Recommendation system always involves huge volumes of data, therefore it causes
the scalability issues that do not only increase the processing time but also reduce
the accuracy. In addition, the type of data used also greatly affects the result of the
recommendations. In the recommendation system, there are two common types of
data namely implicit (binary) rating and explicit (scalar) rating. Binary rating
produces lower accuracy when it is not handled with the properly. Thus, optimized
K-Means+ clustering and user-based collaborative filtering are proposed in this
research. The K-Means clustering is optimized by selecting the K value using the
Davies-Bouldin Index (DBI) method. The experimental result shows that the
optimization of the K values produces better clustering than Elbow Method. The K-
Means+ and User-Based Collaborative Filtering (UBCF) produce precision of 8.6%
and f-measure of 7.2%, respectively. The proposed method was compared to
DBSCAN algorithm with UBCF, and had better accuracy of 1% increase in
precision value. This result proves that K-Means+ with UBCF can handle implicit
feedback datasets and improve precision
Rumpun Ilmu
Teknik Elektro
Bahasa Asli/Original Language
English
Level
Nasional
Status
Dokumen Karya
No
Judul
Tipe Dokumen
Aksi
1
Point of Interest (POI) Recommendation System using Implicit Feedback Based on K-Means+ Clustering and User-Based Collaborative Filtering.pdf
[PAK] Full Dokumen
2
Sinta 3 - ComEngApp.pdf
Dokumen Pendukung Karya Ilmiah (Hibah, Publikasi, Penelitian, Pengabdian)
