DMME-Driven Product Quality Prediction for Semiconductor Manufacturing

Defective products in manufacturing can be reduced by accurately predicting quality outcomes based on process parameters. This study proposes a quality prediction framework for semiconductor manufacturing using the Data Mining Methodology for Engineering Applications (DMME). This study extends DMME with domain-specific preprocessing and demonstrates its superiority on the SECOM dataset compared to other classifiers. Experimental results show that the Random Forest algorithm achieved the highest performance, with 92.99% accuracy and an F-measure of 0.9637, confirming the effectiveness of the proposed approach. These findings highlight the potential of structured, engineering-oriented data mining to improve product quality and support informed decision-making in complex manufacturing environments.

@article{Afifah2025,
  title     = {DMME-Driven Product Quality Prediction for Semiconductor Manufacturing},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {16},
  number    = {8},
  year      = {2025},
  publisher = {The Science and Information Organization},
  author    = {Alif Ulfa Afifah and Angga Prastiyan and Fahmi Arif and Fadillah Ramadhan},
  doi       = {10.14569/IJACSA.2025.0160865},
  url       = {https://doi.org/10.14569/IJACSA.2025.0160865}
}