How to cite (IEEE): E. Emigawaty, K. Adi, A. Rochim, B. Warsito, & A. Wibowo "K-Means Clustering Algorithm for Partitioning the Openness Levels of Open Government Data Portals," JOIV : International Journal on Informatics Visualization, vol. 7, no. 3, , pp. 977-983, Sep. 2023. https://doi.org/10.30630/joiv.7.3.1761

How to cite (APA): Emigawaty, E., Adi, K., Rochim, A., Warsito, B., & Wibowo, A. (2023). K-Means Clustering Algorithm for Partitioning the Openness Levels of Open Government Data Portals. JOIV : International Journal on Informatics Visualization, 7(3), 977-983. https://doi.org/10.30630/joiv.7.3.1761

How to cite (Chicago): Emigawaty, Emigawaty, Adi, Kusworo, Rochim, Adian Fatchur, Warsito, Budi, AND Wibowo, Adi. "K-Means Clustering Algorithm for Partitioning the Openness Levels of Open Government Data Portals" JOIV : International Journal on Informatics Visualization [Online], Volume 7 Number 3 (10 September 2023) https://doi.org/10.30630/joiv.7.3.1761

How to cite (Vancouver): Emigawaty E, Adi K, Rochim A, Warsito B, & Wibowo A . K-Means Clustering Algorithm for Partitioning the Openness Levels of Open Government Data Portals. JOIV : International Journal on Informatics Visualization [Online]. 2023 Sep; 7(3):977-983. https://doi.org/10.30630/joiv.7.3.1761

How to cite (Harvard): Emigawaty, E., Adi, K., Rochim, A., Warsito, B., & Wibowo, A. ,2023. K-Means Clustering Algorithm for Partitioning the Openness Levels of Open Government Data Portals. JOIV : International Journal on Informatics Visualization, [Online] 7(3), pp. 977-983. https://doi.org/10.30630/joiv.7.3.1761

How to cite (MLA8): Emigawaty, Emigawaty, Kusworo Adi, Adian Fatchur Rochim, Budi Warsito, & Adi Wibowo. "K-Means Clustering Algorithm for Partitioning the Openness Levels of Open Government Data Portals." JOIV : International Journal on Informatics Visualization [Online], 7.3 (2023): 977-983. Web. 2 Oct. 2023 , https://doi.org/10.30630/joiv.7.3.1761

BibTex Citation Data :

@article{JOIV1761,
    	author = {Emigawaty Emigawaty and Kusworo Adi and Adian Fatchur Rochim and Budi Warsito and Adi Wibowo},
    	title = {K-Means Clustering Algorithm for Partitioning the Openness Levels of Open Government Data Portals},
    	journal = {JOIV : International Journal on Informatics Visualization},
    	volume = {7},
    	number = {3},
    	year = {2023},
    	keywords = {K-Means; Clustering; Open Government Data; Portals},
    	abstract = {More and more local governments in Indonesia are making their data available to the public. This benefits data scientists, researchers, business owners, and other potential users seeking datasets for empirical research and business innovation. However, just because Open Government Data (OGD) portals are accessible does not mean that they necessarily adhere to the established rules and principles of data openness. To evaluate the level of openness of 24 OGD portals in Indonesia, this study used the K-means Clustering algorithm to partition them into three levels: Leaders, Followers, and Beginners. A group of 30 participants, including researchers, data scientists, business enablers, and graduate students, rated the portals on 32 sub-questions related to the eight main principles of data disclosure, focusing on health, population, and education datasets. The study found that eight portals were categorized as Leaders, ten as Followers, and seven as Beginners regarding their level of openness. The study demonstrated that the K-means Clustering algorithm can be effectively used to assess the degree of openness of OGD portals in Indonesia based on eight main principles of data openness. The study recommends increasing the number of OGD portals in eastern territories to supplement the existing case studies in the western and central regions.},
        	issn = {2549-9904},    	pages = {977--983},    	doi = {10.30630/joiv.7.3.1761},
        	url = {https://joiv.org/index.php/joiv/article/view/1761}
    }
    

Refworks Citation Data :

@article{{JOIV}{1761}, author = {Emigawaty, E., Adi, K., Rochim, A., Warsito, B., Wibowo, A.}, title = {K-Means Clustering Algorithm for Partitioning the Openness Levels of Open Government Data Portals}, journal = {JOIV : International Journal on Informatics Visualization}, volume = {7}, number = {3}, year = {2023}, doi = {10.30630/joiv.7.3.1761}, url = {} }