Peningkatan Akurasi K-Nearest Neighbor Pada Data Index Standar Pencemaran Udara Kota Pekanbaru

Yuliska Yuliska; Khairul Umam Syaliman

doi:10.25299/itjrd.2020.vol5(1).4680

Authors

Yuliska Yuliska Teknik Informatika, Politeknik Caltex Riau
Khairul Umam Syaliman Teknik Informatika, Politeknik Caltex Riau

DOI:

https://doi.org/10.25299/itjrd.2020.vol5(1).4680

Keywords:

Akurasi, Attribute Weighting, K-Nearest Neighbor, Local Mean, Peningkatan

Abstract

kNN adalah salah satu metode yang popular karena mudah dieksploitasi, generalisasi yang biak, mudah dimengerti, kemampuan beradaptasi ke ruang fitur yang rumit, intuitif, atraktif, efektif, flexibility, mudah diterapkan, sederhana dan memiliki hasil akurasi yang cukup baik. Namun kNN memiliki beberapa kelemahan, diantaranya memberikan bobot yang sama pada setiap attribut sehingga attribut yang tidak relevant juga memberikan dampak yang sama dengan attribut yang relevant terhadap kemiripan antar data. Masalah lain dari kNN adalah pemilihan tetangga terdekat dengan system suara terbanyak, dimana system ini mengabaikan kemiripan setiap tetangga terdekat dan kemungkinan munculnya mayoritas ganda serta kemungkinan terpilihnya outlier sebagai tetangga terdekat. Masalah-masalah tersebut tentu saja dapat menimbulkan kesalahan klasifikasi yang mengakibatkan rendahnya akurasi. Pada penelitian kali ini akan dilakukan peningkatan akurasi dari kNN tersebut dalam melakukan klasifikasi terhadap data Index Standar Pencemaran Udara di Pekanbaru dengan menggunakan pembobotan attribut (Attibute Weighting) dan local mean. Adapun hasil dari penelitian ini didapati bahwa metode yang diusulkan mampu untuk meningkatkan akurasi sebesar 2.42% dengan rata-rata tingkat akurasi sebesar 97.09%.

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