@article{MAKHILLRJAS201914410170,
    title = {Implementation of k-Means Clustering Algorithm based on Rice Productivity
Level in Subdistrict Area (Case Study: In Special Region of Yogyakarta)},
    journal = {Research Journal of Applied Sciences},
    volume = {14},
    number = {4},
    pages = {110-116},
    year = {2019},
    issn = {1815-932x},
    doi = {rjasci.2019.110.116},
    url = {https://makhillpublications.co/view-article.php?issn=1815-932x&doi=rjasci.2019.110.116},
    author = {Indrawan,Muhammad and},
    keywords = {Clustering,regional grouping,k-means,rice productivity,data mining process,elbow method},
    abstract = {The need for people to consume rice continues to increase which causes the price of rice in the market
to rise and fall in the Yogyakarta Special Region while the government still lacks attention to rice production
in each year. This research focuses on making regional groupings in Yogyakarta Special Region based on the
level of rice productivity. Data mining process is done by clustering using k-means to classify sub-districts
based on production data and land area. Evaluation of the results of clusters uses elbow method and
comparison of cluster results with other programs. Regional grouping based on the level of rice productivity
produces 3 optimal clusters which are divided into low productivity, medium productivity and high
productivity. With the results of this study, it is expected to help the Yogyakarta agricultural service in an effort
to increase rice productivity more evenly in each region.}
    }