PENGARUH USER PROFILING PADA REKOMENDASI SISTEM MENGGUNAKAN K MEANS DAN KNN

Authors

  • Hartatik Universitas Amikom Yogyakarta
  • Rosyid Universitas AMIKOM Yogyakarta

Keywords:

Sparsity, Skalabilitas, Silhouette, k means, KNN

Abstract

Sparsity adalah satu masalah yang sering terjadi pada teknik collaborative clustering dimana user sedikit sekali memiliki informasi (pada penelitian ini rating) yang menyebabkan sistem seringkali tidak akurat ketika memberikan rekomendasi. Banyak metode yang bisa digunakan untuk menyelesaikan masalah sparsity data, salah satunya adalah metode KNN. Namun metode KNN memiliki kelemahan yaitu scalability. Scalability terjadi ketika ketika data yang harus dicari kesamaannya semakin besar. Salah satu solusi yang mungkin diimplementasikan adalah dengan mencari profil dari user dan mengelompokkannya menjadi satu kelompok. Eksperimen yang dilakukan pada penelitian ini untuk mengatasi masalah sparsity dan scalability adalah dengan menggabungkan algoritma silhouette, k-means, K-Nearest Neighbour. Dataset yang dipakai di penelitian ini, berjumlah 700 rating yang di crawling melalui web traveloka. Data rating antara user dan item akan disimpan dalam database, untuk selanjutnya dirubah menjadi bentuk array user-item. Hasil pengujian dengan 5 data uji didapatkan nilai rata-rata RMSE 1,33% dengan rata-rata akurasi = 100% - 1,33% = 98,67 %.

 

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Published

2020-07-04

How to Cite

Hartatik, & Rosyid. (2020). PENGARUH USER PROFILING PADA REKOMENDASI SISTEM MENGGUNAKAN K MEANS DAN KNN. JOURNAL OF INFORMATION SYSTEM MANAGEMENT, 2(1), 13-18. Retrieved from https://jurnal.amikom.ac.id/index.php/joism/article/view/199

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