Prediksi Kualitas Produk Manufaktur Semikonduktor Menggunakan Machine Learning
Abstract
The semiconductor manufacturing industry faces challenges in efficient and accurate product quality control. Traditional manual inspection methods are still used but have limitations in speed and accuracy. Therefore, the application of machine learning has become a potential solution to improve the prediction of semiconductor production outcomes. This study develops a predictive model using Logistic Regression, Decision Tree, Random Forest, XGBoost, Naïve Bayes, and Support Vector Machine (SVM) with various dataset partitioning techniques such as Normal Data (90:10), Oversampling (70:30), Undersampling (80:20 & 70:30), and Principal Component Analysis (PCA) (90:10). The dataset used is sourced from the Factory Manufacturing Semiconductor Test (FMST), comprising 1,567 samples and 591 features, with product quality test labels (Pass/Fail). The results show that XGBoost and Random Forest achieved the highest accuracy (0.95) on the normal dataset (90:10), while Naïve Bayes had the lowest performance (0.23) due to its limitations in handling datasets with a large number of features. The oversampling technique improved the performance of Decision Tree and Logistic Regression but reduced the accuracy of XGBoost due to the risk of overfitting. Meanwhile, undersampling was more effective for Decision Tree but decreased SVM performance. The application of PCA improved Logistic Regression accuracy to 0.81, proving that dimensionality reduction can enhance model efficiency. Further analysis shows that feature selection using F-score can optimize model performance by eliminating redundant features. This study concludes that XGBoost and Random Forest are the best models for predicting semiconductor manufacturing product outcomes, with broad potential applications in the industry.References
[1] X. Y. Li, F. L. Liu, M. N. Zhang, M. X. Zhou, C. Wu, and X. Zhang, “A Combination of Vision- and Sensor-Based Defect Classifications in Extrusion-Based Additive Manufacturing,” J. Sensors, vol. 2023, 2023, doi: 10.1155/2023/1441936.
[2] N. V. Nguyen, A. J. W. Hum, T. Do, and T. Tran, “Semi-supervised machine learning of optical in-situ monitoring data for anomaly detection in laser powder bed fusion,” Virtual Phys. Prototyp., vol. 18, no. 1, 2023, doi: 10.1080/17452759.2022.2129396.
[3] Y. Fathy, M. Jaber, and A. Brintrup, “Learning with Imbalanced Data in Smart Manufacturing: A Comparative Analysis,” IEEE Access, vol. 9, 2021, doi: 10.1109/ACCESS.2020.3047838.
[4] P. G. Benardos and G. C. Vosniakos, “Predicting surface roughness in machining: A review,” Int. J. Mach. Tools Manuf., vol. 43, no. 8, 2003, doi: 10.1016/S0890-6955(03)00059-2.
[5] A. Fan, Y. Huang, F. Xu, and S. Bom, “Soft-Sensing Regression Model: From Sensor to Wafer Metrology Forecasting,” Sensors (Basel)., vol. 23, no. 20, 2023, doi: 10.3390/s23208363.
[6] Y. Wang, W. Cui, N. K. Vuong, Z. Chen, Y. Zhou, and M. Wu, “Feature selection and domain adaptation for cross-machine product quality prediction,” J. Intell. Manuf., vol. 34, no. 4, 2023, doi: 10.1007/s10845-021-01875-z.
[7] I. Sideris, F. Crivelli, and M. Bambach, “GPyro: uncertainty-aware temperature predictions for additive manufacturing,” J. Intell. Manuf., vol. 34, no. 1, 2023, doi: 10.1007/s10845-022-02019-7.
[8] T. T. H. Vu, T. W. Chang, and H. Kim, “Enhancing Quality Control in Battery Component Manufacturing: Deep Learning-Based Approaches for Defect Detection on Microfasteners,” Systems, vol. 12, no. 1, 2024, doi: 10.3390/systems12010024.
