Candra Dewi, Muhammad Syaifuddin Zuhri, Achmad Basuki, Budi Darma Setiawan


To maintain the availability of the patchouli plants required monitoring the spread of patchouli plantation. This study performed the identification of patchouli plant through Landsat-8 satellite imagery and Improved K-Means method. Improved was done on this study include the process of determining the initial cluster by specifying the closeness between the data and the determination of the number of cluster (K) by using the histogram equalization technique. The result of internal criteria testing shows that determining the number of clusters using the histogram is less effective because it produces the lower value of the silhouette. On almost all image data test found the best value of the silhouette's coefficient is 75.089% at K=2 and data in February. Furthermore, based on the results of testing the external criteria known the highest purity value in February data with a number of cluster 5 is 0.6829268. The test results also show that the use of the Improved K-Means on the Landsat-8 image has not been able to recognize the difference patchouli plants with other crops due to the limited resolution of imagery data and also the minimum number and variation of test data. But, visually the patchouli plant cluster is found for February data while the age of the rice crop surrounding the patchouli is still in the early phase of planting.

Full Text:



AYECH, M. W., & ZIOU, D. (2016). Terahertz image segmentation using k-means clustering based on weighted feature learning and random pixel sampling. Neurocomputing, Volume 175, Part A, 243–264.

DANOEDORO, P. (2012). Pengantar Peninderaan Jauh Digital. Yogyakarta: Andi.

DEEPA, M., & REVATHY, P. (2012). Validation of Document Clustering based on Purity and Entropy Measures. International Journal of Advanced Research in Computer and Communication Engineering Vol. 1, Issue 3, 147-152.

DIRECTORATE GENERAL OF PLANTATIONS, MINISTRY OF AGRICULTURE.(2013). Luas Areal Perkebunan Angka Estimasi Tahun 2013. Accessed March 24, 2015, from http://

GONZALEZ, R. C., & WOODS, R. E. (2008). Digital Image Processing 3rd Edition. New Jersey, USA: Pearson Prentice Hall.

KARDINAN, A. (2005). Tanaman Penghasil Minyak Atsiri. AgroMedia.

KHANMOHAMMADI, S., ADIBEIG, N., & SHANEHBANDY, S. (2017). An Improved overlapping k-means clustering method for Medical applications. Expert Systems With Applications, Vol. 67, 12-18.

KOGAN, J. (2006). Introduction to Clustering Large and High-Dimensional Data 1st Edition. Cambridge University Press.

LILLESAND, T. M., KIEFER, R. W., & CHIPMAN, J. (2008). Remote Sensing and Image Interpretation 6th Edition. USA: John Wiley & Sons.

MACQUEEN, J. (1967). Some Methods for classification and Analysis of Multivariate Observations. 5-th Berkeley Symposium on Mathematical Statistics and Probability (hal. 281-297). Berkeley: University of California Press.

MINISTRY OF AGRICULTURE. 2015. Bahan Baku Dunia, Tapi RI Masih Impor Parfum. Acessed March 24, 2015, from,-Tapi-RI-Masih-Impor-Parfum

SUN, P., XIE, D., ZHANG, J., ZHU, X., WEI, F., & YUAN, Z. (2014). Tempora-Spatial-Probabilistic Model Based For Mapping Paddy Rice Using Multi-Temporal Landsat Images. Geoscience and Remote Sensing Symposium (IGARSS). IEEE International.

XIAO, X., BOLES, S., LIU, J., ZHUANG, D., FROLKING, S., LI, C.. (2005). Mapping paddy rice agriculture in southern China using multi-temporal MODIS images. Remote Sensing of Environment, Volume 95, Issue 4, 480–492.

YAO, H., DUAN, Q., LI, D., & WANG, J. (2013). An improved K-means clustering algorithm for fish image segmentation. Mathematical and Computer Modelling, Volume 58, Issues 3–4 , 790–798.



  • There are currently no refbacks.

Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 International License.