Kombinasi K-Nearest Neighbor (KNN) dan Relief-F untuk Meningkatkan Akurasi Pada Klasifikasi Data

Rahmad Nurhadi Yusra, Opim Salim Sitompul, Sawaluddin Sawaluddin

Abstract


Dalam penelitian ini, penulis mengusulkan proses peningkatan akurasi pada K-Nearest Neighbor (KNN) dengan kombinasi seleksi fitur menggunakan metode Relief-F. Adapun penyebab kurang maksimalnya akurasi pada K-Nearest Neighbor dibandingkan dengan metode klasifikasi lainnya disebabkan oleh pengaruh atribut yang kurang signifikan dan persentase pengaruh yang cenderung rendah dari suatu data dalam menentukan kelas pada data baru. Metode Relief-F digunakan untuk melakukan seleksi pada atribut yang korelasinya kurang baik dari data yang diujikan. Pengujian dari metode yang diusulkan yaitu membandingkan akurasi yang diperoleh dari metode KNN tanpa menggunakan seleksi fitur dengan KNN menggunakan seleksi fitur Relief-F. Hasil pengujian yang diperoleh yaitu metode yang diusulkan mampu meningkatkan akurasi klasifikasi dari KNN dengan peningkatan yang diperoleh yaitu sebesar 10.32% setelah dibandingkan dengan pengujian KNN tanpa seleksi fitur.

Keywords


Klasifikasi, K-Nearest Neighbor, Relief-F, Seleksi Fitur, Peningkatan Akurasi

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References


H. Liu, H. Motoda, R. Setiono, and Z. Zhao, "Feature Selection: Ever Evolving Frontier in Data Mining,". In Feature selection in data mining, pp. 4-13. 2010.

D. A. Irawan, Z. A. Baizal, and E. G. Perdana, "Analisis dan Implementasi Algoritma Relieff untuk Feature Selection pada Klasifikasi Dataset Multiclass (Doctoral dissertation, MS thesis, Universitas Telkom, Jakarta, Indonesia,". 2011.

Y. Chen, and Y. Hao, "A Feature Weighted Support Vector Machine and K-Nearest Neighbor Algorithm for Stock Market Indices Prediction," Expert Systems with Applications (2017), vol. 80, pp. 340-355, 2017.

J. S. Raikwal, and K. Saxena, "Performance Evaluation of SVM and K-Nearest Neighbor Algorithm over Medical Data set," International Journal of Computer Applications. vol. 50, no. 14, pp. 35-39, 2012.

A. Ashari, I. Paryudi, and A. M. Tjoa, "Performance Comparison between Naïve Bayes, Decision Tree and k-Nearest Neighbor in Searching Alternative Design in an Energy Simulation Tool," (IJACSA) International Journal of Advanced Computer Science and Applications. vol. 4, no. 11, pp. 33-39, 2013.

M. Danil, S. Efendi, and R. W. Sembiring, "The Analysis of Attribution Reduction of K-Nearest Neighbor (KNN) Algorithm by Using Chi-Square," In Journal of Physics: Conference Series, vol. 1424, no. 1, pp. 012004, 2019.

T. R. Reddy, B. V. Vardhan, M. GopiChand, and K. Karunakar, "Gender prediction in author profiling using ReliefF feature selection algorithm," In Intelligent Engineering Informatics, pp. 169-176, Springer, Singapore, 2018.

J. Huang, J. Zhou, and L. Zheng, "Support Vector Machine Classification Algorithm Based on Relief-F Feature Weighting," In 2020 International Conference on Computer Engineering and Application (ICCEA), pp. 547-553, 2020.

J. Han, J. Pei, and M. Kamber, "Data Mining Concept and Techniques, 3rd edition," Morgan Kaufmann-Elsevier. vol. 2, no. 1, pp. 88-97, 2012.

A. Danades, D. Pratama, D. Anggraini, and D. Anggriani, "Comparison of Accuracy Level K-Nearest Neighbor Algorithm and Support Vector Machine Algorithm in Classification Water Quality Status," International Conference on System Engineering and Technology, pp. 137-141, 2016.

I. Kononenko, "Estimating Attributes: Analysis and Extensions of Relief," In European conference on machine learning, pp. 171-182, 1994.

P. Refaeilzadeh, L. Tang, and H. Liu, "Encyclopedia of Database Systems," In Cross-validation, pp. 532-538, 2009.

J. D. Novaković, A. Veljović, S. S. Ilić, Ž. Papić, and T. Milica, "Evaluation of Classification Models in Machine Learning," Theory and Applications of Mathematics & Computer Science, vol. 7, no. 1, pp. 39-46, 2017.




DOI: https://doi.org/10.30743/infotekjar.v6i1.4106

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