Article Per Year (5 Year)
p-Index From 2020 - 2025
0.23
P-Index
This Author published in this journals
All Journal Journal of Intelligent Software Systems
Laksono, Triyan Agung
Unknown Affiliation
Computer Science & IT

Published : 1 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 1 Documents
Search

 1 
DIGITAL ACTIVITY LOCATION CLUSTERING BASED ON TWITTER GEOSPATIAL DATA FOR SPATIOTEMPORAL BUSINESS INTELLIGENCE Laksono, Triyan Agung; Andriyani, Widyastuti; Putra, Fadhlih Girindra; Ruas da silva, Ivonia Fatima; widayani, Wiwi
Journal of Intelligent Software Systems Vol 4, No 1 (2025): Juli 2025
Publisher : LPPM UTDI (d.h STMIK AKAKOM) Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26798/jiss.v4i1.2005

Abstract

This research develops an approach for clustering digital activity locations based on Twitter geospatial data with the aim of supporting business intelligence spatiotemporal . By utilizing the Twitter Geospatial Data dataset containing more than 14 million tweets geo-tagged from the United States, this study implements and compares the DBSCAN and K- Means algorithms to identify spatial and temporal patterns of Twitter user activity. The research process begins with the data pre -processing stage using the Knowledge Discovery Database (KDD), followed by the implementation of the clustering algorithm , and ending with the integration of the results into the dashboard.business intelligence using Power BI . The results show that DBSCAN is able to detect irregular clusters that follow geographic patterns and population density, while K- Means produces a division of the region into three main clusters (West Coast, Central Region, and East Coast) with different temporal activity patterns. Integration of clustering results into a BI dashboard produces actionable business insights , such as identification of digital activity hotspots , optimal time for content delivery, geographic segmentation for marketing strategies, and temporal activity patterns for campaign scheduling. This research contributes to the development of an integrated spatiotemporal analysis pipeline to support data-driven decision making.
Co-Authors Andriyani, Widyastuti Putra, Fadhlih Girindra Ruas da silva, Ivonia Fatima Wiwi Widayani, Wiwi