xiv
DAFTAR PUSTAKA
1. Arif, F., Suryana, N., & Hussin, B. (2013). Cascade Quality Prediction Method Using Multiple PCA+ID3 for Multi-Stage Manufacturing System.
IERI Procedia, 4, 201–207. https://doi.org/10.1016/j.ieri.2013.11.029
2. Assauri. 1999. Manajemen Produksi dan Operasi, Edisi Revisi, Lembaga PenerbitFakultas Ekonomi Universitas Indonesia, Jakarta.
3. Bakhtiar, S., Tahir, S., & Hasni, R. A. (2013). Analisa Pengendalian Kualitas Dengan Menggunakan Metode Statistical Quality Control (SQC).
Malikussaleh Industrial Engineering Journal, 2(1), 29–36.
https://doi.org/2302 934X
4. Bechmann, J., Rudolph, F., Gebert, L., Schaub, J., Greulich, B., Dieterle, M.,
& Bradl, H. (2015). Process parameters impacting product quality. BMC Proceedings, 9(Suppl 9), O7. https://doi.org/10.1186/1753-6561-9-s9-o7 5. Demirbilek, E., & Gregoire, J. C. (2017). Machine learning based reduced
reference bitstream audiovisual quality prediction models for realtime communications. Proceedings - IEEE International Conference on
Multimedia and Expo, 13(2), 571–576.
https://doi.org/10.1109/ICME.2017.8019462
6. Han, J., Kamber, M., & Pei, J. (2012). Data Mining: Concepts and Techniques. In Data Mining: Concepts and Techniques.
https://doi.org/10.1016/C2009-0-61819-5
7. House, R. W., & Rado, T. (2013). An approach to artificial intelligence. In IEEE Transactions on Communication and Electronics (Vol. 83, Issue 70).
https://doi.org/10.1109/tcome.1964.6539575
8. Huber, S., Wiemer, H., Schneider, D., & Ihlenfeldt, S. (2019). DMME: Data mining methodology for engineering applications - A holistic extension to the CRISP-DM model. Procedia CIRP, 79, 403–408.
https://doi.org/10.1016/j.procir.2019.02.106
9. Juran, J. M., Godfrey, a B., Hoogstoel, R. E., & Schilling, E. G. (1999).
Juran ’ S Quality Handbook. In Training for Quality (Vol. 1, Issue 3).
xv
DAFTAR PUSTAKA
10. Kantardzic, M. (2011). Data Mining: Concepts, Models, Methods, &
Algoritms. https://doi.org/10.1007/SpringerReference_5414
11. Mauch, P. D. (2009). Quality management: Theory and application. In Quality Management: Theory and Application.
https://doi.org/10.1201/9781439813812
12. McCann, M. and Johnston, A. (2008). UCI Machine Learning Repository [http://archive.ics.uci.edu/ml/datasets/SECOM]. Irvine, CA: University of California, School of Information and Computer Science.
13. North, L., Blackmore, K., Nesbitt, K., & Mahoney, M. R. (2018). Models of coke quality prediction and the relationships to input variables: A review.
Fuel, 219(January), 446–466. https://doi.org/10.1016/j.fuel.2018.01.062 14. ReVelle, J. B. (2002). Manufacturing handbook of best practices : an
innovation, productivity, and quality focus. In St. Lucie Press/APICS series on resource management. https://doi.org/10.1201/9781420025507.ch8
15. Sauter, R. (2007). Introduction to Engineering Statistics and Six Sigma. In Technometrics (Vol. 49, Issue 2). https://doi.org/10.1198/tech.2007.s489 16. Su, T. J., Chen, Y. F., Cheng, J. C., & Chiu, C. L. (2018). An artificial neural
network approach for wafer dicing saw quality prediction. Microelectronics
Reliability, 91(October), 257–261.
https://doi.org/10.1016/j.microrel.2018.10.013
17. Thiede, S., Turetskyy, A., Kwade, A., Kara, S., & Herrmann, C. (2019). Data mining in battery production chains towards multi-criterial quality
prediction. CIRP Annals, 68(1), 463–466.
https://doi.org/10.1016/j.cirp.2019.04.066
18. Turban, E., Aronson, J., & Llang, T. (2003). Decision Support Systems and Intelligent Systems. Decision Support Systems and Intelligent Systems.
19. Wildan. (2018). Quality Prediction Model dengan Menggunakan Pendekatan Data Mining untuk Cacat Cover dan Presisi Cetak Pada Offset Printing.
Skripsi. Tidak diterbitkan. Institut Teknologi Nasional. Bandung.
xvi
DAFTAR PUSTAKA
20. Zhang, X., Kano, M., Tani, M., Mori, J., Ise, J., & Harada, K. (2020).
Control Engineering Practice Prediction and causal analysis of defects in steel products: Handling nonnegative and highly overdispersed count data.
Control Engineering Practice, 95(March 2019), 104258.
https://doi.org/10.1016/j.conengprac.2019.104258
