Penerapan Algoritma Naive Bayes Classifier Dalam Pengklasifikasian Uang Sekolah Siswa(Studi Kasus SMP XAVERIUS 13 Tanjung Sakti)

Retno, Paulina Dwi (2016) Penerapan Algoritma Naive Bayes Classifier Dalam Pengklasifikasian Uang Sekolah Siswa(Studi Kasus SMP XAVERIUS 13 Tanjung Sakti). Other thesis, Universitas Katolik Musi Charitas.

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Abstract

Every year, school, one of the educational institutions, always do their general routine activity. That is accepting new students. In this section, there are some process that should be undertaken by school, like promotion, filling form by prospective students, students selection and parents of student' interview.The results of the interview data inaccuracies often happen because it is still influenced by a factor of subjectivity so that built a system that can assist the school in classifying the tuition. One method of classification that is often used is the Naive Bayes Classifier. Naive Bayes Classifier perform calculations based on criteria which is used for classification of tuition fees. This research uses Naive Bayes Classifier Algorithm with some of criteria that have been set, including parents income, electricity cost, housing condition and number of vehicles. The results of this research is, the program that created it can be classified into three grades for classes that have the specified low, medium and high. In support of the program, there are five data entered into the program as test data. The result is that there are five appropriate data between manual and calculation programs. Therefore, the presentation of the program's success was 80%.

Item Type: Thesis (Other)
Additional Information: Skripsi Lengkap dapat dibaca di Ruang Referensi Perpustakaan UKMC Kampus Bangau.
Uncontrolled Keywords: Tuition fees, Naive Bayes Classifier, Criteria.
Subjects: T Technology > T Technology (General)
Divisions: Theses - S1 > Informatics Study Program
Depositing User: Perpustakaan Unika Musi Charitas
Date Deposited: 30 Nov 2017 01:25
Last Modified: 26 Mar 2018 07:41
URI: http://eprints.ukmc.ac.id/id/eprint/649

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