Business Intelligence Using N-Beats And Rnn Methods End Influence On Decision Making In The Flexible Packaging Manufacturing

Eko Wahyudi, Tjong Wan Sen

Abstract


Today's complex decision-making solutions for intelligent manufacturing depend on the ability to be able to model a manufacturing system realistically, valid and consistent data integrated easily and in a timely manner, able to solve problems efficiently with computational effort to obtain optimal production and product quality optimizations continuously. When an organization uses a data-driven approach, it means that it makes strategic decisions based on data collection, analysis, and interpretations or insights. The purpose of this research is to analyze the business intelligence approach in optimizing print machines by speed, material and time. in this research, using the N-Beats is a deep neural architecture based on backward and forward residual links and a very deep stack of fully-connected layers and Recurrent Neural Networks (RNN). The novelty of this research is increasing machine speed using new insights by combining two deep learning methods. Observing and retrieving raw data from the printing machine process with sensors data for use and ensuring the justification of the addition of new methods. The result is expected to be able to provide new insights that can increase engine speed, the data based decision making provides businesses with the capabilities to generate real time insights and predictions to optimize their performance and provide confidence in decision making that are fast, precise and better.

Keywords


Business Intelligence; Recurrent Neural Networks; N-Beats; Decision-Making; Deep Learning; Insights

Full Text:

PDF

References


S. Mudgal et al., “Deep learning for entity matching: A design space exploration,” Proc. 2018 Int. Conf. Manag. Data, pp. 19–34, 2018.

H. Apaydin, H. Feizi, M. T. Sattari, M. S. Colak, S. Shamshirband, and K. W. Chau, “Comparative analysis of recurrent neural network architectures for reservoir inflow forecasting,” vol. 12, no. 5, p. 1500, 2020.

E. J. C. Lopes and R. A. da Costa Bianchi, “Short-term prediction for Ethereum with Deep Neural Networks,” Work. Artif. Intell. Financ., pp. 1–12, 2022.

B. N. Oreshkin, D. Carpov, N. Chapados, and Y. Bengio, “N-BEATS: Neural basis expansion analysis for interpretable time series forecasting,” arXiv Prepr. arXiv, vol. 1905.10437, 2019.

D. . Adytia, D. Saepudin, S. R. Pudjaprasetya, S. Husrin, and A. Sopaheluwakan, “A deep learning approach for wave forecasting based on a spatially correlated wind feature, with a case thesis in the Java Sea, Indonesia,” Fluids, vol. 7, no. 1, p. 39, 2022.

B. Puszkarski, K. Hryniów, and G. Sarwas, “N-beats for heart dysfunction classification,” 2021 Comput. Cardiol., vol. 48, pp. 1–4, 2021.

R. Islam, K. Desai, and J. Quarles, “Towards Forecasting the Onset of Cybersickness by Fusing Physiological, Head-tracking and Eye-tracking with Multimodal Deep Fusion Network”.

A. Sherstinsky, “Fundamentals of recurrent neural network (RNN) and long short-term memory (LSTM) network,” Phys. D Nonlinear Phenom., vol. 404, p. 132306, 2020.

N. Reski, “Tingkat Minat Belajar Siswa Kelas Ix Smpn 11 Kota Sungai Penuh,” J. Inov. Penelit., vol. 1, no. 11, pp. 2485–2490, 2021.

V. Singhal, N. Neeraj, J. Mathew, and M. Agarwal, “Fusion of Wavelet Decomposition and N-BEATS for improved Stock Market Forecasting,” 2022.

I. H. Sarker, “Deep learning: a comprehensive overview on techniques, taxonomy, applications and research directions,” SN Comput. Sci., vol. 2, no. 6, pp. 1–20, 2021.

A. Sbrana, A. L. D. Rossi, and M. C. Naldi, “N-BEATS-RNN: deep learning for time series forecasting,” 2020 19th IEEE Int. Conf. Mach. Learn. Appl., pp. 765–768, 2020.

E. Stevenson, V. Rodríguez-Fernández, E. Minisci, and D. Camacho Fernandez, “A deep learning approach to space weather proxy forecasting for orbital prediction,” 2020.

Y. Fu, D. Wu, and B. Boulet, “Reinforcement learning based dynamic model combination for time series forecasting,” 2022.

S. Zeng, F. Graf, C. Hofer, and R. Kwitt, “Topological attention for time series forecasting,” Adv. Neural Inf. Process. Syst., vol. 34, pp. 24871–24882, 2021.

N. Widmark, “Short-term electricity consumption forecasting using deep learning and external variables,” 2022.

D. Putz, M. Gumhalter, and H. Auer, “A novel approach to multi-horizon wind power forecasting based on deep neural architecture,” Renew. Energy, vol. 178, pp. 494–505, 2021.

L. Alzubaidi et al., “Review of deep learning: Concepts, CNN architectures, challenges, applications, future directions,” J. big Data, vol. 8, no. 1, pp. 1–74, 2021.

A. Bulatov, “Forecasting Bitcoin Prices Using N-BEATS Deep Learning Architecture,” 2020.

E. Stevenson, V. Rodriguez-Fernandez, E. Minisci, and D. Camacho, “A deep learning approach to solar radio flux forecasting,” Acta Astronaut., vol. 193, pp. 595–606, 2022.

V. Le Guen and N. Thome, “Deep Time Series Forecasting with Shape and Temporal Criteria,” IEEE Trans. Pattern Anal. Mach. Intell., 2022.




DOI: https://doi.org/10.31326/jisa.v6i1.1626

Refbacks

  • There are currently no refbacks.


Copyright (c) 2023 Eko Wahyudi,Tjong Wan Sen

Creative Commons License
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.


JOURNAL IDENTITY

Journal Name: JISA (Jurnal Informatika dan Sains)
e-ISSN: 2614-8404, p-ISSN: 2776-3234
Publisher: Program Studi Teknik Informatika Universitas Trilogi
Publication Schedule: June and December 
Language: Indonesia & English
APC: The Journal Charges Fees for Publishing 
IndexingEBSCODOAJGoogle ScholarArsip Relawan Jurnal IndonesiaDirectory of Research Journals Indexing, Index Copernicus International, PKP IndexScience and Technology Index (SINTA, S4) , Garuda Index
OAI addresshttp://trilogi.ac.id/journal/ks/index.php/JISA/oai
Contactjisa@trilogi.ac.id
Sponsored by: DOI – Digital Object Identifier Crossref, Universitas Trilogi

In Collaboration With: Indonesian Artificial Intelligent Ecosystem(IAIE), Relawan Jurnal IndonesiaJurnal Teknologi dan Sistem Komputer (JTSiskom)

 

 


JISA (Jurnal Informatika dan Sains) is Published by Program Studi Teknik Informatika, Universitas Trilogi under Creative Commons Attribution-ShareAlike 4.0 International License.