E3S Web Conf.
Volume 202, 2020The 5th International Conference on Energy, Environmental and Information System (ICENIS 2020)
|Number of page(s)||7|
|Section||Information System for Economic and Business|
|Published online||10 November 2020|
Business Intelligence using the K-Nearest Neighbor Algorithm to Analyze Customer Behavior in Online Crowdfunding Systems
1 Magister Program of Information System, School of Postgraduate Studies, Diponegoro University, Semarang – Indonesia
2 Department of Physics, Science and Mathematics Faculty, Diponegoro University, Semarang – Indonesia
* Corresponding author: email@example.com
Customer behavior classification can be useful to assist companies in conducting business intelligence analysis. Data mining techniques can classify customer behavior using the K-Nearest Neighbor algorithm based on the customer's life cycle consisting of prospect, responder, active and former. Data used to classify include age, gender, number of donations, donation retention and number of user visits. The calculation results from 2,114 data in the classification of each customer’s category are namely active by 1.18%, prospect by 8.99%, responder by 4.26% and former by 85.57%. System accuracy using a range of K from K = 1 to K = 20 produces that the highest accuracy is 94.3731% at a value of K = 4. The results of the training data that produce a classification of user behavior can be used as a Business Intelligence analysis that is useful for companies in determining business strategies by knowing the target of optimal market.
Key words: classification / data mining / k-nearest neighbor / business intelligence / user segmentation / customer life cycle / customer relationship management
© The Authors, published by EDP Sciences, 2020
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