Analisis Penerapan Algoritma ID3 dalam Mendiagnosis Kesuburan Pria

Khaerul Ma'mur

Abstract


Men’s fertility disorder is one of difficult thing to be diagnosed, if fertility has a problem, the fertilization process will be disrupted. That’s all to be the most predominant cause of discordant household. Numbers of data on men's fertility are obtained by detailed enough. Secondary data from this dataset, made a reference in making decision tree using the ID3 algorithm in software Rapid Miner. Iterative Dichotomizer (ID3) is used because it is one of the commonly used classification algorithm in Data Mining to implement decisions of some attributes that have been determined in accordance with the data customized with their needs. ID3 algorithm generally calculates the Entropy value of each attribute and the value of the Information Gain of each attribute that are able to form a decision tree that is expected to assist in decision making. The results of the tests and the method can be used to diagnose men’s fertility appropriately with a fairly high degree of accuracy.


Keywords


Decision Tree; Data Mining; Iterative Dichotomizer 3; Men’s Fertility

References


David, G., Jose Luis Girela, Joaquin De Juan, M. Jose Gomez-Torrez, Magnus Johnsson, 2012, “Predicting Seminal Quality with Artificial Intelligence Methods”. Expert Systems with Applications, http://www.researchgate.net/publication/230868076_Predicting_seminal_quality_with_artificial_intelligence_methods/file/79e415058f10cc3081.pdf, (Diakses 10 April 2016)

David, Mcg. 2004. Tutorial: The ID3 Decision Tree Algorithm, Monash Uviversity Faculty of Information Technology.

Dunham H. Margareth. 2002. Data Mining Introductory and Advantaced Topics, Southern Methodist University.

Han, J., & Kamber, M. 2006. Data Mining Concept and Tehniques. San Fransisco: Morgan Kauffman.

Hand, D., Mannila, H. and Smyth, P.; 2001. Principles of Data Mining. MIT Press.

Irvine DS. 1998. Epidemiology and aetiology of male infertility. Hum. Reprod.; Vol 13(1):33-44.

Kusrini & Luthfi, E. T. 2009. ”Algoritma Data Mining. Yogyakarta: Andi Publishing.

Larose, D. T. 2005. Discovering Knowledge in Data An Introduction to Data Mining. New Jersey: John Willey and Sons.

Saifudin, A. (2018). Metode Data Mining untuk Seleksi Calon Mahasiswa pada Penerimaan Mahasiswa Baru di Universitas Pamulang. Jurnal Teknologi, 10(1), 25-36.

Santosa Budi. 2007. Data Mining: Teknik Pemanfaatan Data Untuk Keperluan Bisnis, Yogyakarta.

Tan, Pang-Ning, Michael Steinbach, and Vipin Kumar. 2004. Introduction to Data Mining. Boston: Pearson.




DOI: http://dx.doi.org/10.32493/informatika.v4i2.2274

Refbacks

  • There are currently no refbacks.


Copyright (c) 2019 Khaerul Ma'mur

Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.

Jurnal Informatika Universitas Pamulang (ISSN: 2541-1004 e-ISSN: 2622-4615)



This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International (CC BY-NC 4.0) License