Community Purchase Decision Modeling in Bali with Non-Linier Methods

  • Ni Putu Nanik Hendayanti ITB STIKOM Bali
  • Maulida Nurhidayati IAIN Ponorogo
  • Siti Soraya
  • Habib Ratu Perwira Negara Universitas Bumigora
Keywords: Buying decision, Covid-19, Feed Forward Neural Network, Support Vector Regression, Structural Ecuation Modeling, Partial Least Square

Abstract

The Covid-19 pandemic has resulted in all activities having to be carried out by implementing physical distancing or social distancing in accordance with health protocols for mutual safety. The government encourages people to do more activities at home, including shopping. Consumer perception of purchasing goods online is a process of evaluating various alternatives and choosing one alternative to purchase goods using internet media. The government appealed to the public to take advantage of online shopping to minimize the spread of Covid-19. This indicates that there are factors that influence consumer perceptions of purchasing goods online during the Covid-19 pandemic. The purpose of this study was to examine the effect of perceived convenience, perceived benefits, perceived trustworthiness, and product quality on people’s purchasing decisions in Bali using the Structural Equation Modeling-Partial Least Square (SEM-PLS) approach, Support Vector Regression (SVR), and Feed Forward Neural Network (FFNN). Based on the results of the tests carried out, the SEM-PLS model is able to produce a model with an R2 value of 72.7% with a MAPE of 337.37, an SVR model of 65.88% with a MAPE of 219.56 and a FFNN model of 97.28% with a MAPE of 90.22. Based on the resulting R2 and MAPE values, the FFNN model gives the highest results compared to other models.

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Published
2022-07-31
How to Cite
Hendayanti, N. P., Nurhidayati, M., Soraya, S., & Perwira Negara, H. (2022). Community Purchase Decision Modeling in Bali with Non-Linier Methods. MATRIK : Jurnal Manajemen, Teknik Informatika Dan Rekayasa Komputer, 21(3), 721-734. https://doi.org/https://doi.org/10.30812/matrik.v21i3.1740
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Articles

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