Open AccessPAMELA-CL: Partition Membership Based on Lazy Classifier for Neuromarketing
Author(s) -
Intan Nurma Yulita,
Asep Sholahuddin,
Emilliano,
Dessy Novita
Publication year - 2020
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/1577/1/012050
Subject(s) - neuromarketing , classifier (uml) , computer science , electroencephalography , artificial intelligence , pattern recognition (psychology) , machine learning , psychology , psychiatry , neuroscience
Neuromarketing is one of the business strategies that has developed lately. The strategy studies the effect of product promotion on the brain. If the impact analysis on the brain is successfully carried out, the company can find a good and effective marketing strategy for potential customers. This study used electroencephalography (EEG) as data. 30 respondents were involved in data recording. The final goal in this study was to classify the emotions of respondents to the video simulations that were displayed. The video contains a number of products. There were 14 electrodes used for the recording process. Then the EEG data were preprocessed, and its characteristics were extracted before being classified. This study proposed PAMELA-CL for the classification. The classifier was compared with lazy classifier. The result was obtained that this new classifier has higher accuracy than the lazy classifier. The difference in accuracy between the two was above 25%. All experiments involving PAMELA-CL had accuracy above 85%. It showed that this new classifier could be recommended in solving neuromarketing problems, especially for the dataset used in this study.