Pendekatan Level Data untuk Menangani Ketidakseimbangan Data Menggunakan Algoritma K-Nearest Neighbor

Isi Artikel Utama

Resianta Perangin-angin
Eva Julia Gunawati Harianja
Indra Kelana Jaya

Abstrak

Dalam penelitian ini digunakan dataset yang memiliki tingkat ketidakseimbangan yang berbeda beda mulai dari 16.40, 8.60, 2.06, 2.78, 1.87, tentu hal ini dapat menurunkan kinerja algoritma klasifikasi. Secara umum ketidakseimbangan kelas dapat ditangani dengan dua pendekatan, yaitu level data dan level algoritma. Pendekatan level data ditujukan untuk memperbaiki keseimbangan kelas, sedangkan pendekatan level algoritma ditujukan untuk memperbaiki algoritma atau menggabungkan (ensemble) pengklasifikasi agar lebih konduktif terhadap kelas minoritas. Pada penelitian ini diusulkan pendekatan level data dengan resampling, yaitu random oversampling (ROS), dan random undersampling (RUS), Pengklasifikasi yang digunakan adalah k-near neighbors. Hasil penelitian menunjukkan bahwa model ROS+KNN dan RUS+KNN didapat dengan selisih G-Means sebesar 13% dan F-Measure 2,08%, dari, hal ini menunjutkan bahwa RUS+KNN dan ROS+KNN bisa meningkatkan akurasi dari G-Mean dan F-Measure namun tidak memiliki perbedaan yang signifikan.

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Cara Mengutip
[1]
R. Perangin-angin, E. J. G. Harianja, dan I. K. Jaya, “Pendekatan Level Data untuk Menangani Ketidakseimbangan Data Menggunakan Algoritma K-Nearest Neighbor”, JTM, vol. 9, no. 1, Mei 2020.
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Biografi Penulis

Resianta Perangin-angin, Universitas Methodist Indonesia

Program Studi Komputerisasi Akuntansi

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