Data Mining Technology Used in an Internet of Things-Based Decision
Support System for Information Processing Intelligent Manufacturing
Dublin Core
Title
Data Mining Technology Used in an Internet of Things-Based Decision
Support System for Information Processing Intelligent Manufacturing
Support System for Information Processing Intelligent Manufacturing
Subject
Data Mining; IoT; ARS; Apriori Algorithm; Decision Support Systems
Description
In recent years, database technology has improved significantly, and database management systems have gained widespread
adoption. As a result, the volume of data saved across numerous databases has increased exponentially. However, the vast
majority of information is hidden beneath this mountain of data. The goal of this study is to get a comprehensive understanding
of the decision information system employed in the Internet of Things for intelligent manufacturing data processing. The
proposed Decision support system (DSS) information processing is accomplished through the use of an IoT-based intelligent
manufacturing data mining model. Numerous DM algorithms that are frequently encountered are analyzed, including the ARS
and Apriori Algorithm (AA). The Decision Tree data mining algorithm is investigated, as is the generation of several Decision
Trees and the pruning algorithm for digital twins. The findings demonstrate that data mining technology is capable of analyzing
statistical data from a variety of angles and perspectives by modeling, classifying, and grouping large amounts of data as well as
discovering correlations between them. Additionally, statistical work involves the calculation of data and the use of their
correlations to aid in decision analysis. The proposed theoretical framework demonstrates how DSS-integrated components can
work cooperatively in Intelligent Manufacturing to define a stable data flow within the Internet of Things. Particular emphasis is
placed on conceptualizing the decision support system's integrated performance.
adoption. As a result, the volume of data saved across numerous databases has increased exponentially. However, the vast
majority of information is hidden beneath this mountain of data. The goal of this study is to get a comprehensive understanding
of the decision information system employed in the Internet of Things for intelligent manufacturing data processing. The
proposed Decision support system (DSS) information processing is accomplished through the use of an IoT-based intelligent
manufacturing data mining model. Numerous DM algorithms that are frequently encountered are analyzed, including the ARS
and Apriori Algorithm (AA). The Decision Tree data mining algorithm is investigated, as is the generation of several Decision
Trees and the pruning algorithm for digital twins. The findings demonstrate that data mining technology is capable of analyzing
statistical data from a variety of angles and perspectives by modeling, classifying, and grouping large amounts of data as well as
discovering correlations between them. Additionally, statistical work involves the calculation of data and the use of their
correlations to aid in decision analysis. The proposed theoretical framework demonstrates how DSS-integrated components can
work cooperatively in Intelligent Manufacturing to define a stable data flow within the Internet of Things. Particular emphasis is
placed on conceptualizing the decision support system's integrated performance.
Creator
Ahmad Sulhi
Date
2021
Contributor
PERI IRAWAN
Format
PDF
Language
ENGLISH
Type
TEXT
Files
Collection
Citation
Ahmad Sulhi, “Data Mining Technology Used in an Internet of Things-Based Decision
Support System for Information Processing Intelligent Manufacturing,” Repository Horizon University Indonesia, accessed June 6, 2025, https://repository.horizon.ac.id/items/show/9272.
Support System for Information Processing Intelligent Manufacturing,” Repository Horizon University Indonesia, accessed June 6, 2025, https://repository.horizon.ac.id/items/show/9272.