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  3. Vol. 6, No. 4, November 2021
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Vol. 6, No. 4, November 2021

Issue Published : Nov 30, 2021
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This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.

Study of Neuromarketing: Visual Influence with Decision Making on Impulse Buying

https://doi.org/10.22219/kinetik.v6i4.1334
Rifat Januar
Telkom University
Hilman Fauzi
Telkom University
Maya Ariyanti
Telkom University
Faradisya Heris
Telkom University

Corresponding Author(s) : Hilman Fauzi

hilmanfauzitsp@telkomuniversity.ac.id

Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control, Vol. 6, No. 4, November 2021
Article Published : Nov 30, 2021

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Abstract

Marketing trends have been increasing in the last few decades. Products need good branding and the right marketing strategy. Various marketing methods have been widely done, and one of them is with the study of neuroscience, especially neuromarketing. Neuromarketing is used to seek the influence of marketing stimuli on consumers and objective data through advances in neurology by utilizing human senses such as restraint, smell, taste, and touch. Measurements of neuromarketing responses to the brain can use electroencephalography signals (EEG). Measurement is done with the visual stimulus of consumers when making decisions. To analyze consumer interests, the majority still using qualitative methods, but it is still considered less effective due to many uncertain factors. In this study, neuromarketing responses were measured to the human brain using (EEG) signal analysis. Data collection was conducted on 11 respondents with a stimulus in the form of different product colors and was affected by changes in light intensity. For pre-processing used bandpass filters to get beta signals in the absence of noise. Then the data will be processed using Fast Fourier Transform (FFT) and energy extraction as characteristic extraction and classification of Support Vector Machines (SVM) in the signal pattern recognition process. The results of testing the best feature combination parameters showed an accuracy value of 72% with a combination of magnitude and phase features. By using the range of phase feature values obtained an accuracy of 67% for signal pattern recognition respondents.

Keywords

Electroencephalography Support Vector Machine Fast Fourier Transform Neuromarketing
Januar, R., Fauzi, H., Ariyanti, M., & Heris, F. (2021). Study of Neuromarketing: Visual Influence with Decision Making on Impulse Buying. Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control, 6(4). https://doi.org/10.22219/kinetik.v6i4.1334
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References
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References


A. Reghukumar, “Sense and Sensitivity in Architecture – The Use of Five Senses in Space making,” Int. Res. J. Archit. Plan., vol. 4, no. 3, hal. 97–101, 2019.

H. Zamani, A. Abas, dan M. K. MAmin, “Eye tracking application on emotion analysis for marketing strategy,” J. Telecommun. Electron. Comput. Eng., vol. 8, no. 11, hal. 87–91, 2016.

J. L. Burton, J. Gollins, L. E. McNeely, dan D. M. Walls, “Revisiting the relationship between ad frequency and purchase intentions how affect and cognition mediate outcomes at different levels of advertising frequency,” J. Advert. Res., vol. 59, no. 1, hal. 27–39, 2019. https://doi.org/10.2501/JAR-2018-031

B. Gonchigjav, “Results of neuromarketing study of visual attention and emotions of buyers in retail store environment,” Proc. Mong. Acad. Sci., vol. 60, no. 01, hal. 52–64, 2020. https://doi.org/10.5564/pmas.v60i1.1337

J. Saura, A. Reyes-Menendez, N. Matos, M. Correia, dan P. Palos-Sanche, “Consumer Behavior in the Digital Age,” J. Spat. Organ. Dyn., vol. 8, no. 3, hal. 190–196, 2020.

D. Khurniawan, M. Dimyati, dan D. Wulandari, “Influence of Sensory Branding Elements On Consumer Decision-Making Behavior In Buying Aqua With Neuromarketing Approach In Jember Regency,” e-Journal Ekon. Bisnis dan Akunt., vol. 4, no. 1, hal. 44, 2017. https://doi.org/10.19184/ejeba.v4i1.4572

P. Widyastuti, “Does visual merchandising, store atmosphere and private label product influence impulse buying? Evidence in Jakarta,” J. Bus. Retail Manag. Res., vol. 12, no. 3, hal. 140–148, 2018. https://doi.org/10.24052/JBRMR/V12IS03/ART-12

L. Sun, J. Liang, Z. Liu, dan Y. Zhang, “Research on the Atmosphere and Emotionality of Apparel Stores Under LED Lighting Environment,” in Lecture Notes in Electrical Engineering, 2019, vol. 543, hal. 44–49. http://dx.doi.org/10.1007/978-981-13-3663-8_7

