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PENERAPAN METODE PENGENDALIAN KUALITAS MEWMA BERDASARKAN ARL DENGAN PENDEKATAN RANTAI MARKOV (Studi Kasus: Batik Semarang 16, Meteseh)

*Enggartya Andini  -  Departemen Statistika, Fakultas Sains dan Matematika, Universitas Diponegoro, Indonesia
Sudarno Sudarno  -  Departemen Statistika, Fakultas Sains dan Matematika, Universitas Diponegoro, Indonesia
Rita Rahmawati  -  Departemen Statistika, Fakultas Sains dan Matematika, Universitas Diponegoro, Indonesia
Open Access Copyright 2021 Jurnal Gaussian under http://creativecommons.org/licenses/by-nc-sa/4.0.

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Abstract
An industrial company requires quality control to maintain quality consistency from the production results so that it is able to compete with other companies in the world market. In the industrial sector, most processes are influenced by more than one quality characteristic. One tool that can be used to control more than one quality characteristic is the Multivariate Exponentially Weighted Moving Average (MEWMA) control chart. The graph is used to determine whether the process has been controlled or not, if the process is not yet controlled, the next analysis that can be used is to use the Average Run Length (ARL) with the Markov Chain approach. The markov chain is the chance of today's event is only influenced by yesterday's incident, in this case the chance of the incident in question is the incident in getting a sampel of data on the production process of batik cloth to get a product that is in accordance with the company standards. ARL is the average number of sample points drawn before a point indicates an uncontrollable state. In this study, 60 sample data were used which consisted of three quality characteristics, namely the length of the cloth, the width of the cloth, and the time of the fabric for the production of written batik in Batik Semarang 16 Meteseh. Based on the results and discussion that has been done, the MEWMA controller chart uses the λ weighting which is determined using trial and error. MEWMA control chart can not be said to be stable and controlled with λ = 0.6, after calculating, the value is obtained Upper Control Limit (BKA) of 11.3864 and Lower Control Limit (BKB) of 0. It is known that from 60 data samples there is a Tj2 value that comes out from the upper control limit (BKA) where the amount of 15.70871, which indicates the production process is not controlled statistically. Improvements to the MEWMA controller chart can be done based on the ARL with the Markov Chain approach. In this final project, the ARL value used is 200, the magnitude of the process shift is 1.7 and the r value is 0.28, where the value of r is a constant obtained on the r parameter graph. The optimal MEWMA control chart based on ARL with the Markov Chain approach can be said to be stable and controlled if there is no Tj2 value that is outside the upper control limit (BKA). The results of the MEWMA control chart based on the ARL with the Markov Chain approach show that the process is not statistically capable because the MCpm value is 0.516797 and the MCpmk value is 0.437807, the value indicates a process capability index value of less than 1. 

Keywords: Handmade batik, Multivariate Exponentially Weighted Moving Average (MEWMA), Average Run Length (ARL), Capability process.

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Keywords: Handmade batik, Multivariate Exponentially Weighted Moving Average (MEWMA), Average Run Length (ARL), Capability process.

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  1. Ari, W. 2011. Batik Nusantara: Makna Filosofis, Cara Pembuatan, dan Industri Batik. Yogyakarta: ANDI
  2. Arinda, A., Mustafid, dan Mukid, M.A. 2016. Penerapan Diagram Kontrol Multivariate Exponentially Weighted Moving Average (MEWMA) pada Pengendalian Karakteristik Kualitas Air. Jurnal Gaussian vol. 5, No. 1 : Hal. 31-40
  3. Assauri, S. 1980. Management Produksi. Fakultas Ekonomi Universitas Indonesia
  4. Daniel, W. W. 1989. Statistika Nonparametrik Terapan. Diterjemahkan oleh: Alex Tri Kantjono W. Jakarta: PT Gramedia
  5. Gaspersz, V. 2005. ISO 9001: 200 And Continual Improvement. Jakarta: Gramedia Pustaka Umum
  6. Handoko, T.H. 1999. Dasar-Dasar manajemen Produksi dan Operasi. Edisi Pertama. Yogyakarta: BPFE
  7. Jayanti, J. D., Wibawati. 2014. Penerapan Diagram Kontrol Mewma dan Mewmv Pada Pengendalian Kualitas Air Produksi Di Ipam Ngagel I. Jurnal Sains dan Sei Pomits vol. 3, No.2 : Hal. 314-319
  8. Johnson, R, & Wichern, D. 2007. Applied Multivariat Statistical Analysis 6th Edition. United States of America: Pearson Education
  9. Kotler, P. dan Keller, K. L. 2008. Marketing Management 13th edition. United States of America: Pearson Education
  10. Lee, M. H. dan M. B. C. Khoo. 2005. Optimal Statistical Design of a Multivariat EWMA Chart based on ARL and MRL. Journal Statistics- Simulations and Computation, 35: 831-847
  11. Lowry, C. A., Woodall, W. H., Champ, C. W., dan Rigdon, S. E. 1992. A Multivariat Exponentially Weighted Moving Average Control Chart Technometrics 34(1): 46-53
  12. Marimin, 2005. Teknik dan Aplikasi: Pengambilan Keputusan Kriteria Majemuk. Jakarta: PT Grasindo
  13. Montgomery, D. C. 2009. Introduction to Statistical Quality Control 6th Edition. United States of America: John & Wiley Sons, Inc
  14. Morrison, D. 1990. Multivariat Statistical Methods 3th Edition. New York: Mc Graw Hill Publishing Company
  15. Raissi, S. 2009. Multivariat Process capability Indices On The Presence of Priority for Quality Characteristics. Journal of Industrial Engeneering international, Vol 5, No. 9, 27-36
  16. Runger, G. C., Prabhu, S. S. 1996. A Morkov Chain Model for The Multivariat American Statistical Assosiation. 91(436): 1701-1706
  17. Suryanto. 1988. Metode Statistik Multivariat. Jakarta: P2LPTK

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