APPLICATION OF NAIVE BAYES CLASSIFIER ALGORITHM IN DETERMINING NEW STUDENT ADMISSION PROMOTION STRATEGIES
PENERAPAN ALGORITMA NAIVE BAYES CLASSIFIER DALAM MENENTUKAN STRATEGI PROMOSI PENERIMAAN MAHASISWA BARU
DOI:
https://doi.org/10.33557/journalisi.v1i1.2Keywords:
Data Mining, Naive Bayes, ClassificationAbstract
Data Mining is a process that uses statistical techniques, mathematics, artificial intelligence, machine learning to extract and identify useful information and related knowledge from large databases. Data mining is the process of finding new patterns in data by filtering large amounts of data. Data mining uses pattern recognition technology that is similar to statistical techniques and mathematical techniques. The patterns found can provide useful information for generating economic benefits, effectiveness and efficiency. Algorithm Naive Bayes Classifier is one method of data mining that can be used to support effective and efficient promotion strategies. The Naive Bayes Classifier algorithm is used to predict the interest of the study based on the calculations performed. The data used are new student registration data from 2014 until 2016 at Bina Darma University. The results of this study are new models that are expected to provide important information can be used to assist the Marketing Team of Bina Darma University Palembang in policy making and implementation of appropriate marketing strategy. The results obtained are expected to help to support the promotion strategies that impact on the effectiveness and efficiency of promotion and increase the number of new students who will register.
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References
Arikunto, Suharsimi. 2011. Prosedur Penelitian: Suatu Pendekatan Praktik. Edisi Revisi VII. Jakarta: PT. Rineka Cipta.
Basuki, Sulistyo. 2006. Metode Penelitian. Jakarta: Wedatama Widya Sastra.
Budi, Santoso, 2007, Data Mining : Teknik Pemanfaatan Data untuk Keperluan Bisnis, Graha Ilmu, Yogyakarta
Kusrini dan Luthfi. E. Taufiq.(2009).Algoritma Data Mining. Yogyakarta: Andi. Laksana, Fajar. 2008. Manajemen Pemasasaran : Pendekatan Praktis. Edisi Pertama. Cetakan Pertama. Graha Ilmu. Yogyakarta.
Setyosari, Punaji. 2010. Metode Penelitian dan Pengembangan. Jakarta: Kencana
Sugiyono. 2012. Metode Penelitian Kuantitatif Kualitatif dan R&D. Bandung: Alfabeta.
Turban, E., dkk, 2005, Decicion Support Systems and Intelligent Systems, Andi Offse
www.binadarma.ac.id, website Universitas Bina Darma Palembang pada tanggal 29-05-2016
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