[9] S. Chen, H. Gao, Y. Zhang, Q. Wu, Z. Gao, and X. Zhou, “Review on residual stresses in metal additive manufacturing: formation mechanisms, parameter dependencies, prediction and control approaches,” J. Mater. Res. Technol., vol. 17, 2022, doi: 10.1016/j.jmrt.2022.02.054.
[10] S. K. Sen, G. C. Karmakar, and S. Pang, “Critical Data Detection for Dynamically Adjustable Product Quality in IIoT-Enabled Manufacturing,” IEEE Access, vol. 11, 2023, doi: 10.1109/ACCESS.2023.3276942.
[11] C. Bak, A. G. Roy, and H. Son, “Quality prediction for aluminum diecasting process based on shallow neural network and data feature selection technique,” CIRP J. Manuf. Sci. Technol., vol. 33, 2021, doi: 10.1016/j.cirpj.2021.04.001.
[12] S. Tian, Z. Zhang, X. Xie, and C. Yu, “A new approach for quality prediction and control of multistage production and manufacturing process based on Big Data analysis and Neural Networks,” Adv. Prod. Eng. Manag., vol. 17, no. 3, 2022, doi: 10.14743/apem2022.3.439.
[13] F. Psarommatis, M. Danishvar, A. Mousavi, and D. Kiritsis, “Cost-Based Decision Support System: A Dynamic Cost Estimation of Key Performance Indicators in Manufacturing,” IEEE Trans. Eng. Manag., vol. 71, 2024, doi: 10.1109/TEM.2021.3133619.
[14] H. Tercan, P. Deibert, and T. Meisen, “Continual learning of neural networks for quality prediction in production using memory aware synapses and weight transfer,” J. Intell. Manuf., vol. 33, no. 1, 2022, doi: 10.1007/s10845-021-01793-0.
[15] C. Song, Z. Wu, J. Gray, and Z. Meng, “An RFID-Powered Multisensing Fusion Industrial IoT System for Food Quality Assessment and Sensing,” IEEE Trans. Ind. Informatics, vol. 20, no. 1, 2024, doi: 10.1109/TII.2023.3262197.
[16] S. Ma, W. Ding, Y. Liu, S. Ren, and H. Yang, “Digital twin and big data-driven sustainable smart manufacturing based on information management systems for energy-intensive industries,” Appl. Energy, vol. 326, 2022, doi: 10.1016/j.apenergy.2022.119986.
[17] T. Wang, B. Hu, Y. Feng, X. Gao, C. Yang, and J. Tan, “Data Augmentation-Based Manufacturing Quality Prediction Approach in Human Cyber-Physical Systems,” J. Manuf. Sci. Eng., vol. 145, no. 12, 2023, doi: 10.1115/1.4063269.
[18] H. Zhou, K. M. Yu, Y. C. Chen, and H. P. Hsu, “A Hybrid Feature Selection Method RFSTL for Manufacturing Quality Prediction Based on a High Dimensional Imbalanced Dataset,” IEEE Access, vol. 9, 2021, doi: 10.1109/ACCESS.2021.3059298.
[19] Q. Wang, W. Jiao, P. Wang, and Y. M. Zhang, “A tutorial on deep learning-based data analytics in manufacturing through a welding case study,” J. Manuf. Process., vol. 63, 2021, doi: 10.1016/j.jmapro.2020.04.044.
[20] T. Batu, H. G. Lemu, and H. Shimels, “Application of Artificial Intelligence for Surface Roughness Prediction of Additively Manufactured Components,” 2023. doi: 10.3390/ma16186266.
[21] C. Liu, L. Le Roux, C. Körner, O. Tabaste, F. Lacan, and S. Bigot, “Digital Twin-enabled Collaborative Data Management for Metal Additive Manufacturing Systems,” J. Manuf. Syst., vol. 62, 2022, doi: 10.1016/j.jmsy.2020.05.010.
