Physical Activities Recommender System Based on Sequential Data Use K-Mean Clustering

Authors

  • Rizky Haffiyan Roseno School of computing, Telkom University Bandung, Indonesia
  • Z. K. A. Baizal School of computing, Telkom University Bandung, Indonesia
  • Ramanti Dharayani School of computing, Telkom University Bandung, Indonesia

DOI:

10.33395/sinkron.v9i1.13374

Keywords:

K-mean Clustering, Apache Spark, Recommender System, Physical Activities

Abstract

Physical activities such as Exercise are essential in maintaining health and fitness, especially for those who adopt a healthy lifestyle. Irregularity in doing Exercise can hurt the body and health, especially if it is not done according to one's physical capacity. In the framework of this research, we developed a Recommender System that aims to provide exercise suggestions according to the user's preferences, especially in the categories of cycling, running, walking, and horse riding. The primary considerations of the variables include heart rate (Average Heart Rate) and pace (Speed Rate). This research approach uses the FitRec Dataset and applies the K-Mean Clustering Algorithm, with the support of APACHE SPARK, for large-scale data processing, given the large data size in the FitRec dataset. Grouping is done using the FitRec dataset and K-Mean. Users are grouped according to heart rate and pace information; this provides appropriate Exercise for users. The test results show that the proposed system performs well, as indicated by the silhouette score = 0.596, calinzski-harabaz score = 2133.09, and davies bouldin score = 0.480. These test metrics reflect the system's ability to cluster. Indirectly, the accuracy performance of the system is assessed through these metrics, showing good accuracy test results.

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Author Biography

Rizky Haffiyan Roseno, School of computing, Telkom University Bandung, Indonesia

School Of Computing

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How to Cite

Roseno, R. H. ., Baizal, Z. K. A., & Dharayani, R. . (2024). Physical Activities Recommender System Based on Sequential Data Use K-Mean Clustering. Sinkron : Jurnal Dan Penelitian Teknik Informatika, 9(1), 581-593. https://doi.org/10.33395/sinkron.v9i1.13374