Peran Machine Learning dalam Sistem Manufaktur Otomatis: Literature Review
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Muhammad Dzaki Alfarizi
Perkembangan revolusi Industry 4.0 mendorong transformasi besar dalam sistem manufaktur melalui integrasi teknologi digital dan kecerdasan buatan. Machine Learning (ML) menjadi salah satu elemen kunci dalam otomasi industri karena kemampuannya memproses data secara adaptif untuk mendukung pengambilan keputusan otonom. Penelitian ini bertujuan untuk meninjau secara sistematis penerapan Machine Learning dalam sistem manufaktur otomatis dengan menyoroti tren, tantangan, serta arah penelitian masa depan. Metode yang digunakan adalah Systematic Literature Review (SLR) terhadap 30 artikel jurnal internasional terindeks Scopus, IEEE Xplore, dan ScienceDirect dalam rentang tahun 2020–2025. Hasil studi menunjukkan bahwa Machine Learning banyak diterapkan pada empat domain utama: predictive maintenance, process optimization, quality control, serta intelligent automation. Algoritma yang dominan digunakan meliputi supervised learning dan deep reinforcement learning, yang terbukti mampu meningkatkan efisiensi operasional hingga 35%. Namun demikian, isu terkait interpretabilitas model, keterbatasan data industri, serta integrasi manusia–AI masih menjadi tantangan utama. Studi ini mengusulkan kerangka konseptual AI–Driven Manufacturing Framework sebagai panduan pengembangan sistem manufaktur cerdas yang adaptif, transparan, dan berkelanjutan menuju era Industry 5.0.
Alshater, M. (2022). Exploring the role of artificial intelligence in enhancing academic performance: A case study of ChatGPT. SSRN 4312358.
Antoniadi, A. M., Du, Y., Guendouz, Y., Wei, L., Mazo, C., Becker, B. A., & Mooney, C. (2021). Current challenges and future opportunities for XAI in machine learning-based clinical decision support systems: A systematic review. Applied Sciences, 11(11), 5088.
Azevedo, B. F., Rocha, A. M. A., & Pereira, A. I. (2024). Hybrid approaches to optimization and machine learning methods: a systematic literature review. Machine Learning, 113(7), 4055–4097.
Bunian, S., Al-Ebrahim, M. A., & Nour, A. A. (2024). Role and applications of artificial intelligence and machine learning in manufacturing engineering: A review. Engineered Science, 29, 1088.
Creely, E. (2024). Exploring the role of generative AI in enhancing language learning: Opportunities and challenges. International Journal of Changes in Education, 1(3), 158–167.
Elahi, M., Afolaranmi, S. O., Martinez Lastra, J. L., & Perez Garcia, J. A. (2023). A comprehensive literature review of the applications of AI techniques through the lifecycle of industrial equipment. Discover Artificial Intelligence, 3(1), 43.
Faccio, M., Granata, I., Menini, A., Milanese, M., Rossato, C., Bottin, M., ... & Rosati, G. (2023). Human factors in cobot era: a review of modern production systems features. Journal of Intelligent Manufacturing, 34(1), 85–106.
Fantozzi, I. C., Santolamazza, A., Loy, G., & Schiraldi, M. M. (2025). Digital twins: Strategic guide to utilize digital twins to improve operational efficiency in Industry 4.0. Future Internet, 17(1), 41.
Fernandes, M., Corchado, J. M., & Marreiros, G. (2022). Machine learning techniques applied to mechanical fault diagnosis and fault prognosis in the context of real industrial manufacturing use-cases: a systematic literature review. Applied Intelligence, 52(12), 14246–14280.
Ferreira, C., & Gonçalves, G. (2022). Remaining Useful Life prediction and challenges: A literature review on the use of Machine Learning Methods. Journal of Manufacturing Systems, 63(May), 550–562.
Ganjare, S. A., Satao, S. M., & Narwane, V. (2024). Systematic literature review of machine learning for manufacturing supply chain. The TQM Journal, 36(8), 2236–2259.
Gheibi, O., Weyns, D., & Quin, F. (2021). Applying machine learning in self-adaptive systems: A systematic literature review. ACM Transactions on Autonomous and Adaptive Systems (TAAS), 15(3), 1–37.
Jamwal, A., Agrawal, R., Sharma, M., & Giallanza, A. (2021). Industry 4.0 technologies for manufacturing sustainability: A systematic review and future research directions. Applied Sciences, 11(12), 5725.
Kaggwa, S., Eleogu, T. F., Okonkwo, F., Farayola, O. A., Uwaoma, P. U., & Akinoso, A. (2024). AI in decision making: transforming business strategies. International Journal of Research and Scientific Innovation, 10(12), 423–444.
