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All Journal Indonesian Journal of Science and Technology Aiti: Jurnal Teknologi Informasi
Rung Ching Chen
Chaoyang University of Technology
Computer Science & IT Environmental Science

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Consumer Behavior based on APP use for Food and Beverage Consumption Hendry Hendry; Rung Ching Chen
AITI Vol 15 No 1 (2018)
Publisher : Fakultas Teknologi Informasi Universitas Kristen Satya Wacana

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (303.396 KB) | DOI: 10.24246/aiti.v15i1.1-13

Abstract

McDonalds is one of the brands that release the APP on the Smartphone, the APP is called McDonalds McDelivery APP. It suitable for the way of today’s society way of life, where people are busy and don’t want to line and queue in store to buy foods and beverages for too long. People have a freedom to choose and to order through their Smartphone. The mobile APP offers the advantages, it is easy to operate, easy to use, and doesn’t spend a lot of money. In order to understand the consumers behaviour of using APP, this study conduct the descriptive statistical analysis, variance analysis and regression analysis to detect technology acceptance model for perceived usefulness, ease of use, behaviour intention and actual of use. This study conduct the questionnaire through online google forms and obtained 109 valid questionnaires for analysis. We finds that there was no significant effect on degree of the users, and frequencies of using internet. Perceived usefulness and ease of use of behavioural intentions, behavioural intentions and actual of use had significantly difference.
Hybrid Vector Autoregression Feedforward Neural Network with Genetic Algorithm Model for Forecasting Space-Time Pollution Data Rezzy Eko Caraka; Rung Ching Chen; Hasbi Yasin; Suhartono Suhartono; Youngjo Lee; Bens Pardamean
Indonesian Journal of Science and Technology Vol 6, No 1 (2021): IJOST: VOLUME 6, ISSUE 1, April 2021
Publisher : Universitas Pendidikan Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.17509/ijost.v6i1.32732

Abstract

The exposure rate to air pollution in most urban cities is really a major concern because it results to a life-threatening consequence for human health and wellbeing. Furthermore, the accurate estimation and continuous forecasting of pollution levels is a very complicated task.  In this paper, one of the space-temporal models, a vector autoregressive (VAR) with neural network (NN) and genetic algorithm (GA) was proposed and enhanced. The VAR could tackle the issue of multivariate time series, NN for nonlinearity, and GA for parameter estimation determination. Therefore, the model could be used to make predictions, such as the information of series and location data. The applied methods were on the pollution data, including NOX, PM2.5, PM10, and SO2 in Taipei, Hsinchu, Taichung, and Kaohsiung. The metaheuristics genetic algorithm was used to enhance the proposed methods during the experiments. In conclusion, the VAR-NN-GA gives a good accuracy when metric evaluation is used. Furthermore, the methods can be used to determine the phenomena of 10 years air pollution in Taiwan.
Co-Authors Bens Pardamean Hasbi Yasin Hendry Rezzy Eko Caraka Suhartono Suhartono Youngjo Lee