Community Purchase Decision Modeling in Bali with Non-Linier Methods
DOI:
https://doi.org/10.30812/matrik.v21i3.1740Keywords:
Buying decision, Covid-19, Feed Forward Neural Network, Support Vector Regression, Structural Ecuation Modeling, Partial Least SquareAbstract
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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[2] J. Hair Jr,W. Black, B. Babin, and R. Anderson, Multivariate Data Analysis (7th ed). United States: Prentice Hall International:
New York, 2010.
[3] H. Liu and Y. Zhou, “The marketization of rural collective construction land in northeastern China: The mechanism exploration,â€
Sustainability (Switzerland), vol. 13, no. 1, pp. 1–17, 2021.
[4] K. Gbongli, Y. Xu, K. M. Amedjonekou, and L. Kov´acs, “Evaluation and Classification of Mobile Financial Services Sustainability
Using Structural Equation Modeling and Multiple Criteria Decision-Making Methods,†Sustainability, vol. 12, no. 4, p.
1288, feb 2020.
[5] S. Gunn, Support Vector Machine for Classification and Regression. University of Southampton.: University of Southampton.,
1988.
[6] G. Astudillo, R. Carrasco, C. Fern´andez-Campusano, and M. Chac´on, “Copper Price Prediction Using Support Vector Regression
Technique,†Applied Sciences, vol. 10, no. 19, p. 6648, sep 2020.
[7] M. El Amine Ben Seghier, B. Keshtegar, K. F. Tee, T. Zayed, R. Abbassi, and N. T. Trung, “Prediction of maximum pitting
corrosion depth in oil and gas pipelines,†Engineering Failure Analysis, vol. 112, p. 104505, may 2020.
[8] B. Handaga and H. Asy’ari, “Kombinasi Algoritma Cuckoo-Search dan Levenberg- Marquardt (CS-LM) pada Proses Pelatihan
Artificial Neural Network (ANN),†in Simposium Nasional RAPI XI FT UMS. Surakarta: Universitas Muhammadiyah
Surakarta, 2012, pp. 1–8.
[9] R. Latumeten, Y. A. Lesnussa, and F. Y. Rumlawang, “Penggunaan Structural Equation Modeling (Sem) untuk Menganalisis
Faktor yang Mempengaruhi Loyalitas Nasabah (Studi Kasus : PT Bank Negara Indonesia (BNI) KCU Ambon),†Sainmatika:
Jurnal Ilmiah Matematika dan Ilmu Pengetahuan Alam, vol. 15, no. 2, p. 76, 2018.
[10] U. Mahdiyah, M. I. Irawan, and E. M. Imah, “Study Comparison Backpropogation, Support Vector Machine, and Extreme
Learning Machine for Bioinformatics Data,†Journal of Computer Science and Information, vol. 8, no. 1, pp. 53–59, mar 2015.
[11] N. P. N. Hendayanti, I. K. P. Suniantara, and M. Nurhidayati, “Penerapan Support Vector Regression (Svr) Dalam Memprediksi
Jumlah Kunjungan Wisatawan Domestik Ke Bali,†Jurnal Varian, vol. 3, no. 1, pp. 43–50, 2019.
[12] N. P. N. Hendayanti and M. Nurhidayati, “Perbandingan Metode Seasonal Autoregressive Integrated Moving Average
(SARIMA) dengan Support Vector Regression (SVR) dalam Memprediksi Jumlah Kunjungan Wisatawan Mancanegara ke
Bali,†Jurnal Varian, vol. 3, no. 2, pp. 149–162, 2020.
[13] S. Rachmania, Nathasya; Darwis, “Pemodelan Survival Menggunakan Support Vector Regression (SVR) pada Data Vibrasi
Bearing,†in Fakultas Matematika dan Ilmu Pengetahuan Alam, 2020.
[14] M. R. Ramadhan, S. B. Waluya, and M. Kharis, “Pemodelan Arimax, FFNN, dan Arimax-FFNN untuk Peramalan Produksi
Padi Provinsi Jawa Tengah,†Unnes Journal of Mathematics, vol. 10, no. 2, pp. 12–21, 2021.
[15] H. Santoso and D. Murdianto, “Analisis Pengenalan Bendera Negara Rumpun Melayu Menggunakan Metode Feed Forward
Neural Network,†Jurnal Teknologi dan Informasi, vol. 10, no. 2, pp. 144–152, 2020.
[16] E. Sugiono, Andini Nurwulandari, and Christiani Junita, “The Influence of Marketing Mix Variables on Purchasing Decisions
and Its Impact on Post-Purchase Customer Satisfaction of Royal Garden Residence Bali Housing (Study at PT Properti Bali
Benoa),†Open Access Indonesia Journal of Social Sciences, vol. 4, no. 1, pp. 157–172, 2021.
[17] W. A. Basudani, “Level 4 PPKM Implementation Effect of Food and Beverage Purchase Decisions On Street Vendors in Jakarta
Region,†Jurnal Sosial dan Sains, vol. 3, no. 2, pp. 56–65, 2022.
[18] E. Hutabarat and A. P. Tua, “The Influence of Local Food Brand Image on Consumer Purchase Decision During Covid-19
Pandemic,†Journal of Management and Leadership, vol. 4, no. 2, pp. 1–12, 2021.
[19] W. W. S. Wei, Time Series Analysis: Univariate and Multivariate Methods Second Edition. USA: Pearson Education, 2006.
[20] N. Mardiana and A. Faqih, “Model SEM-PLS Terbaik untuk Evaluasi Pembelajaran Matematika Diskrit dengan LMS,â€
BAREKENG: Jurnal Ilmu Matematika dan Terapan, vol. 13, no. 3, 2019.
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Published
2022-07-31
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Section
Articles
How to Cite
[1]
N. P. N. Hendayanti, M. Nurhidayati, S. Soraya, and H. R. Perwira Negara, “Community Purchase Decision Modeling in Bali with Non-Linier Methods”, MATRIK, vol. 21, no. 3, pp. 721–734, Jul. 2022, doi: 10.30812/matrik.v21i3.1740.
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