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All Journal JOIV : International Journal on Informatics Visualization
Aida Mustapha
Universiti Tun Hussein Onn Malaysia, Johor, Malaysia
Computer Science & IT

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Customer Profiling using Classification Approach for Bank Telemarketing Shamala Palaniappan; Aida Mustapha; Cik Feresa Mohd Foozy; Rodziah Atan
JOIV : International Journal on Informatics Visualization Vol 1, No 4-2 (2017): The Advancement of System and Applications
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (887.021 KB) | DOI: 10.30630/joiv.1.4-2.68

Abstract

Telemarketing is a type of direct marketing where a salesperson contacts the customers to sell products or services over the phone. The database of prospective customers comes from direct marketing database. It is important for the company to predict the set of customers with highest probability to accept the sales or offer based on their personal characteristics or behavior during shopping. Recently, companies have started to resort to data mining approaches for customer profiling. This project focuses on helping banks to increase the accuracy of their customer profiling through classification as well as identifying a group of customers who have a high probability to subscribe to a long term deposit. In the experiments, three classification algorithms are used, which are Naïve Bayes, Random Forest, and Decision Tree. The experiments measured accuracy percentage, precision and recall rates and showed that classification is useful for predicting customer profiles and increasing telemarketing sales.
Classification of Alcohol Consumption among Secondary School Students Shamala Palaniappan; Norhamreeza A Hameed; Aida Mustapha; Noor Azah Samsudin
JOIV : International Journal on Informatics Visualization Vol 1, No 4-2 (2017): The Advancement of System and Applications
Publisher : Politeknik Negeri Padang

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (650.68 KB) | DOI: 10.30630/joiv.1.4-2.64

Abstract

In 2016, the National Institute of Health reported that 26% of 8th graders, 47% of 10th graders, and 64% of 12th graders have all had experience in consuming alcoholic drinks. This finding indicates an accelerating trend in alcohol use among school students, hence a growing concerns among the public. To address this issue, this paper is set to model the alcohol consumption data among the secondary school students and attempt to predict the alcohol consumption behaviors among them. A set of classification experiments are carried out and the classification accuracies are compared between two variations of neural network algorithms; a self-tuning multilayer perceptron classifier (AutoMLP) against the standard MLP using the student alcohol consumption dataset. It is found that AutoMLP produced better accuracy of 64.54% than neural network with 61.78%.
Co-Authors Cik Feresa Mohd Foozy Noor Azah Samsudin Norhamreeza A Hameed Rodziah Atan Shamala Palaniappan