## 1 Introduction  
The aim of a robust measurement system is to design a measurement system to get an optimized output by taking variation into consideration. Variation is the main keyword for robust engineering [1]. Robust engineering is described using parameter design in which a system is insensitive to variation. Thus, the development of a robust measurement system is important to obtain this objective. A robust measurement system reduces the variation that is contributed by the noise factor [1]. The noise factor cannot be eliminated, but the effect can be reduced by choosing the proper level for control factors.  
This is done in parameter design that is used to improve the quality without removing the cause of variation and to make the product robust against the noise factors [1].  
Measurement and instrumentation are the keys to enable the technology of science and other practical activities [2]. They reflect a very wide variety of equipment and techniques for a diversity of applications. [PERSON] [3,4] emphasized that measurement science should address the whole range of applications of measurement and provide a universal framework of concepts and principles for the applications of measurement. [PERSON] et al. [5] presents different variation countermeasure compared to [PERSON]'s [6] approach in metrology. [PERSON] and [PERSON] [7] also presented on different look at dynamic parameter design and robust design measurement systems. A performance measure on signal-response relationship is one of the contradictions.  
[PERSON]'s strongest contribution to the design of experiments is to focus not only on minimizing the variability of the response, but also on optimizing the response. Thus, control factor and noise factor are separated in inner array and outer array, respectively. Most statistical methods concentrate on modeling, predicting, and controlling the average response, and this type of experimental design and analysis is crucial. Control factor is placed in inner array of orthogonal array [1]. Control factors affect process variability as measured by the signal-to-noise ratio (SNR). Control factor has three categories. The first category is the factor in which different settings give different average responses. These factors are said to be active but have no interaction with noise factors. The second category is a group of control factors that are active by virtue of having an interaction with noise factors [8]. They have the dispersion effect. Finally, the third category is the factors that have no effect on the response, called non-active. This kind of factor can be set at their cheapest or most convenient levels.  
It is very important to have a robust measurement system explaining from the method of data measurement up to its optimization with minimum variation in the system. Most published papers discussed the test method used in some case studies [9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21] while others discussed the measurement concepts and theories [8, 10, 22, 23, 24, 25]. However, less research has been done to connect both aspects in a single study [9]. Thus, the main study in this paper connects the test method used for measurement and the theories behind the setting up of its measurement which are described.  
A case of T-peel strength measurement is used to depict the robust measurement system for parameter design of Taguchi Method. This paper aims to provide the procedure on determining optimum conditions to analyze variability and optimization when designing a measurement system, and to establish a process flow to achieve high-quality experimental design for a robust measurement system [26]. Therefore, a systematic measurement system is developed that results in a robust product and process by using parameter design. The finding of this paper reveals the quality result or optimum condition in a measurement system which consists of the function, noise, control parameter, and optimization elements stated as F-N-C-O.