[22] A. Khdoudi, N. Barka, T. Masrour, I. El-Hassani, and C. El Mazgualdi, “Online prediction of automotive tempered glass quality using machine learning,” Int. J. Adv. Manuf. Technol., vol. 125, no. 3–4, 2023, doi: 10.1007/s00170-022-10649-7.
[23] D. Liu, Y. Du, W. Chai, C. Q. Lu, and M. Cong, “Digital Twin and Data-Driven Quality Prediction of Complex Die-Casting Manufacturing,” IEEE Trans. Ind. Informatics, vol. 18, no. 11, 2022, doi: 10.1109/TII.2022.3168309.
[24] H. Helgers et al., “Towards autonomous operation by advanced process control—process analytical technology for continuous biologics antibody manufacturing,” Processes, vol. 9, no. 1, 2021, doi: 10.3390/pr9010172.
[25] F. Xiang, L. Yang, M. Zhang, Y. Zuo, X. F. Zou, and F. Tao, “Model fusion based product quality prediction for complex manufacturing process,” Zhongguo Kexue Jishu Kexue/Scientia Sin. Technol., vol. 53, no. 7, 2023, doi: 10.1360/SST-2022-0427.
[26] H. Jung, J. Jeon, D. Choi, and A. J. Y. Park, “Application of machine learning techniques in injection molding quality prediction: Implications on sustainable manufacturing industry,” Sustain., vol. 13, no. 8, 2021, doi: 10.3390/su13084120.
[27] L. P. Zhao, B. H. Li, and Y. Y. Yao, “A novel predict-prevention quality control method of multi-stage manufacturing process towards zero defect manufacturing,” Adv. Manuf., vol. 11, no. 2, 2023, doi: 10.1007/s40436-022-00427-9.
[28] M. Papananias, T. E. McLeay, M. Mahfouf, and V. Kadirkamanathan, “A Bayesian information fusion approach for end product quality estimation using machine learning and on-machine probing,” J. Manuf. Process., vol. 76, 2022, doi: 10.1016/j.jmapro.2022.01.020.
[29] C. Ruiz, D. Jafari, V. Venkata, T. H. J. Vaneker, W. Ya, and Q. Huang, “Prediction and Control of Product Shape Quality for Wire and Arc Additive Manufacturing,” J. Manuf. Sci. Eng., vol. 144, no. 11, 2022, doi: 10.1115/1.4054721.
[30] C. H. Chien, P. Y. Chen, A. J. C. Trappey, and C. V. Trappey, “Intelligent Supply Chain Management Modules Enabling Advanced Manufacturing for the Electric-Mechanical Equipment Industry,” Complexity, vol. 2022, 2022, doi: 10.1155/2022/8221706.
[31] J. Gim and L. S. Turng, “A review of current advancements in high surface quality injection molding: Measurement, influencing factors, prediction, and control,” 2022. doi: 10.1016/j.polymertesting.2022.107718.
[32] N. Leberruyer, J. Bruch, M. Ahlskog, and S. Afshar, “Toward Zero Defect Manufacturing with the support of Artificial Intelligence—Insights from an industrial application,” Comput. Ind., vol. 147, 2023, doi: 10.1016/j.compind.2023.103877.
[33] J. Takalo-Mattila, M. Heiskanen, V. Kyllonen, L. Maatta, and A. Bogdanoff, “Explainable Steel Quality Prediction System Based on Gradient Boosting Decision Trees,” IEEE Access, vol. 10, 2022, doi: 10.1109/ACCESS.2022.3185607.
[34] X. Li, Z. Huang, and W. Ning, “Intelligent manufacturing quality prediction model and evaluation system based on big data machine learning,” Comput. Electr. Eng., vol. 111, 2023, doi: 10.1016/j.compeleceng.2023.108904.
[35] V. Azamfirei, F. Psarommatis, and Y. Lagrosen, “Application of automation for in-line quality inspection, a zero-defect manufacturing approach,” 2023. doi: 10.1016/j.jmsy.2022.12.010.
