Human Computer Interaction, How It Satisfaction Human As a Users

When we discuss Human Computer Interaction, most people will discuss it from a functional point of view but few people pay attention to user satisfaction orientation. When we discuss ergonomics that focuses on user comfort. We cannot be separated from an Asian figure namely Prof. Nagamachi from Japan. Since the 1970s he has introduced ergonomics and how it comfort to users. But if we further discuss the correlation between ergonomics and computer science, as well as how comfortable a computer application is when used by a user. Then we will not be separated from a researcher from Malaysia, namely Dr. Anita Moch Lukman. He has successfully introduced the science of ergonomics to the technique of making applications that are comfortable to use.

.
.
.
.

Human Computer Interaction and Kansei Engineering

Kansei Engineering is a framework that was first introduced and created by Prof. Nagamachi. This framework was created to be a measure of how ergonomics can be a signal of satisfaction from users. The steps taken in this method are as same as step in human computer interaction system are:

1. Determine the type of design that will be used as a reference and target users. This can be determined randomly or according to the target user to be targeted. Then this type of design will follow from the target user.
2. Gather words that can represent a human feeling about the type of design. This process can be done by looking for words that are appropriate and can describe human feelings about something. Of course this must be consulted with linguists.
3. Make a Semantic Differential to calculate the ranking of Kansei’s words.
4. Gather the desired design sample. Start a search on the Google search engine design type that you want to be a suitable sample. It not deviate from the target specified in the first step.
5. Classify the design sample type or category. From the search results of the design type of the google search engine. Then do the classification of types and categories of design samples.
6. Evaluate design samples or specimens based on Semantic Differential from the words kansei. By way of calculating the tendency of each respondent. Who saw the sample by transforming the tendency of the words kansei.

.
.
.
.

Human Computer Interaction and Multivariate Statistic

1. Perform data analysis using multivariate statistical methods. In this stage there are several steps and statistical formulas used. Namely; cronbach’s alpha to measure the minimum feasibility of a data if each specimen is worth a minimum of 0.7 After that the specimen data is considered feasible. Then measure the strength of the relationship between each Kansei Word using Coeffisient Correlation Analysis (CCA), after that do reduce the variable words Kansei of small value that will weaken the results of research with the Principal Component Analysis (PCA) process. Therefore to strengthen the research results do Factor Analysis with the aim of describing the covariance relationship between some underlying but not observed variables.
2. Mapping data from the analysis of design elements. In other words from the results of the PCA and the FA then do the Partial Least Square process. However at this stage the result of PCA and FA analysis is the Kansei Words concept which will be translated into design elements using Partial Least Square (PLS) analysis. Above all the main purpose of this analysis process is to find out the design elements that greatly affect the emotions of respondents. The results of this process will be a reference for design element recommendations in accordance with the respondent’s emotional goals.
3. Designing a new sketch of the results of the mapping of the data analysis results conducted by experts in the design of the display.
4. Propose a New Design Sketch in the hope that the new design can better represent the user’s wishes.

Scientific Journal

You can find more complete steps about Kansei Engineering in a scientific journal wrote by Ginanjar, A., Sari, WP., Herlina., 2018., Inovasi Alternatif Perancangan Tampilan Website Berdasarkan Analisis Faktor Multivariat Sebagai Bagian Dari Implementasi Kansei Engineering., Media Jurnal Informatika., 10 (2). Universitas Suryakancana. in this link you’ll find more complete action for this research.