WOROROMI, JONATHAN K. (2016) PENGEMBANGAN DIAGRAM KONTROL MEWMA BERBASIS MODEL UNTUK PENGAMATAN TAK RANDOM. Doctoral thesis, Institut Teknologi Sepuluh Nopember.
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Abstract
Proses produksi pada umumnya melibatkan lebih dari satu karakteristik kualitas (yang saling berkorelasi
atau berautokorelasi). Karakteristik ini dikenali sebagai pola sistematik atau pola data tak random. Pola
data tak random dalam proses multivariat tidak hanya berpengaruh pada penentuan sensitivitas batas
kontrol yang bersesuaian dengan jenis diagram kontrol multivariat, tetapi juga pada kesalahan
pendeteksian outlier atau assignable cause lainnya dalam proses multivariat. Diagram kontrol
Multivariate Exponential Weighted Moving Average (MEWMA) konvensional pada umumnya mampu
bekerja pada shift-shift kecil sampai moderat, dan tidak optimal bila bekerja pada suatu data yang bersifat
tak random dengan pola sistematik. Sehingga diperlukan pengembangan suatu diagram kontrol MEWMA
yang juga mampu bekerja optimal pada pola data tak random. Penelitian dalam disertasi ini bertujuan
untuk mengembangkan suatu diagram kontrol MEWMA baru mampu meningkatkan sensitivitas diagram
kontrol MEWMA konvensional terhadap efek pola data tak random.
Diagram kontrol MEWMA yang dikembangkan ini merupakan diagram kontrol MEWMA baru berbasis
residual dari model Vektor Auto-Regresif (VAR) yang mengadopsi Genetic Algorithm (GA). GA tidak
hanya memperbaiki kinerja diagram kontrol MEWMA dalam mendeteksi outlier pada kenaikan-kenaikan
shift kecil dan moderat, tetapi juga bermanfaat dalam mendeteksi outlier pada kenaikan-kenaikan shift
besar dalam proses vektor mean.
Hasil evaluasi terhadap kinerja diagram kontrol MEWMA berbasis model melalui GA dan penaksir Least
Squared (LS) menunjukkan perbaikkan kinerja diagram kontrol dalam penanganan efek non normalitas
yang berasal dari observasi tak random. Secara analitik, ditunjukkan bahwa efek shift dalam vektor mean
dan variabilitas yang terjadi dalam suatu proses dapat dikontrol dan dievaluasi melalui matriks koefisien.
Secara numerik, matriks koefisien
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Generally, a process of production involves more than one quality of characteristics (correlated or
autocorrelated). These qualities of characteristics are known as systematic patterns or non random data
patterns. Impact of a non random data patterns in the multivariable process is not affected by sensitivity of
an associate control limits determination only, but also by fault detection of outlier or from other
assignable causes in multivariate process. A conventional multivariate exponential weighed moving
average (MEWMA) works on small shifts to moderate shifts level, but it does not optimally work on a
non randomized data patterns with a systematic patterns. Therefore, it has been required to develop a new
MEWMA control charts that can work optimally on non random data. The aim of the research in this
dissertation is to develop a new MEWMA control charts in order to modify the conventional MEWMA
due to its sensitivities to effect of the non random data patterns.
This new developed MEWMA control chart is MEWMA that based on residuals from a Vector Auto-
Regressive (VAR) model with Genetic Algorithm (GA) as an unbiased estimator. The GA estimator was
used to improve not only the MEWMA control charts for outlier detection on small to moderate shift
increaments, but also large shift increaments in mean vector processes.
Due to the result of MEWMA control charts based model evaluations by using of GA and Least Squared
(LS) estimator, the performances of the control charts were improved according to non-normality effects
from non random observation. Analytically, the occurance of the shift effects on mean vector and
variability in a process were controllable and could be evaluated by a coefficient matrices. As the main
parameter of VAR(p) models,
| Item Type: | Thesis (Doctoral) |
|---|---|
| Additional Information: | RDSt 519.86 Wor p |
| Uncontrolled Keywords: | Diagram kontrol MEWMA, Vektor Auto-Regresif (VAR), Pola data tak random, dan Genetic Algorithm (GA) |
| Subjects: | Q Science > QA Mathematics Q Science > QA Mathematics > QA75 Electronic computers. Computer science. EDP |
| Divisions: | Faculty of Mathematics and Science > Statistics > 49001-(S3) PhD Thesis |
| Depositing User: | Mr. Tondo Indra Nyata |
| Date Deposited: | 03 Jan 2017 02:28 |
| Last Modified: | 27 Dec 2018 08:37 |
| URI: | http://repository.its.ac.id/id/eprint/1244 |
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