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IMPLEMENTASI FUNGSI DISPERSION RATIO PADA PROSES SPLITING ATRIBUT ALGORITMA DECISION TREE

Author


Dimas Ari Setyawan(1Mail), Deni Nur Fauzi(2),
(1) Politeknik Negeri Madiun, Indonesia
(2) Politeknik Negeri Madiun,

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Available online: 2023-03-29  |  Published : 2023-03-29
Copyright (c) 2023 Dimas Ari Setyawan, Deni Nur Fauzi
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Abstract


Metode klasifikasi yang memiliki proses seleksi fitur adalah algoritma decision tree. Proses seleksi fitur pada algortima decision tree biasanya menggunakan fungsi Information Gain. Information gain  pada algoritma decision tree memiliki kelemahan jika ada dataset yang memiliki atribut key seperti Product-ID. Fungsi dispersion ratio pada algoritma decision tree dapat meningkatkan signifikansi proses seleksi fitur, sehingga dapat mengatasi kekurangan fungsi information gain. Proses splitting atribut pada decision tree menggunakan fungsi dispersion ratio dengan menggunakan dataset yang diambil dari UCI machine learning repository. Dari 7 dataset, spliting menggunakan dispersion ratio menghasilkan nilai prediksi yang lebih baik denagn rata-rata selisih nilai prediksi untuk decision tree dispersion ratio dengan decision tree information gain adalah 4.71 %.


Keywords


Decision tree, Spliting Atribut, Dispersion Ratio

References


D. A. Setyawan and C. Fathicah, “Pengembangan Metode Decision Tree dengan Diskritisasi Data dan Splitting Atribut Menggunakan Hierarchical Clustering dan Dispersion Ratio,” JUTI J. Ilm. Teknol. Inf., vol. 18, no. 2, pp. 179–187, 2020.

S. Roy, S. Mondal, A. Ekbal, M. Sankar, and D. Felix, “Dispersion Ratio based Decision Tree Model for Classification,” Expert Syst. Appl., vol. 116, pp. 1–9, 2019, doi: 10.1016/j.eswa.2018.08.039.

J. Wang, S. Zhou, Y. Yi, and J. Kong, “An Improved Feature Selection Based on Effective Range for Classification,” vol. 2014, 2014.

S. Roy, “CRDT : Correlation Ratio Based Decision Tree Model for Healthcare Data Mining,” IEEE 16th Int. Conf. Bioinforma. Bioeng. CRDT, 2016, doi: 10.1109/BIBE.2016.21.

M. K. Mouthami, “Sentiment Analysis and Classification Based On Textual Reviews,” Int. Conf. Inf. Commun. Embed. Syst., 2013.

A. C. Müller and S. Guido, Introduction to Machine Learning with Python and Scikit-Learn. 2015.

R. Pandya, “C5 . 0 Algorithm to Improved Decision Tree with Feature Selection and Reduced Error Pruning,” vol. 117, no. 16, pp. 18–21, 2015.

L. Rutkowski, L. Pietruczuk, P. Duda, and M. Jaworski, “Decision Trees for Mining Data Streams Based on the McDiarmid ’ s Bound,” vol. 25, no. 6, pp. 1272–1279, 2013.


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