Kan, M. S., Tan, A. C. C., & Mathew, J. (2015). A review on prognostic techniques for non-stationary and non-linear rotating systems. Mechanical Systems and Signal Processing, 62, 1–20. https://doi.org/10.1016/j.ymssp.2015.02.016
Kang, Z., Catal, C., & Tekinerdogan, B. (2020). Machine learning applications in production lines: A systematic literature review. Computers & Industrial Engineering, 149, 106773.
Khuat, T. T., Kedziora, D. J., & Gabrys, B. (2023). The roles and modes of human interactions with automated machine learning systems: a critical review and perspectives. Foundations and Trends® in Human–Computer Interaction, 17(3–4), 195–387.
Kulkov, I., Kulkova, J., Rohrbeck, R., Menvielle, L., Kaartemo, V., & Makkonen, H. (2024). Artificial intelligence‐driven sustainable development: Examining organizational, technical, and processing approaches to achieving global goals. Sustainable Development, 32(3), 2253–2267.
Lakra, A., Gupta, S., Ranjan, R., Tripathy, S., & Singhal, D. (2022). The significance of machine learning in the manufacturing sector: an ISM approach. Logistics, 6(4), 76.
Liang, J. C., Hwang, G. J., Chen, M. R. A., & Darmawansah, D. (2023). Roles and research foci of artificial intelligence in language education: An integrated bibliographic analysis and systematic review approach. Interactive Learning Environments, 31(7), 4270–4296.
Merhi, M. I., & Harfouche, A. (2024). Enablers of artificial intelligence adoption and implementation in production systems. International Journal of Production Research, 62(15), 5457–5471.
Nagy, M., Lăzăroiu, G., & Valaskova, K. (2023). Machine intelligence and autonomous robotic technologies in the corporate context of SMEs: Deep learning and virtual simulation algorithms, cyber-physical production networks, and Industry 4.0-based manufacturing systems. Applied Sciences, 13(3), 1681.
Nayal, K., Raut, R., Priyadarshinee, P., Narkhede, B. E., Kazancoglu, Y., & Narwane, V. (2022). Exploring the role of artificial intelligence in managing agricultural supply chain risk to counter the impacts of the COVID-19 pandemic. The International Journal of Logistics Management, 33(3), 744–772.
Odonkor, B., Kaggwa, S., Uwaoma, P. U., Hassan, A. O., & Farayola, O. A. (2024). The impact of AI on accounting practices: A review: Exploring how artificial intelligence is transforming traditional accounting methods and financial reporting. World Journal of Advanced Research and Reviews, 21(1), 172–188.
Panzer, M., & Bender, B. (2022). Deep reinforcement learning in production systems: A systematic literature review. International Journal of Production Research, 60(13), 4316–4341.
Presciuttini, A., Cantini, A., Costa, F., & Portioli-Staudacher, A. (2024). Machine learning applications on IoT data in manufacturing operations and their interpretability implications: A systematic literature review. Journal of Manufacturing Systems, 74, 477–486.
Rai, R., Tiwari, M. K., Ivanov, D., & Dolgui, A. (2021). Machine learning in manufacturing and industry 4.0 applications. International Journal of Production Research, 59(16), 4773–4778.
Rathore, M. M., Shah, S. A., Shukla, D., Bentafat, E., & Bakiras, S. (2021). The role of AI, machine learning, and big data in digital twinning: A systematic literature review, challenges, and opportunities. IEEE Access, 9, 32030–32052.
Sharifani, K., & Amini, M. (2023). Machine learning and deep learning: A review of methods and applications. World Information Technology and Engineering Journal, 10(07), 3897–3904.
Sharma, A., Zhang, Z., & Rai, R. (2021). The interpretive model of manufacturing: A theoretical framework and research agenda for machine learning in manufacturing. International Journal of Production Research, 59(16), 4960–4994.
Suthahar, P., Palanikumar, K., Ponshanmugakumar, A., & Anbuchezhiyan, G. (2024, October). Machine learning advancements in machining processes: A comprehensive review for manufacturing optimization. Journal of Physics: Conference Series, 2837(1), 012102. IOP Publishing.
Vaccaro, L., Sansonetti, G., & Micarelli, A. (2021). An empirical review of automated machine learning. Computers, 10(1), 11.
Zheng, P., Xia, L., Li, C., Li, X., & Liu, B. (2021). Towards Self-X cognitive manufacturing network: An industrial knowledge graph-based multi-agent reinforcement learning approach. Journal of Manufacturing Systems, 61, 16–26.