[36] S. Nannapaneni, S. Mahadevan, A. Dubey, and Y. T. T. Lee, “Online monitoring and control of a cyber-physical manufacturing process under uncertainty,” J. Intell. Manuf., vol. 32, no. 5, 2021, doi: 10.1007/s10845-020-01609-7.
[37] M. Papananias, T. E. McLeay, M. Mahfouf, and V. Kadirkamanathan, “A probabilistic framework for product health monitoring in multistage manufacturing using Unsupervised Artificial Neural Networks and Gaussian Processes,” Proc. Inst. Mech. Eng. Part B J. Eng. Manuf., vol. 237, no. 9, 2023, doi: 10.1177/09544054221136510.
[2] N. V. Nguyen, A. J. W. Hum, T. Do, and T. Tran, “Semi-supervised machine learning of optical in-situ monitoring data for anomaly detection in laser powder bed fusion,” Virtual Phys. Prototyp., vol. 18, no. 1, 2023, doi: 10.1080/17452759.2022.2129396.
[3] Y. Fathy, M. Jaber, and A. Brintrup, “Learning with Imbalanced Data in Smart Manufacturing: A Comparative Analysis,” IEEE Access, vol. 9, 2021, doi: 10.1109/ACCESS.2020.3047838.
[4] P. G. Benardos and G. C. Vosniakos, “Predicting surface roughness in machining: A review,” Int. J. Mach. Tools Manuf., vol. 43, no. 8, 2003, doi: 10.1016/S0890-6955(03)00059-2.
[5] A. Fan, Y. Huang, F. Xu, and S. Bom, “Soft-Sensing Regression Model: From Sensor to Wafer Metrology Forecasting,” Sensors (Basel)., vol. 23, no. 20, 2023, doi: 10.3390/s23208363.
[6] Y. Wang, W. Cui, N. K. Vuong, Z. Chen, Y. Zhou, and M. Wu, “Feature selection and domain adaptation for cross-machine product quality prediction,” J. Intell. Manuf., vol. 34, no. 4, 2023, doi: 10.1007/s10845-021-01875-z.
[7] I. Sideris, F. Crivelli, and M. Bambach, “GPyro: uncertainty-aware temperature predictions for additive manufacturing,” J. Intell. Manuf., vol. 34, no. 1, 2023, doi: 10.1007/s10845-022-02019-7.
[8] T. T. H. Vu, T. W. Chang, and H. Kim, “Enhancing Quality Control in Battery Component Manufacturing: Deep Learning-Based Approaches for Defect Detection on Microfasteners,” Systems, vol. 12, no. 1, 2024, doi: 10.3390/systems12010024.
[9] S. Chen, H. Gao, Y. Zhang, Q. Wu, Z. Gao, and X. Zhou, “Review on residual stresses in metal additive manufacturing: formation mechanisms, parameter dependencies, prediction and control approaches,” J. Mater. Res. Technol., vol. 17, 2022, doi: 10.1016/j.jmrt.2022.02.054.
[10] S. K. Sen, G. C. Karmakar, and S. Pang, “Critical Data Detection for Dynamically Adjustable Product Quality in IIoT-Enabled Manufacturing,” IEEE Access, vol. 11, 2023, doi: 10.1109/ACCESS.2023.3276942.
[11] C. Bak, A. G. Roy, and H. Son, “Quality prediction for aluminum diecasting process based on shallow neural network and data feature selection technique,” CIRP J. Manuf. Sci. Technol., vol. 33, 2021, doi: 10.1016/j.cirpj.2021.04.001.
[12] S. Tian, Z. Zhang, X. Xie, and C. Yu, “A new approach for quality prediction and control of multistage production and manufacturing process based on Big Data analysis and Neural Networks,” Adv. Prod. Eng. Manag., vol. 17, no. 3, 2022, doi: 10.14743/apem2022.3.439.
