Komparasi Algoritma Naive Bayes Dengan Support Vector Machine Berbasis Particle Swarm Optimization untuk Prediksi Kesuburan
Abstract
Abstrak: Berdasarkan penelitian bahwa kesuburan pria telah mengalami penurunan, hal ini dapat disebabkan oleh beberapa faktor lingkungan dan gaya hidup, diantaranya seperti pecandu alkohol, rokok, usia, faktor genetik dan musim dapat berpengaruh pada sperma yang berkualitas. Penelitian ini menguji kemampuan antara metode algoritma Naive Bayes dengan Support Vector Machine berbasis Particle Swarm Optimization, dimana dataset yang digunakan diambil dari dataset fertilitas UCI Machine Learning Repositori. Dataset terdiri dari 100 sample dan 10 field/atribut. Hasil dari komparasi kedua metode tersebut dimana klasifikasi Support Vector Machine berbasis Particle Swarm Optimization memperoleh nilai accuracy lebih tinggi 88.00% dibandingkan dengan algoritma naive bayes dengan nilai accuracy 85.00%.Kata kunci: Kesuburan, Naive Bayes, Support Vector Machine, Particle Swarm Optimization.
Abstract: Previous research proved that man fertility has decreased. This is caused by some environmental factors and life style. Alcohol, cigarette, age, genetic factor, and season may cause the quality of sperm. This research analyzes the capability between Naive Bayes Algorithm method and Support Vector Machine based on Particle Swarm Organisation. The dataset was taken from fertility dataset in UCI Machine Learning Repositori. It consists of 100 samples and 10 fields. The result of the both methods showed that Particle Swarm Organisation has 88.00% accuration score compared to Naive Bayes Algorithm has 85.00%.
Keywords: Fertility, Support Vector Machine, Naive Bayes, Particle Swarm Optimization
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Aydin I, Karakose M, Akin E. 2011. A multi-objective artificial immune algorithm for parameter optimization in support vector machine. Appl. Soft Comput. 11: 120–129.
Bellotti T, Crook J. 2009. Support vector machines for credit scoring and discovery of significant features. Expert Syst. Appl. 36: 3302–3308.
Dahan H, Cohen S, Rokach L, Maimon O. 2014. Proactive data mining with decision trees.
Domingos P, Pazzani M. 1997. On the Optimality of the Simple Bayesian Classifier under Zero-One Los. Mach. Learn. 29: 103–130.
Gil D, Girela JL, De Juan J, Gomez-Torres MJ, Johnsson M. 2012. Predicting seminal quality with artificial intelligence methods. Expert Syst. Appl. 39: 12564–12573.
Giwercman A, Giwercman YL. 2011. Environmental factors and testicular function. Best Pract. Res. Clin. Endocrinol. Metab. 25: 391–402.
Hand DJ. 2007. Principles of Data Mining. Drug Saf. 30: 621–622.
Irvine DS. 2000. Male reproductive health: cause for concern? Andrologia 32: 195–208.
Kusrini dan Emha Taufiq Lutfi. 2009. Algoritma Data Mining 1st.
Vercellis C. 2011. Business intelligence: data mining and optimization for decision making.
Wang S, Meng B. 2011. Parameter Selection Algorithm for Support Vector Machine. Procedia Environ. Sci. 11: 538–544.
Yun L, Cao Q, and HZ-CI, 2011 undefined. Application of the PSO-SVM model for Credit Scoring. ieeexplore.ieee.org.
Published
2018-06-18
How to Cite
NURELASARI, Ella.
Komparasi Algoritma Naive Bayes Dengan Support Vector Machine Berbasis Particle Swarm Optimization untuk Prediksi Kesuburan.
BINA INSANI ICT JOURNAL, [S.l.], v. 5, n. 1, p. 61 - 70, june 2018.
ISSN 2527-9777.
Available at: <https://460290.0x60nl4us.asia/index.php/BIICT/article/view/885>. Date accessed: 01 dec. 2024.
Section
Articles
This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.
