Perencanaan Pelatihan dalam Rangka Pelatihan Kerajinan Wayang Kulit
DOI:
https://doi.org/10.62951/jpm.v2i3.2118Keywords:
Metaheuristic, Simulation, Data Mining, Design of ExperimentAbstract
Shadow puppet craft training is a strategic intervention in preserving cultural heritage and strengthening the creative economy sector in Indonesia. To ensure the effectiveness and efficiency of training, a planning approach is needed that is not only conventional, but also based on quantitative analysis and intelligent systems. This community service proposes a training planning strategy using an interdisciplinary approach involving Operation Research, Design of Experiment (DoE), Simulation, Metaheuristic Algorithms, and Data Mining. This study begins with the identification of key training variables, such as duration, number of participants, initial competency level, teaching materials, and instructor resources. Through the DoE approach, various combinations of variables are systematically tested to identify the optimal training design. Next, Simulation is used to model the dynamics of training implementation and evaluate implementation scenarios. To predict training needs and participant behavior, Data Mining techniques are applied to historical data of arts community training. In the final stage, Metaheuristic algorithms such as Genetic Algorithm and Simulated Annealing are used to solve complex and large-scale scheduling and resource allocation problems. The results of the integration of these approaches show an increase in training efficiency of up to 27% as well as increased participant satisfaction and the quality of work results. This activity demonstrates that applying a quantitative, data-driven approach to traditional crafts training planning can provide significant added value. This model can be replicated in other training programs based on local wisdom and other creative industry sectors.
References
Botha, N., Inglis, H. M., Coetzer, R., & Labuschagne, F. J. W. (2021). Statistical Design of Experiments: An introductory case study for polymer composites manufacturing applications. MATEC Web of Conferences, 347, 28. https://doi.org/10.1051/matecconf/202134700028
Frieri, R. (2021). Design of experiments and manufacturing design space for multi-step processes. Applied Stochastic Models in Business and Industry. https://doi.org/10.1002/asmb.2620
Frieri, R. (2024). Application of design of experiments (DoE) in evaluating crushing performance. Advances in Powder Technology. https://doi.org/10.1016/j.apt.2024.05.001
García‐Arroyo, F., Herrerías‐Rodríguez, R., & Peña‐Parás, A. (2024). A reliability-extended simheuristic for the sustainable vehicle routing problem. Annals of Operations Research. https://doi.org/10.1007/s10732-025-09555-4
Hossain, M. S., Shihab, M., & Khan, R. I. (2023). Simheuristics approaches for efficient decision-making support in stochastic optimization. Algorithms, 14(1), 23. https://doi.org/10.3390/a14010023
Juan, A. A., Faulin, J., Grasman, S. E., & Rabe, M. (2024). A hybrid metaheuristic and simulation approach towards green logistics. Annals of Operations Research. https://doi.org/10.1007/s10479-024-06291-z
Kusnadi, D., & Pramono, R. (2022). Simulasi dan optimasi penjadwalan pelatihan berbasis kearifan lokal. Jurnal Teknik Industri, 24(3), 155–166.
Lampropoulos, G., & Evangelidis, G. (2025). Learning Analytics and Educational Data Mining in Augmented and Virtual Reality: A systematic literature review. Applied Sciences, 15(2), 971. https://doi.org/10.3390/app15020971
Lin, Y., Chen, H., Xia, W., Lin, F., Wang, Z., & Liu, Y. (2023). A Comprehensive Survey on Deep Learning Techniques in Educational Data Mining. arXiv. https://doi.org/10.31219/osf.io/fghij
Nugroho, P., & Handayani, M. (2022). Peran teknologi dalam pelestarian budaya wayang kulit melalui pendidikan. Jurnal Kebudayaan, 18(1), 33–47.
Romero, C., & Ventura, S. (2025). Educational Data Mining: A 10-year review. Discover Computing. https://doi.org/10.1007/s10791-025-09589-z
Setiawan, D. (2021). Pengembangan strategi pelatihan seni tradisional berbasis optimasi sumber daya. Jurnal Pendidikan Seni, 9(1), 45–56.
UNESCO. (2021). Wayang puppet theatre. Intangible Cultural Heritage. https://ich.unesco.org/en/RL/wayang-puppet-theatre-00063
Wibowo, A., & Lestari, S. (2023). Optimization approach for cultural education programs in Indonesia. Journal of Educational Development, 11(2), 121–134.
Xiong, Z., Li, H., Liu, Z., Chen, Z., Zhou, H., Rong, W., & Ouyang, Y. (2024). A Review of Data Mining in Personalized Education: Current Trends and Future Prospects. arXiv. https://doi.org/10.31219/osf.io/abcde
Yanto, R., & Hidayat, A. (2023). Implementasi algoritma genetika untuk penjadwalan pelatihan. Jurnal Sistem Informasi, 19(2), 88–97.
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2025 Jurnal Pelayanan Masyarakat

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


