BibTex Citation Data :
@article{J.Gauss26621, author = {Mifta Sany and Rukun Santoso and Arief Hakim}, title = {PENERAPAN PENGENDALIAN KUALITAS DENGAN MEWMA DAN FUNGSI DENSITAS KERNEL MULTIVARIAT (Studi Kasus: PT Sukorejo Indah Textile Kab. Batang)}, journal = {Jurnal Gaussian}, volume = {8}, number = {1}, year = {2019}, keywords = {Multivariate Exponentially Weighted Moving Average (MEWMA) control chart, multivariate kernel control chart, process capability.}, abstract = { In an era of industrial revolution 4.0, technology is increasingly sophisticated, requiring companies to be more creative. Product quality control is an effort to minimize the defective products produced by the company. The production of weaving sarongs at PT SUKORINTEX pays attention to the accuracy of the length and width of the sarong to conform to the standards set by the company. To find out the quality of woven sarong products at PT SUKORINTEX, analysis was performed using Multivariate Exponentially Weighted Moving Average (MEWMA) control charts and multivariate kernel control charts. The research variable was the characteristics of the X sarongs which is reflected in 2 variates, namely the average length and average width. Based on the results and discussion that has been done, the MEWMA control chart used a weighting λ which is determined using trial and error. MEWMA control charts can be said to be stable and controlled by λ = 0.1, Upper Control Limit (UCL) of 14.62943, and Lower Control Limit (LCL) of 0. Multivariate kernel control chart were declared uncontrolled with α = 0.1 and level = 0.06130611 because there were data that was outside the contour. Chart improvement was done by trial and error and obtained a controlled chart results at α = 0.01 and a level value of 0.03125701. Based on this case study, the quality control of the average length and width of WADIMOR woven sarong types 30 STR with MEWMA is better than the multivariate kernel density, because MEWMA is controlled and stable in controlling product quality. The results of the MEWMA control chart show a capable process because more than 1 process capability index value is obtained. Keywords : Multivariate Exponentially Weighted Moving Average (MEWMA) control chart, multivariate kernel control chart, process capability.}, issn = {2339-2541}, pages = {46--57} doi = {10.14710/j.gauss.8.1.46-57}, url = {https://ejournal3.undip.ac.id/index.php/gaussian/article/view/26621} }
Refworks Citation Data :
In an era of industrial revolution 4.0, technology is increasingly sophisticated, requiring companies to be more creative. Product quality control is an effort to minimize the defective products produced by the company. The production of weaving sarongs at PT SUKORINTEX pays attention to the accuracy of the length and width of the sarong to conform to the standards set by the company. To find out the quality of woven sarong products at PT SUKORINTEX, analysis was performed using Multivariate Exponentially Weighted Moving Average (MEWMA) control charts and multivariate kernel control charts. The research variable was the characteristics of the X sarongs which is reflected in 2 variates, namely the average length and average width. Based on the results and discussion that has been done, the MEWMA control chart used a weighting λ which is determined using trial and error. MEWMA control charts can be said to be stable and controlled by λ = 0.1, Upper Control Limit (UCL) of 14.62943, and Lower Control Limit (LCL) of 0. Multivariate kernel control chart were declared uncontrolled with α = 0.1 and level = 0.06130611 because there were data that was outside the contour. Chart improvement was done by trial and error and obtained a controlled chart results at α = 0.01 and a level value of 0.03125701. Based on this case study, the quality control of the average length and width of WADIMOR woven sarong types 30 STR with MEWMA is better than the multivariate kernel density, because MEWMA is controlled and stable in controlling product quality. The results of the MEWMA control chart show a capable process because more than 1 process capability index value is obtained.
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