The Implementation of Natural Language Processing in Manufacturing and Service Industry through Skateboard Monitoring Device

Tia Tanjung; Riza Muhida; Muhammad Riza; Ari Kurniawan; Erlangga Erlangga; Nofian Pratama; Fenty Ariani; Taqwan Thamrin; Robby Yuli Endra; Ayu Kartika Puspa; Wiwin Susanty; Ahmad Cucus

doi:10.4028/p-piUWy7

Paper Titles

Identification of Endophytic Fungi on the Leaves of Jeruju (Acanthus ilicifolius) which are Potential as Antibacteria
p.9

DNA Mutation of Rc Gene and the Phenotype of Aleurone and Pericarp of Local Red Rice from South Sumatera
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Safe Avoidance Region Detection for Unmanned Aerial Vehicle Using Cues from Expansion of Feature Points
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Development of the Obstacle Avoider of Fish Robot
p.31

The Implementation of Natural Language Processing in Manufacturing and Service Industry through Skateboard Monitoring Device
p.43

Review of Deep Learning Using Convolutional Neural Network Model
p.49

Risk Based Inspection Analysis on Primary and Secondary Cooling Pipe of the TRIGA 2000 Reactor
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A Multifaceted Assessments of Occupational Hazards in a Building Construction Site: Insights from a Case Study
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Settlement of Land Disputes between PT Selatan Agro Makmur Lestari (PT SAML) and Residents of Tirta Mulya Village, Air Sugihan District, Ogan Komering Ilir (OKI) Regency, South Sumatra Province, Indonesia
p.77

HomeEngineering HeadwayEngineering Headway Vol. 3The Implementation of Natural Language Processing...

The Implementation of Natural Language Processing in Manufacturing and Service Industry through Skateboard Monitoring Device

Abstract:

The application of artificial intelligence (AI) in the manufacturing and service industries has witnessed rapid advancements in recent years. One prominent aspect is the utilization of Natural Language Processing (NLP) to facilitate human-machine interactions and enhance efficiency and user experience. This journal explores the implementation of NLP in the context of the manufacturing and service industry, focusing on the skateboard monitoring device. We demonstrate how NLP can improve analysis, prediction, and personalization in skateboard production, providing users with a more interactive and informative experience.

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Periodical:

Engineering Headway (Volume 3)

Pages:

43-48

DOI:

https://doi.org/10.4028/p-piUWy7

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Cite this paper

Online since:

March 2024

Authors:

Tia Tanjung*, Riza Muhida, Muhammad Riza, Ari Kurniawan, Erlangga Erlangga, Nofian Pratama, Fenty Ariani, Taqwan Thamrin, Robby Yuli Endra, Ayu Kartika Puspa, Wiwin Susanty, Ahmad Cucus

Keywords:

AI, Manufacturing Industry, Monitoring Device, Natural Language Processing, Service Industry, Skateboard

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* - Corresponding Author

References

[1] Jurafsky, D., & Martin, J. H. (2019). "Speech and Language Processing" (3rd ed.). Pearson. This book is a highly comprehensive source on Natural Language Processing, covering various topics such as text analysis, syntax, language modeling, and machine translation.

Google Scholar

[2] Manning, C. D., Raghavan, P., & Schütze, H. (2020). "Introduction to Information Retrieval." Cambridge University Press. Although this book focuses on the field of information retrieval, it contains many concepts and techniques relevant to Natural Language Processing, especially in the context of indexing and text search.

DOI: 10.1007/s10791-009-9096-x

Google Scholar

[3] Goldberg, Y. (2020). "Neural Network Methods for Natural Language Processing." Synthesis Lectures on Human Language Technologies, 10(1), 1-309. This book discusses neural network-based approaches in Natural Language Processing, including topics like word embeddings, language modeling, and text understanding.

DOI: 10.1007/978-3-031-02165-7_11

Google Scholar

[4] Manning, C. D., & Schütze, H. (2020). "Foundations of Statistical Natural Language Processing." MIT Press. This book provides a comprehensive overview of statistically-based Natural Language Processing, including techniques such as probabilistic language modeling and syntactic analysis.

Google Scholar

[5] Bird, S., Klein, E., & Loper, E. (2021). "Natural Language Processing with Python." O'Reilly Media. This book offers a practical approach to Natural Language Processing using the Python programming language and popular libraries like NLTK (Natural Language Toolkit).

DOI: 10.1007/s10579-010-9124-x

Google Scholar

[6] Socher, R., Manning, C. D., & Ng, A. Y. (2021). "Recursive Deep Models for Semantic Compositionality Over a Sentiment Treebank." Proceedings of the Conference on Empirical Methods in Natural Language Processing (EMNLP), 1631-1642. This article discusses the use of recursive deep neural models in sentiment analysis, which is one of the important applications of NLP.

DOI: 10.3115/v1/d14-1220

Google Scholar