipta Kusnadi, Iwan Henri Mahesa Nanda, Rezky Mahrizal Mahrizal Nashirul Haq, Irsyad Reza Fauzi Iskandar Saeful Rohman