[13] F. Psarommatis, M. Danishvar, A. Mousavi, and D. Kiritsis, “Cost-Based Decision Support System: A Dynamic Cost Estimation of Key Performance Indicators in Manufacturing,” IEEE Trans. Eng. Manag., vol. 71, 2024, doi: 10.1109/TEM.2021.3133619.
[14] H. Tercan, P. Deibert, and T. Meisen, “Continual learning of neural networks for quality prediction in production using memory aware synapses and weight transfer,” J. Intell. Manuf., vol. 33, no. 1, 2022, doi: 10.1007/s10845-021-01793-0.
[15] C. Song, Z. Wu, J. Gray, and Z. Meng, “An RFID-Powered Multisensing Fusion Industrial IoT System for Food Quality Assessment and Sensing,” IEEE Trans. Ind. Informatics, vol. 20, no. 1, 2024, doi: 10.1109/TII.2023.3262197.
[16] S. Ma, W. Ding, Y. Liu, S. Ren, and H. Yang, “Digital twin and big data-driven sustainable smart manufacturing based on information management systems for energy-intensive industries,” Appl. Energy, vol. 326, 2022, doi: 10.1016/j.apenergy.2022.119986.
[17] T. Wang, B. Hu, Y. Feng, X. Gao, C. Yang, and J. Tan, “Data Augmentation-Based Manufacturing Quality Prediction Approach in Human Cyber-Physical Systems,” J. Manuf. Sci. Eng., vol. 145, no. 12, 2023, doi: 10.1115/1.4063269.
[18] H. Zhou, K. M. Yu, Y. C. Chen, and H. P. Hsu, “A Hybrid Feature Selection Method RFSTL for Manufacturing Quality Prediction Based on a High Dimensional Imbalanced Dataset,” IEEE Access, vol. 9, 2021, doi: 10.1109/ACCESS.2021.3059298.
[19] Q. Wang, W. Jiao, P. Wang, and Y. M. Zhang, “A tutorial on deep learning-based data analytics in manufacturing through a welding case study,” J. Manuf. Process., vol. 63, 2021, doi: 10.1016/j.jmapro.2020.04.044.
[20] T. Batu, H. G. Lemu, and H. Shimels, “Application of Artificial Intelligence for Surface Roughness Prediction of Additively Manufactured Components,” 2023. doi: 10.3390/ma16186266.
[21] C. Liu, L. Le Roux, C. Körner, O. Tabaste, F. Lacan, and S. Bigot, “Digital Twin-enabled Collaborative Data Management for Metal Additive Manufacturing Systems,” J. Manuf. Syst., vol. 62, 2022, doi: 10.1016/j.jmsy.2020.05.010.
[22] A. Khdoudi, N. Barka, T. Masrour, I. El-Hassani, and C. El Mazgualdi, “Online prediction of automotive tempered glass quality using machine learning,” Int. J. Adv. Manuf. Technol., vol. 125, no. 3–4, 2023, doi: 10.1007/s00170-022-10649-7.
[23] D. Liu, Y. Du, W. Chai, C. Q. Lu, and M. Cong, “Digital Twin and Data-Driven Quality Prediction of Complex Die-Casting Manufacturing,” IEEE Trans. Ind. Informatics, vol. 18, no. 11, 2022, doi: 10.1109/TII.2022.3168309.
[24] H. Helgers et al., “Towards autonomous operation by advanced process control—process analytical technology for continuous biologics antibody manufacturing,” Processes, vol. 9, no. 1, 2021, doi: 10.3390/pr9010172.
[25] F. Xiang, L. Yang, M. Zhang, Y. Zuo, X. F. Zou, and F. Tao, “Model fusion based product quality prediction for complex manufacturing process,” Zhongguo Kexue Jishu Kexue/Scientia Sin. Technol., vol. 53, no. 7, 2023, doi: 10.1360/SST-2022-0427.
[26] H. Jung, J. Jeon, D. Choi, and A. J. Y. Park, “Application of machine learning techniques in injection molding quality prediction: Implications on sustainable manufacturing industry,” Sustain., vol. 13, no. 8, 2021, doi: 10.3390/su13084120.
