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Widiyanto, Muhamad
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Computer Science & IT Control & Systems Engineering

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Pengelompokan Pola Gerakan Berdasarkan Data Akselerometer Android Menggunakan Metode K-Means untuk Penelitian Pengenalan Aktivitas: Grouping Movement Patterns Based on Android Accelerometer Data Using K-Means Method for Activity Recognition Research Widiyanto, Muhamad; Hasbi Firmansyah
SITEDI (Sistem Informasi dan Teknologi Digital) Vol. 2 No. 4 (2025): Jurnal Sistem Informasi dan Teknologi Digital (SITEDI)
Publisher : Universitas Teknologi Digital

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70888/sitedi.v2i4.63

Abstract

The objective of this research is to identify human movement patterns from accelerometer data using the K-Means algorithm. The data used was collected from a mobile phone's accelerometer placed in a chest pocket, which recorded walking activity along a specific route. The analysis process began with a preprocessing stage that included data cleaning and transformation, and concluded with clustering using the K-Means algorithm. The clustering results show that the data, consisting of 5068 data points, was divided into 5 clusters. The algorithm's performance evaluation indicates that the K-Means algorithm is effective in grouping data based on movement patterns. The conclusion of this study is that the K-Means algorithm is a reliable approach for identifying movement patterns from accelerometer data and can be utilized for various applications, such as user authentication and activity monitoring.
Pengelompokan Pola Gerakan Berdasarkan Data Akselerometer Android Menggunakan Metode K-Means untuk Penelitian Pengenalan Aktivitas: Grouping Movement Patterns Based on Android Accelerometer Data Using K-Means Method for Activity Recognition Research Widiyanto, Muhamad; Hasbi Firmansyah
SITEDI (Sistem Informasi dan Teknologi Digital) Vol. 2 No. 4 (2025): Jurnal Sistem Informasi dan Teknologi Digital (SITEDI)
Publisher : Universitas Teknologi Digital

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70888/sitedi.v2i4.63

Abstract

The objective of this research is to identify human movement patterns from accelerometer data using the K-Means algorithm. The data used was collected from a mobile phone's accelerometer placed in a chest pocket, which recorded walking activity along a specific route. The analysis process began with a preprocessing stage that included data cleaning and transformation, and concluded with clustering using the K-Means algorithm. The clustering results show that the data, consisting of 5068 data points, was divided into 5 clusters. The algorithm's performance evaluation indicates that the K-Means algorithm is effective in grouping data based on movement patterns. The conclusion of this study is that the K-Means algorithm is a reliable approach for identifying movement patterns from accelerometer data and can be utilized for various applications, such as user authentication and activity monitoring.
Co-Authors Hasbi Firmansyah