Main Article Content


Abstract. One of the tasks of the Indonesian Citrus and Subtropical Research Institute is research on crossing between citrus varieties to produce saplings with the best quality products through observation of the fruit produced. Because the amount of fruit production studied is very large, it requires a fast and accurate observation process, one of which is the clustering method of data mining. Observations were made using a clustering process or grouping Density Based Spatial Clustering Application with Noise (DBSCAN) on fruit characteristics that indicate quality. DBSCAN works by grouping data based on density, so that it is expected to find several data groups that are close to each other which shows the tendency of the quality of the observed fruit data as well as labeling outlays for data that are too far from the crowd. The results of the grouping will be analyzed to find out the number and characteristics of the groups formed where the results of the grouping are assessed using the Silhouette Coefficient method to determine the best parameter values. The results obtained in this study are obtained three group results which will be divided into medium quality, good, and not so good. The quality of grouping using the Silhouette Coefficient value of 0.69.

Article Details

How to Cite
Alqorni, F., Mahmudy, W. F., & Widodo, A. W. (2021). Application of Density Based Spatial Clustering Application With Noise (DBSCAN) in Determining the Quality of Keprok Orange and Siam Orange Hybrid in the Research Center of Orange and Subtropic Plants Batu City. Journal of Information Technology and Computer Science, 6(1), 1–8.


  1. Litbang. (2019). Badan Litbang Pertanian. Retrieved February 28, 2020, from
  2. Suyanto. (2017). Data Mining untuk Klasifikasi dan Klasterisasi Data (1st ed.). Bandung: Penerbit Informatika.
  3. Ester, M., Kriegel, H.-P., & Xu, X. (1996). A density-based algorithm for discovering clusters in large spatial databases with noise. Atlantic: AAAI Press.
  4. Alfiyatin, A., Mahmudy, W., & Anggodo, Y. (2018). K-Means Clustering and Genetic Algorithm to Solve Vehicle Routing Problem with Time Windows Problem. Indonesian Journal of Electrical Engineering and Computer Science, 11(2), 462-468.
  5. Auliya, Y., Mahmudy, W., & Sudarto. (2019). Improve Hybrid Particle Swarm Optimization and K-Means for Clustering. Journal of Information Technology and Computer Science, 4(1), 42-56.
  6. Endarto, O., & Martini, E. (2016). Budidaya Jeruk Sehat. Bogor: Balai Penelitian Tanaman Jeruk dan Subtropika (Balitjestro).
  7. Hasibuan, H., S, F. R., Suprima, R., Rasul, M., & Saputra, B. (2014). Persilangan (Hibridasi). Jurnal Praktikum Pemuliaan Hibrida, I(2), 1-6.
  8. Lado, J., Rodrigo, M. J., & Zacarias, L. (2014). Maturity Indicators and citrus fruit quality. Stewart Postharvest Review, 2(2), 1-6.
  9. Abouzari, A., & Nezad, N. M. (2016). The Investigation of Citrus Fruit Quality Popular Characteristic and Breeding. Acta Universitatis Agriculturae Et Silviculturae Mendelianae Brunensi, 64(3), 726 - 740.
  10. Kaufmann, M. (2012). Data Mining Concepts and Techniques (3rd ed.). Waltham: Elsevier.
  11. Duda, R. O., Hart, P. E., & Stork, D. G. (2000). Pattern Classification (2nd ed.). New York: Wiley-Interscience.
  12. Mohammad, I., & Usman, D. (2013). Standardization and Its Effects on K-Means Clustering Algorithm. Research Journal of Applied Sciences, Engineering and Technology, 6(17), 3299-3303.
  13. Patro, S. K., & Sahu, K. K. (2015, April 01). ResearchGate. Retrieved 03 18, 2020, from
  14. Wilson, D. R., & Martinez, T. R. (1997). Improved Heterogeneous Distance Functions. Journal of Artificial Intelligence Research, 6(1), 1-34.
  15. Spencer, M. R., Prins, S. C., & Beckom, M. S. (2010). Heterogeneous Distance Measures and Nearest-Neighbor Classification in an Ecological Setting. Missouri Journals of Mathematic and Science, 22(2), 108-123.
  16. Kettleborough, G., & Rayward-Smith, V. (2013). Optimising sum-of-squares measures for clustering multisets defined over a metric space. Discrete Applied Mathematics, 161(16-17), 2499-2513.
  17. Cady, F. (2017). The Data Science Handbook. New Jersey: John Wiley & Sons, Inc.