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

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.

Monitoring Blockchain Cryptocurrency Transactions to Improve the Trustworthiness of the Fourth Industrial Revolution (Industry 4.0)

A completely new economic system is required for the era of Industry 4.0. Blockchain technology and blockchain cryptocurrencies are the best means to confront this new trustless economy. Millions of smart devices are able to complete transparent financial transactions via blockchain technology and its related cryptocurrencies. However, via blockchain technology, internet-connected devices may be hacked to mine cryptocurrencies. In this regard, monitoring the network of these blockchain-based transactions can be very useful to detect the abnormal behavior of users of these cryptocurrencies. Therefore, the trustworthiness of the transactions can be assured. In this paper, a novel procedure is proposed to monitor the network of blockchain cryptocurrency transactions. To do so, a hidden Markov multi-linear tensor model (HMTM) is utilized to model the transactions among nodes of the blockchain network. Then, a multivariate exponentially weighted moving average (MEWMA) control chart is applied to the monitoring of the latent effects. Average run length (ARL) is used to evaluate the performance of the MEWMA control chart in detecting blockchain network anomalies. The proposed procedure is applied to a real dataset of Bitcoin transactions.

Novel Condition Monitoring Method for Wind Turbines Based on the Adaptive Multivariate Control Charts and SCADA Data

A novel condition monitoring method based on the adaptive multivariate control charts and the supervisory control and data acquisition (SCADA) system is developed. Two types of control charts are adopted: one is the adaptive exponential weighted moving average (AEWMA) control chart for abnormal state detection, and the other is the multivariate exponential weighted moving average (MEWMA) control chart for anomaly location determination. Optimization procedures for these control charts are implemented to achieve minimum out-of-control average running length. Multivariate regression analysis is utilized to obtain the normal condition prediction model of wind turbine with fault-free SCADA data. After comparing the regression accuracy of several popular algorithms in the MRA, the random forest is adopted for feature selection and regression prediction. Various tests on the wind turbine with normal and abnormal states are conducted. The performance and robustness of various control charts are compared comprehensively. Compared with conventional control charts, the AEWMA control chart is more sensitive to the abnormal state and thus has a more effective anomaly identification ability and better robustness. It is shown that the MEWMA control chart combined with the out-of-limit number index can effectively locate and identify the abnormal component.

KOMPUTASI METODE MULTIVARIATE EXPONENTIALLY WEIGHTED MOVING AVERAGE (MEWMA) UNTUK PENGENDALIAN KUALITAS PROSES PRODUKSI MENGGUNAKAN GUI MATLAB (STUDI KASUS: PT. Pismatex Textile Industry Pekalongan)

Control chart is one of the effective statistical tools to overcome the problem of process quality in a production. Multivariate Exponentially Weighted Moving Average (MEWMA) control chart is an effective quality control tool in processes with more than one variable and correlated (multivariate). The MEWMA control chart has a weight value (λ) which makes this chart more sensitive in detecting small shifts process mean. The weight (λ) has values ranging from 0 to 1 ( ), where this weight will be given to each data. The MEWMA control chart in this study was used to form a control chart by the product defects percentage of grade B and grade B at PT. Pismatex Textile Industry Pekalongan. In this study, GUI Matlab was formed to assist the computational process in forming MEWMA control charts to control the quality of production at PT. Pismatex Textile Industry Pekalongan. Based on the result, the optimal weight is obtained at the weight value λ = 0.9. Keywords: Multivariate Exponentially Weighted Moving Average (MEWMA), Weight (λ), GUI Matlab, Percentage of product defects.

GRAFIK PENGENDALI MULTIVARIATE EXPONENTIALLY WEIGHTED MOVING COVARIANCE MATRIX (MEWMC) PADA DATA SAMPEL ZAT KANDUNGAN BATU BARA (Studi Kasus : PT Bukit Asam (Persero) Tbk. Tahun 2016)

The progress of industrial business in the midst of global competition increased rapidly. A businessman should have special treatment for their products to compete of market quality. The quality of product is an important factor in choosing a product or service, particularly for the costumers. In technological development, the factors of failure in the product can be minimized by Statistical Quality Control. Besides to reducing diversity in product characteristics, statistical quality control can increase business income. The data source of this research is sekunder sample data of coal products of PT Bukit Asam (Persero) Tbk. with seven variables, the variables is Total Moisture (TM), Inherent Moisture (IM), Ash Content (ASH), Volatile Matter (VM), Fixed Carbon (FC), Total Sulfur (TS), and Calorific Value (CV). The analytical method is the controlling chart of Multivariate Exponentially Weighted Moving Covariance Matrix (MEWMC) which is one of the multivariate charts that serves to detect small shift in covariance matrix and the development of Multivariate Exponentially Weighted Moving Average (MEWMA) charts. Based on the results of the analysis, the MEWMA control chart is statistically controlled with a weighting value λ=0,2 while the MEWMC chart with λ=0,2 is not controlled statistically and detected small shift in covariance matrix . In a controlled process, the capability value of multivariate process is 0,83222 < 1 which means the process is not capable.Keywords: MEWMA control chart, MEWMC control chart, Process capability analysis.

PENGENDAIAN MULTIVARIATE DENGAN DIGRAM KONTROL MEWMA ENGGUNAKAN METODE SIX SIGMA (STUDI KASUS PT FUMIRA SEMARANG TAHUN 2019)

As one of the biggest corrugation producing industries, PT Fumira Semarang is always required to fulfill customer needs by continuously improving their quality. Galvanized Steel is the raw material for the production of corrugation at PT Fumira Semarang. There are three important quality characteristics to be controlled in order that the results of galvanized steel production fit the standards to be manufactured as corrugation are waves, rust, and scratches. Six Sigma is a method for controlling quality. Six Sigma has focus on reducing defects, by standard 3,4 defects per one million opportunties. This research aims to identify the galvanized steel production process using Six Sigma method with MEWMA control chart and the capability of the process to fit the standards. Multivariate Exponentially Weighted Moving Average (MEWMA) control chart is a tool used to control multivariate process averages. The result of this research are MEWMA control chart with lambda 0.7 shows that the process is controlled statistically and The Sigma value for waves is 2,33, for rust 2,05, and for scratches 2,64. And the research reveals the galvanized steel production process has not fit to the standard because the process capabilty index is 0,2805. Keywords: Galvanized Steel, Quality Control, Six Sigma, Multivariate Exponentially Weighted Moving Average, Process Capability Analysis