The Development of Classification System of Student Final Assignment Using Naive Bayes Classifier Case Study: State Community Academy of Bojonegoro

 
 
 
  • Abstract
  • Keywords
  • References
  • PDF
  • Abstract


    In determining interest, students are faced with the choice of specialization in determining the final field of interest. Specialization in the Information Management Study Program of State Community Academy of Bojonegoro is divided into five specializations. The choice of specialization groups is an important part. This is because the accuracy in choosing specialization groups is part of the initial plan of students to determine the final assignment project. Thus, the field of specialization taken will be in accordance with the interests and abilities of the students and will have an impact on the process. In this work, we propose a system that can provide information about the classification of student final assignments. We use Naive Bayes Classifier (NBC) algorithm to do the classification. In this work, we used datasets, that obtained from the State Community Academy of Bojonegoro Informatics Management Study Program. Based on the accuracy testing of the classification results, the system gives higher result, than test manual calculation of 83.33%.

     

     


  • Keywords


    Data Mining, classification, Naive Bayes Classifier, Machine Learning, final assignment.

  • References


      [1] Meilani BD & Susanti N (2014), “Aplikasi Data Mining Untuk Menghasilkan Pola Kelulusan Siswa Dengan Metode Naive Bayes”. Jurnal LINK , Vol.21, No.2, (2014), pp:1-6.

      [2] McLeod JrR & Schell GP (2007), "Management Information System. 10 th ed" Pearson Education, Inc.

      [3] Zhang H, Jiang L, & Su J (2005), “Augmenting Naive Bayes for Ranking”, Proceedings of the 22nd International Conference on Machine Learning, (2005), pp.1025-1032.

      [4] Ranjan J (2007), “Application of Data Mining Technique in Pharmaceutical Industry”, Journal of Theoritical and Applied Information Technology, Vol 3, (2007), pp: 61 – 67.

      [5] Davies & Beynon P. (2004), “Database Systems Third Edition”, Palgrave Macmillan, New York.

      [6] Santoso B (2007), “Data Mining: Teknik Pemanfaatan Data Untuk Keperluan Bisnis”, Yogyakarta: Graha Ilmu.

      [7] Han J & Kamber M. (2006), “Data Mining : Concept and Techniques Second Edition”, Morgan Kaufmann Publishers.

      [8] Mulyanto A. (2009), “Sistem Informasi Konsep & Aplikasi”, Yogyakarta: Pustaka Pelajar.

      [9] Nugroho A & Subanar (2013). "Klasifikasi Naive Bayes untuk Prediksi Kelahiran pada Data Ibu Hamil", Berkala MIPA , Vol.23, No.3, (2013), pp:297-308.

      [10] Luthfiansyah DH (2016), “SPK Pemilihan Jurusan Berdasarkan Kuisoner Minat Bakat Menggunakan Metode Naive Bayes”, Seminar Informatika Aplikatif Polinema, (2016).

      [11] Musthofa M (2016), “Pengembangan Sistem Pendukung Keputusan Penjurusan Bagi Siswa Baru Menggunakan Metode Naive Bayes”, Seminar Informatika Aplikatif Polinema, (2016).

      [12] Saraswati NW (2013), “Naïve Bayes Classifier Dan Support Vector Machines Untuk Sentiment Analysis”, Seminar Nasional Sistem Informasi Indonesia, (2013).

      [13] PDD Polinema Rintisan AKN Bojonegoro, “Politeknik Negeri Malang Program Studi Diluar Domisili Di Kabupaten Bojonegoro (Rintisan Akademi Komunitas Negeri Bojonegoro)”, Dasar Hukum, Program Studi, Unit Kegiatan Mahasiswa, Kerjasama Internasional, url: http://www.aknbojonegoro.ac.id/ [accessed: January 25th, 2018].

      [14] Tim Penyusun (2015), "Pedoman Akademik AKN Bojonegoro, Bojonegoro".

      [15] Muktamar BA, Setiawan NA & Adji TB (2015), "Analisis Perbandingan Tingkat Akurasi Algoritma Naïve Bayes Classifier Dengan Correlated-Naïve Bayes Classifier", Seminar Nasional Teknologi Informasi dan Multimedia, (2015), pp: 49-54.


 

View

Download

Article ID: 26996
 
DOI: 10.14419/ijet.v7i4.44.26996




Copyright © 2012-2015 Science Publishing Corporation Inc. All rights reserved.