Literature Review The Impact of Machine Learning in Modern Industries

Authors

  • Ade Ryan Pratama Universitas Pamulang
  • Farmin Wabula Universitas Pamulang
  • Haekal Ilmandry Universitas Pamulang
  • Maria Laura Isabela Universitas Pamulang
  • Mugi Raharjo Universitas Pamulang
  • Ronald Sianipar Universitas Pamulang

DOI:

https://doi.org/10.59603/niantanasikka.v3i1.680

Keywords:

Machine Learning, Industry, Big Data, Challenges, Opportunities, Machine LearningIndustry

Abstract

Machine learning has revolutionized various industrial sectors by giving systems the ability to learn and make decisions based on data without the need for explicit programming. This technology plays an increasingly important role in the era of big data, helping to solve various business and operational challenges. This study aims to explore the literature related to the application of machine learning in various industrial fields, identify advantages, challenges, and direct future research. The research method in the form of a literature review was conducted by collecting and analyzing 15 international journals in the last 10 years that are relevant to this topic. The results of the study show that machine learning has a significant impact in the fields of health, finance, and transportation. Techniques such as deep learning and reinforcement learning have expanded the scope of implementation, although challenges such as interpretability, data bias, and computational requirements remain major obstacles. With continued development, machine learning opens up great opportunities for industrial transformation, although it must still pay attention to ethical and desirable aspects.

References

BARC. (n.d.). Machine learning in Industry 4.0: Five use cases. Retrieved from https://barc.com

Bishop, C. M. (2006). Pattern recognition and machine learning. Springer.

Columbia University. (n.d.). Top machine learning applications by industry. Retrieved from https://bootcamp.cvn.columbia.edu

Goodfellow, I., Bengio, Y., & Courville, A. (2016). Deep learning. MIT Press.

IBM. (n.d.). Machine learning examples, applications & use cases. Retrieved from https://www.ibm.com

LeCun, Y., Bengio, Y., & Hinton, G. (2015). Deep learning. Nature, 521(7553), 436–444.

McKinsey & Company. (2021). The state of AI in 2021. Retrieved from https://www.mckinsey.com

MobiDev. (n.d.). Machine learning in manufacturing: Industrial use cases in 2025. Retrieved from https://mobidev.biz

Pixelplex. (n.d.). 7 applications of machine learning in manufacturing in 2023. Retrieved from https://pixelplex.io

Silver, D., et al. (2016). Mastering the game of Go with deep neural networks and tree search. Nature, 529(7587), 484–489.

Simplilearn. (n.d.). Top 10 machine learning applications and examples in 2024. Retrieved from https://www.simplilearn.com

Tableau. (n.d.). Real-world examples of machine learning (ML). Retrieved from https://www.tableau.com

TechTarget. (n.d.). Top 12 machine learning use cases and business applications. Retrieved from https://www.techtarget.com

University of Virginia Data Science. (n.d.). Which industries benefit from machine learning? Retrieved from https://stage.datascience.virginia.edu

Wikipedia. (n.d.). Artificial intelligence in industry. Retrieved from https://en.wikipedia.org

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Published

2025-01-07

How to Cite

Ade Ryan Pratama, Farmin Wabula, Haekal Ilmandry, Maria Laura Isabela, Mugi Raharjo, & Ronald Sianipar. (2025). Literature Review The Impact of Machine Learning in Modern Industries. Nian Tana Sikka : Jurnal ilmiah Mahasiswa, 3(1), 177–182. https://doi.org/10.59603/niantanasikka.v3i1.680

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