Grouping 2 Hour Load Data Substation Using Hierarchical Clustering

Abstract

In this modern era, information technology is very influential in many ways, especially in the business and business world. Computer is a device that is needed for data processing, so the data processed can be effectively and efficiently to provide information needed by individuals, companies, and agencies. Clustering is a method for finding and grouping data that has similarity between one data and others. In the clustering algorithm, data will be grouped into clusters based on the similarity of one data to another. The principle of clustering is to maximize the similarity between members of one cluster and minimize similarities between different cluster members. The most well-known category of clustering algorithms is Hierarchical Clustering. As well as PLN (State Electricity Company), where PLN is a state-owned enterprise (BUMN) that provides electricity services to all communities and all circles, both private and business entities whose job it is to serve users in electricity. Services will be maximized and can be accessed by all parties if in the service there is a role for the media as a assistant in conveying information to all circles. This information will be right on the target if the information is processed from a reliable source. PT PLN (Persero) Krueng Geukuh did not yet have a load data collection system that makes staff still did data collection by manual methods. The result of this study shows the system of 2 hour load cluster data grouping using hierarchical clustering of PT PLN (Persero) Krueng Geukuh is an effective solution to help staff record computerized loads and can increasing the work performance in data collection. Designing systems of Grouping Data Load 2 Hours Substation Using Hierarchical Clustering PT PLN (persero) Krueng Geukuh was built by using PHP so it has a more attractive appearance and uses a MySQL database so that load managers become more effective.

Evaluation of ATM Location Placement Using the K-Means Clustering in BNI Denpasar Regional Office

“…Therefore, clustering is very useful because it can discover various previously unknown groups in the data. Clustering has been widely used in multiple applications such as business intelligence, image pattern recognition, image pattern recognition, web search, biology, and security (Rasyid & Andayani, 2018;Yusmartato et al, 2019).…”