L. Alvino, E. Constantinides, dan M. Franco, “Towards a Better Understanding of Consumer Behavior: Marginal Utility as a Parameter in Neuromarketing Research,” Int. J. Mark. Stud., vol. 10, no. 1, hal. 90, 2018. https://doi.org/10.5539/ijms.v10n1p90

N. Kalkova, O. Yarosh, E. Mitina, dan V. Khokhlov, “Asymmetry of Visual Perception When Choosing Products: Methods and Algorithms of Neuromarketing,” Int. J. Innov. Technol. Explor. Eng., vol. 9, no. 8, hal. 179–187, 2020. https://doi.org/10.35940/ijitee.H6256.069820

B. Glova dan I. Mudryk, “Application of deep learning in neuromarketing studies of the effects of unconscious reactions on consumer behavior,” Proc. 2020 IEEE 3rd Int. Conf. Data Stream Min. Process. DSMP 2020, hal. 337–340, 2020. https://doi.org/10.1109/DSMP47368.2020.9204192

A. A. Mansor dan S. M. Isa, “Fundamentals of neuromarketing: What is it all about?,” Neurosci. Res. Notes, vol. 3, no. 4, hal. 22–28, 2020. https://doi.org/10.31117/neuroscirn.v3i4.58

D. Approach, M. Nilashi, E. Yadegaridehkordi, S. Samad, dan A. Mardani, “Decision to Adopt Neuromarketing Techniques for Sustainable Product Marketing: A Fuzzy Decision-Making Approach,” 2020. https://doi.org/10.3390/sym12020305

K. Bočková, J. Škrabánková, dan M. Hanák, “Theory and practice of neuromarketing: Analyzing human behavior in relation to markets,” Emerg. Sci. J., vol. 5, no. 1, hal. 44–56, 2021. https://doi.org/10.28991/esj-2021-01256

R. Indrawan, E. C. Djamal, dan R. Ilyas, “Neuropsychological Identification of Video Ads In Real-Time Using Fast Fourier Transform and Support Vector Machine,” Semin. Nas. Apl. Teknol. Inf., hal. 6–10, 2017.

I. Wijayanto, “Identification Of Electroencephalogram (EEG) Signal Conditions Exposed To 1800 Mhz 4g Lte Device Signal Radiation Using Learning Vector Quantization (LVQ),” TEKTRIKA - J. Penelit. dan Pengemb. Telekomun. Kendali, Komputer, Elektr. dan Elektron., vol. 1, no. 2, hal. 138–143, 2019. https://doi.org/10.25124/tektrika.v1i2.1746

I. Lusiawati, “Brain development and optimization of human resources,” Tedc, vol. 11, no. 2, hal. 162–171, 2017.

J. Pratama, E. C. Djamal, F. Renaldi, dan U. Jenderal Achmad Yani Jl Terusan Sudirman, “Identification of Product Attention Level Based on EEG Signals as Neuro Marketing,” Semin. Nas. Apl. Teknol. Inf., no. August, hal. 2016, 2016.

G. R. Iyer, M. Blut, S. H. Xiao, dan D. Grewal, “Impulse buying : a meta-analytic review,” hal. 384–404, 2020. https://doi.org/10.1007/s11747-019-00670-w

H. S. Azman, M. K. M. Amin, dan S. Wibirama, “Exploring the subconscious decision making in neuromarketing research using eye tracking technique,” J. Adv. Manuf. Technol., vol. 13, no. Special Issue 2, hal. 35–44, 2019.

G. Plonka, D. Potts, G. Steidl, dan M. Tasche, Numerical Fourier Analysis. 2018.

Z. Wei, C. Wu, X. Wang, A. Supratak, P. Wang, dan Y. Guo, “Using support vector machine on EEG for advertisement impact assessment,” Front. Neurosci., vol. 12, no. MAR, 2018. https://doi.org/10.3389/fnins.2018.00076

J. Zamani dan A. B. Naieni, “Best Feature Extraction and Classification Algorithms for EEG Signals in.pdf,” vol. 7, no. 3, hal. 186–191, 2020. https://doi.org/10.18502/fbt.v7i3.4621

K. Gibert, M. Sànchez-Marrè, dan J. Izquierdo, “A survey on pre-processing techniques: Relevant issues in the context of environmental data mining,” AI Commun., vol. 29, no. 6, hal. 627–663, 2016. https://doi.org/10.3233/AIC-160710

B. Richhariya dan M. Tanveer, “EEG signal classification using universum support vector machine,” Expert Syst. Appl., vol. 106, hal. 169–182, 2018. https://doi.org/10.1016/j.eswa.2018.03.053

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