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Journal : Journal of Computer Networks, Architecture and High Performance Computing

Implementation of K-Means Clustering in Recognizing Crime Hotspots and Traffic Issues Through GIS Aryo Pratama; Muhammad Dedi Irawan; Septiana Dewi Andriana
Journal of Computer Networks, Architecture and High Performance Computing Vol. 6 No. 2 (2024): Articles Research Volume 6 Issue 2, April 2024
Publisher : Information Technology and Science (ITScience)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47709/cnahpc.v6i2.3771

Abstract

The challenge of accurately identifying instances of crime and traffic issues has rendered the precise localization thereof difficult, thereby impeding the populace's access to information concerning areas of high risk and safety. Employing a Geographic Information System (GIS)-based mapping system utilizing the K-means clustering method, spatial data pertaining to crime and traffic concerns are grouped. The primary objective is to aid in the identification of high-risk areas concerning crime and traffic matters. The methodology employed in this study revolves around the application of the K-means clustering method to categorize spatial data relevant to crime and traffic issues. K-means clustering represents a non-hierarchical cluster analysis technique designed to partition data into multiple groups based on spatial similarities. Research findings elucidate that through the utilization of the K-means clustering method, three distinct sets of clusters predicated upon the intensity of crime and traffic issues emerge. Consequently, from these clustering outcomes, districts and specific locales falling within each cluster, denoted as moderately vulnerable (C1), vulnerable (C2), and highly vulnerable (C3), can be delineated. This system is poised to furnish recommendations to pertinent authorities for addressing areas exhibiting heightened intensity levels while concurrently facilitating the generation of reports and dissemination of information to the public via a dedicated website pertaining to areas at elevated risk of crime and traffic issues.
Implementation of Statistical Quality Control Method in Product Quality Monitoring Information System Iqbal Maulana Syahputra; Triase Triase; Septiana Dewi Andriana
Journal of Computer Networks, Architecture and High Performance Computing Vol. 6 No. 2 (2024): Articles Research Volume 6 Issue 2, April 2024
Publisher : Information Technology and Science (ITScience)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47709/cnahpc.v6i2.3825

Abstract

The business sector faced intensifying competition due to significant advancements in information systems and technology. PT. Florindo Makmur, a leading private company in the cassava processing industry producing tapioca flour, has proven to implement quality standards to uphold product quality and ensure customer satisfaction. The product quality inspection process had to meet standards before packaging; however, reporting remained manual using paper sheets, elevating the risk of data loss and reducing monthly evaluation efficiency due to manual calculations. The aim of this research was to design an efficient information system for monitoring product quality at PT. Florindo Makmur, utilizing the Statistical Quality Control (SQC) method. The quality control monitoring system played a central role in gathering quality control data to support management decisions regarding product quality certainty. Therefore, obtaining monitoring information promptly was crucial to ensure products met quality standards and reduce rejected product quantities. The research approach included observation, interviews, and literature review as data collection strategies, while the system development method used was the waterfall method encompassing system requirement analysis, design, coding, and implementation. This information system enabled PT. Florindo Makmur to efficiently monitor its products by applying SQC concepts such as data analysis and creating control charts to swiftly identify improvements in product defects and take appropriate actions.
Implementation of K-Means Clustering in Recognizing Crime Hotspots and Traffic Issues Through GIS Pratama, Aryo; Irawan, Muhammad Dedi; Andriana, Septiana Dewi
Journal of Computer Networks, Architecture and High Performance Computing Vol. 6 No. 2 (2024): Articles Research Volume 6 Issue 2, April 2024
Publisher : Information Technology and Science (ITScience)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47709/cnahpc.v6i2.3771

Abstract

The challenge of accurately identifying instances of crime and traffic issues has rendered the precise localization thereof difficult, thereby impeding the populace's access to information concerning areas of high risk and safety. Employing a Geographic Information System (GIS)-based mapping system utilizing the K-means clustering method, spatial data pertaining to crime and traffic concerns are grouped. The primary objective is to aid in the identification of high-risk areas concerning crime and traffic matters. The methodology employed in this study revolves around the application of the K-means clustering method to categorize spatial data relevant to crime and traffic issues. K-means clustering represents a non-hierarchical cluster analysis technique designed to partition data into multiple groups based on spatial similarities. Research findings elucidate that through the utilization of the K-means clustering method, three distinct sets of clusters predicated upon the intensity of crime and traffic issues emerge. Consequently, from these clustering outcomes, districts and specific locales falling within each cluster, denoted as moderately vulnerable (C1), vulnerable (C2), and highly vulnerable (C3), can be delineated. This system is poised to furnish recommendations to pertinent authorities for addressing areas exhibiting heightened intensity levels while concurrently facilitating the generation of reports and dissemination of information to the public via a dedicated website pertaining to areas at elevated risk of crime and traffic issues.