Optimasi Algoritma Naive Bayes Menggunakan Metode Cross Validation Untuk Meningkatkan Akurasi Prediksi Tingkat Kelulusan Tepat Waktu

  • Yohakim Benedictus Samponu Universitas Amikom Yogyakarta
  • Kusrini Kusrini Universitas Amikom Yogyakarta
Keywords: data mining, Cross Validation, Naïve Bayes

Abstract

Education at this time is an important requirement in facing the demands of an increasingly advanced era in technolo-gy. To compensate this, the existing educational standards in universities must also be improved, this is a bit much affect the pattern of teaching from universities that produce qualified graduates who can compete in the world of work later and indirectly give a positive impact on the university itself. Qualified graduates are of course not only depending on the role of a university but also majors and quality of education as long as students are still in high school / vocational school also plays an important role. Results of the on-time graduation rate prediction research can be used as an information to im-prove the quality and optimization of the education system but it requires a maximum degree of accuracy. This research predicts on time graduation rates by conducting analysis using data mining classification techniques. Naïve Bayes algo-rithm that are used for this research will be discussed as a reference in conducting research. The author performs a series of different experimental scenarios / cross validation to perform comparisons that can give a difference in the level of ac-curacy gained from this research. The results of this research indicate that with the addition of Cross Validation testing scenario there is an increase of 2% accuracy of the test.

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References

Kusrini; Taufiq, Luthfi Emha, Algoritma Data Mining, Penerbit Andi, Yogyakarta, 2009
Larose, Daniel, T, Discovering Knowledge in Data an Introduction to Data Mining, Wiley-Interscience, United States of America, 2005
Suyanto, Data Mining Untuk Klasifikasi dan Klasterisasi Data, Penerbit Informatika, Bandung, 2017
Ishtiaq Ahmed; Donghai Guan; Tae Choong Chung, 2014, SMS Classification Based on Naïve Bayes Classifier and Apriori Algorithm Frequent Itemset, International Journal of Machine Learning and Computing, Vol. 4, No. 2, April 2014
Kumar Krishna Venkata S; Kiruthika P: 2015, An Overview of Classification Algorithm in Data mining, International Journal of Advanced Research in Computer and Communication Engineering Vol. 4, Issue 12, December 2015
Kusumadewi Sri, 2009, Klasifikasi Status Gizi Menggunakan Naïve Bayesian Classification, CommIT, Vol. 3 No. 1 Mei 2009, hlm. 6 – 11
Manjusha K. K., Sankaranarayanan, K., Seena P., 2014, Prediction of DifferentDermatological Conditions Using Naïve Bayesian Classification, International Journal ofAdvanced Research in Computer Science and Software Engineering, Vol 4, No 1, Hal 864868.
Priyanti, Evi; 2017, Penerapan Algoritma Naïve Bayes untuk klasifikasi Bakteri Gram Negatif, Jurnal Teknik Komputer Vol 3 No. 2 Agustus 2017
Nofriansyah Dicky; ErwansyahKamil; RamadhanMukhlis, 2016, Penerapan Data Mining dengan Algoritma Naïve Bayes Clasifier untuk Mengetahui Minat Beli Pelanggan terhadap Kartu Internet XL (Studi Kasus di CV. Sumber Utama Telekomunikasi), Jurnal SAINTIKOM Vol.15, No. 2, Mei 2016
Saleh Alfa, 2015, Implementasi Metode Klasifikasi Naïve Bayes Dalam Memprediksi Besarnya Penggunaan Listrik Rumah Tangga, Citec Journal, Vol. 2, No. 3, Mei 2015 – Juli 2015
Faisal, M Reza, Seri Belajar Data Science Klasifikasi Dengan Bahasa Pemrograman R, https://bukudatascienceklasifikasir.codeplex.com/, September 2017
Han, J., Kamber, M., & Pei, J. , Data Mining: Concepts and Techniques Third. Elsevier, http://myweb.sabanciuniv.edu/rdehkharghani/files/2016/02/The-Morgan-Kaufmann-Series-in-Data-Management-Systems-Jiawei-Han-Micheline-Kamber-Jian-Pei-Data-Mining.-Concepts-and-Techniques-3rd-Edition-Morgan-Kaufmann-2011.pdf, 2012
Published
2018-01-09
Section
Articles
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