Peningkatan Kinerja K-Means  Clustering Berdasarkan Pembobotan Jarak Menggunakan Metode Principal Component Analysis

Alfian Agusnady; Opim Salim Sitompul Sitompul; Tulus Tulus

doi:10.30865/json.v7i2.9394

Authors

Alfian Agusnady Universitas Sumatera Utara
Opim Salim Sitompul Sitompul Universitas Sumatera Utara
Tulus Tulus Universitas Sumatera Utara

DOI:

https://doi.org/10.30865/json.v7i2.9394

Keywords:

Algoritma K-Means; Pembobotan atribut; Principal Component Analysis; Clustering; Sum of Square Error (SSE).

Abstract

Algoritma K-Means memiliki beberapa kelemahan, salah satunya terletak pada model jarak yang digunakan dalam penentuan kemiripan antar data yang memberikan perlakuan yang sama terhadap setiap atribut data, sehingga atribut yang kurang relevan dan memiliki sedikit kontribusi terhadap variasi data dapat memberikan dampak yang cukup berpengaruh terhadap hasil clustering. Hal ini tentu saja dapat menurunkan kinerja algoritma K-Means. Pembobotan atribut merupakan salah satu cara yang dapat digunakan untuk mendapatkan korelasi atribut data terhadap variasi data. Semakin tinggi nilai bobot dari suatu atribut maka semakin besar korelasinya terhadap variasi data, sehingga nilai bobot yang rendah dari suatu atribut tentunya memiliki sedikit kontribusi terhadap variasi data dan dapat memberikan dampak yang cukup berpengaruh terhadap kinerja dan hasil clustering. Pada penelitian ini, metode yang digunakan dalam perhitungan bobot atribut data yaitu Principal Component Analysis (PCA). Untuk melakukan pengujian terhadap metode yang diusulkan, maka penelitian ini menggunakan dataset dari UCI Machine Learning yang terdiri dari 351 data Ionosphere, 4177 data Abalone serta 1096 data kualitas udara dari Laboratorium Udara Kota Pekanbaru dan 120 data kualitas air. Evaluasi kinerja Clustering yang diusulkan berdasarkan nilai Sum of Square Error (SSE). Hasil pengujian pada penelitian ini terlihat bahwa dengan metode yang diusulkan dapat menghasilkan nilai SSE yang signifikan lebih kecil.

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