Comparison of Machine Learning Algorithms Using WEKA and Sci-Kit Learn in Classifying Online Shopper Intention

Yefta Christian(1*),


(1) Universitas Internasional Batam
(*) Corresponding Author

Abstract


The growth of online stores nowadays is very rapid. This is supported by faster and better internet infrastructure. The increasing growth of online stores makes the competition more difficult in this business field. It is necessary for online stores to have a website or an application that is able to measure and classify consumers’ spending intentions, so that the consumers will have eyes on things on the sites and applications to make purchases eventually. Classification of online shoppers’ intentions can be done by using several algorithms, such as Naïve Bayes, Multi-Layer Perceptron, Support Vector Machine, Random Forest and J48 Decision Trees. In this case, the comparison of algorithms is done with two tools, WEKA and Sci-Kit Learn by comparing the values of F1-Score, accuracy, Kappa Statistic and mean absolute error. There is a difference between the test results using WEKA and Sci-Kit Learn on the Support Vector Machine algorithm. Based on this research, the Random Forest algorithm is the most appropriate algorithm to be used as an algorithm for classifying online shoppers’ intentions.


Keywords


j48 decision trees, machine learning, multi layer perceptron, niat belanja, naïve bayes, random forest, sci-kit learn, support vector machine, weka

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References


Christian, Y. 2018. Comparison Of Data Mining Algorithm Models In Phising Websites Classification. Prosiding ICTE 2018 & ICITED 2018

Hidayatulloh, T. 2014. Kajian Komparasi Penerapan Algoritma Support Vector Machine (SVM) dan Multilayer Perceptron (MLP) Dalam Prediksi Indeks Saham Sektor Perbankan : Studi Kasus Saham LQ45 IDX Bank BCA. Prosiding SNIT 2014

Kaur, G., & Chhabra, A. et. Al. 2014. Improved J48 Classification Algorithm for The Prediction of Diabetes. In:Internasional Journal of Computer Applications.

Khan, M. A., & Khan, S. et. Al. 2018. Service Convenience and Post-Purchase Behaviour of Online Buyers: An Empirical Study. In:Innovation in the High-Tech Economy. Springer.

Mittal, P., & Gill, N. S. (2014). A Comparative Analysis Of Classification Techniques On Medical Data Sets. IJRET: International Journal of Research in Engineering and Technology, 3 (6): 454-460.

Patil, T. R., Sherekar, S.S. (2013). Performance Analysis of Naïve Bayes and J48 Classification Algorithm for Data Classification. International Journal of Computer Science and Applications, 6 (2): 256-261.

Sakar, C. O., Polat, S. O., Katircloglu, M. et. Al. 2018. Real-Time Prediction of Online Shopper’ Purchasing Intention Using Multilayer Perceptron and LSTM Recurrent Neural Networks. In:Neural Computing and Applications. Springer.

Salim, M., (2016), Klasifikasi Tutupan Lahan Perkotaan Menggunakan Naïve Bayes Berbasis Forward Selection, Jurnal Teknosains, 10 (2): 165-182.

Sakar. C. O., Kastro Y., 2018. Online Shoppers Purchasing Intention Dataset. Diunduh di https://archive.ics.uci.edu/ml/datasets/Online+Shoppers+Purchasing+Intention+Dataset

Shwartz, S. S., & David, S. B. 2002. Understanding Machine Learning From Theory To Algorithms. Cambridge University Press

Suki, N. M., & Suki, N. M. et. Al. 2013. Examining Factors Correlated with Consumer Online Shopping Behaviour. In: Innovation in the High-Tech Economy. Springer.

Zheng. F., Ni. F. C., & Zhao L. 2018. Localization and Recognition of Single Particle Image in Microscopy Based on Region Based Convolutional Neural Networks. Prosiding ICPSEE 2018




DOI: http://dx.doi.org/10.31289/jite.v3i1.2599

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