[27] L. P. Zhao, B. H. Li, and Y. Y. Yao, “A novel predict-prevention quality control method of multi-stage manufacturing process towards zero defect manufacturing,” Adv. Manuf., vol. 11, no. 2, 2023, doi: 10.1007/s40436-022-00427-9.
[28] M. Papananias, T. E. McLeay, M. Mahfouf, and V. Kadirkamanathan, “A Bayesian information fusion approach for end product quality estimation using machine learning and on-machine probing,” J. Manuf. Process., vol. 76, 2022, doi: 10.1016/j.jmapro.2022.01.020.
[29] C. Ruiz, D. Jafari, V. Venkata, T. H. J. Vaneker, W. Ya, and Q. Huang, “Prediction and Control of Product Shape Quality for Wire and Arc Additive Manufacturing,” J. Manuf. Sci. Eng., vol. 144, no. 11, 2022, doi: 10.1115/1.4054721.
[30] C. H. Chien, P. Y. Chen, A. J. C. Trappey, and C. V. Trappey, “Intelligent Supply Chain Management Modules Enabling Advanced Manufacturing for the Electric-Mechanical Equipment Industry,” Complexity, vol. 2022, 2022, doi: 10.1155/2022/8221706.
[31] J. Gim and L. S. Turng, “A review of current advancements in high surface quality injection molding: Measurement, influencing factors, prediction, and control,” 2022. doi: 10.1016/j.polymertesting.2022.107718.
[32] N. Leberruyer, J. Bruch, M. Ahlskog, and S. Afshar, “Toward Zero Defect Manufacturing with the support of Artificial Intelligence—Insights from an industrial application,” Comput. Ind., vol. 147, 2023, doi: 10.1016/j.compind.2023.103877.
[33] J. Takalo-Mattila, M. Heiskanen, V. Kyllonen, L. Maatta, and A. Bogdanoff, “Explainable Steel Quality Prediction System Based on Gradient Boosting Decision Trees,” IEEE Access, vol. 10, 2022, doi: 10.1109/ACCESS.2022.3185607.
[34] X. Li, Z. Huang, and W. Ning, “Intelligent manufacturing quality prediction model and evaluation system based on big data machine learning,” Comput. Electr. Eng., vol. 111, 2023, doi: 10.1016/j.compeleceng.2023.108904.
[35] V. Azamfirei, F. Psarommatis, and Y. Lagrosen, “Application of automation for in-line quality inspection, a zero-defect manufacturing approach,” 2023. doi: 10.1016/j.jmsy.2022.12.010.
[36] S. Nannapaneni, S. Mahadevan, A. Dubey, and Y. T. T. Lee, “Online monitoring and control of a cyber-physical manufacturing process under uncertainty,” J. Intell. Manuf., vol. 32, no. 5, 2021, doi: 10.1007/s10845-020-01609-7.
[37] M. Papananias, T. E. McLeay, M. Mahfouf, and V. Kadirkamanathan, “A probabilistic framework for product health monitoring in multistage manufacturing using Unsupervised Artificial Neural Networks and Gaussian Processes,” Proc. Inst. Mech. Eng. Part B J. Eng. Manuf., vol. 237, no. 9, 2023, doi: 10.1177/09544054221136510.
Published
2025-06-18
How to Cite
DARUSMAN, Darusman; ABBAS, Aries; FIRMANTO, Angga Dwi.
Prediksi Kualitas Produk Manufaktur Semikonduktor Menggunakan Machine Learning.
INFORMATICS FOR EDUCATORS AND PROFESSIONAL : Journal of Informatics, [S.l.], v. 10, n. 1, p. 13 - 24, june 2025.
ISSN 2548-3412.
Available at: <https://ejournal-binainsani.ac.id/index.php/ITBI/article/view/3348>. Date accessed: 05 july 2026.
doi: https://doi.org/10.51211/itbi.v10i1.3348.
