@article{MAKHILLJEAS2019141518164,
    title = {Power Plant Clustering in Indonesia by using k-Means},
    journal = {Journal of Engineering and Applied Sciences},
    volume = {14},
    number = {15},
    pages = {5123-5129},
    year = {2019},
    issn = {1816-949x},
    doi = {jeasci.2019.5123.5129},
    url = {https://makhillpublications.co/view-article.php?issn=1816-949x&doi=jeasci.2019.5123.5129},
    author = {Purba},
    keywords = {Power plant,installed capacity,electricity,k-means clustering,Indonesia,several findings},
    abstract = {There are two problems in Indonesian electricity statistical report that are published by the Ministry
of Energy and Mineral Resources. First, most of these reports are presented in tabular form. Second, these
reports are independent and have not been related to other data, yet such as geographic and demographic data.
So, these reports are still difficult to be analyzed. In this research, we proceed and analyze the Indonesian power
plant installed capacity data that is published by the Ministry of Energy and Mineral Resources. This data then
is combined with the demographic and geographic data. The main research is to mapping the distribution of
the installed capacity of power plants based on provinces. In this research, we use k-means clustering as the
basis clustering method. The analyzed data is the installed capacity of the PLN’s power plants, both they are
owned or rented. Based on the clustering result there are several findings in installed capacity of power plants
in Indonesia. First, there are significant gap between provinces in the island of Java and provinces outside the
island of Java. PLN’s owned power plants dominate in Java while PLN’s rented power plants dominate in outer
Java. DI Yogyakarta is not only behind any provinces in the island of Java but also behind lots of provinces
in Indonesia provinces in Sumatera are promising to competing East Java and Central Java.}
    }