Perbandingan 5 Jarak K-Nearest Neighbor pada Analisis Sentimen

Authors

  • Almuzhidul Mujhid Universitas Ahmad Dahlan, Indonesia
  • Aris Thobirin Universitas Ahmad Dahlan, Indonesia
  • Salma Nadya Firdausy Universitas Ahmad Dahlan, Indonesia
  • Sugiyarto Surono Universitas Ahmad Dahlan, Indonesia
  • Lanova Ade Rahmadani Universitas Ahmad Dahlan, Indonesia

DOI:

https://doi.org/10.26555/konvergensi.v0i0.23170

Abstract

K-Nearest Neighbor (KNN) merupakan algoritma yang biasa digunakan untuk klasifikasi. Penelitian ini menggunakan ulasan aplikasi Maxim di Google Play Store. Pengguna yang sudah mengunduh aplikasi Maxim berhak memberikan ulasan di Google Play Store guna berbagi informasi untuk pengguna lain. Implementasi K-Nearest Neighbor (KNN) terhadap Sentiment Analysis ulasan aplikasi Maxim dapat digunakan untuk menentukan kelas ulasan bernilai positif, neutral, atau negatif. Peneliti melakukan perbandingan 5 jarak yang berbeda untuk metode KNN yaitu jarak Euclidean, Manhattan, Minkowski, Chebyshev dan Canberra. Pengujian yang telah dilakukan memberikan hasil akurasi pada klasifikasi KNN dengan jarak yang berbeda, memberikan hasil akurasi yang berbeda-beda, yaitu jarak Euclidean  84 persen, jarak Manhattan  79 persen, jarak Minkowski 84 persen, jarak Chebyshev  7 persen dan jarak Canberra =44 persen.

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Published

2021-10-11

Issue

Vol 8, No 2 (2021)

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Articles

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