International Journal of Scientific & Technology Research

Home About Us Scope Editorial Board Blog/Latest News Contact Us
10th percentile
Powered by  Scopus
Scopus coverage:
Nov 2018 to May 2020


IJSTR >> Volume 9 - Issue 3, March 2020 Edition

International Journal of Scientific & Technology Research  
International Journal of Scientific & Technology Research

Website: http://www.ijstr.org

ISSN 2277-8616

Diagnostic Analysis Of Rice Productivity Using Classification Based On Shannon And Renyi Entropy

[Full Text]



Fajar Delli Wihartiko, Eneng Tita Tosida, Ruhul Amin



Shannon Entropy, Renyi Entropy, Diagnostic Analysis, Rice Productivity



Rice is the main commodity in Indonesia both for consumption and in terms of production. The increasing number of Indonesian population resulting in increased demand for rice is a problem that must be faced by the government to maintain national food stability. Currently Indonesian rice productivity data is available at the Central Statistics Agency and at the Ministry of Agriculture. The data is used in descriptive and diagnostic analysis. Descriptive analysis uses clustering, data visualization and entropy. The diagnostic process uses an entropy-based classification to see factors of production. The entropy function used is Shannon and Renyi Entropy. The results of using entropy in the description analysis show that production attributes have a higher level of uniformity. The results of entropy in the classification show that there are differences in the decision tree that results from Shannon and Renyi entropy. In this case Renyi Entropy has better accuracy..



[1] Maione, C.; Barbosa, R.M. Recent applications of multivariate data analysis methods in the authentication of rice and the most analyzed parameters: A review. Crit. Rev. Food Sci. Nutr. 2018, 1–12.
[2] Su, Y.; Xu, H.; Yan, L. Support vector machine-based open crop model (SBOCM): Case of rice production in China. Saudi J. Biol. Sci. 2017, 24, 537–547.
[3] Chung, C.L.; Huang, K.J.; Chen, S.Y.; Lai, M.H.; Chen, Y.C.; Kuo, Y.F. Detecting Bakanae disease in rice seedlings by machine vision. Comput. Electron. Agric. 2016, 121, 404–411.
[4] Maione, C.; Batista, B.L.; Campiglia, A.D.; Barbosa, F.; Barbosa, R.M. Classification of geographic origin of rice by data mining and inductively coupled plasma mass spectrometry. Comput. Electron. Agric. 2016, 121, 101–107.
[5] Mays DC, Boris A. Faybishenko, and Stefan Finsterle (2002). Information entropy to measure temporal and spatial complexity of unsaturated flow in heterogeneous media. Water Resources Research, VOL. 38, NO. 12, 1313, doi:10.1029/2001
[6] Prasetyo, A., Koestoer, R. H., & Waryono, T. (2016). Pola Spasial Penjalaran Perkotaan Bodetabek : Studi Aplikasi Model Shannon ’ S Entropy, 16, 144–160. Jurnal Pendidikan Geografi, Volume 16, Nomor 2, Oktober 2016
[7] Purvis B, Yong M, Darren R (2019)Entropy and its Application to Urban Systems.Entropy. 21, 56; doi:10.3390/e21010056
[8] Han & Kamber.2013.Data mining 3rd Edition.Elsevier.United States of America
[9] Bekker A, 2018. 4 Types of Data Analytics to Improve Decision-Making https://www.scnsoft.com/
[10] M. J. Zaki, Jr. W. Meira and W. Meira. Data mining and analysis: fundamental concepts and algorithms. Cambridge: Cambridge University Press, 2014.
[11] BPS. 2018. Ringkasan Eksekutif Luas Panen dan Produksi Beras di Indonesia 2018. ISSN / ISBN : 978-602-438-237-7
[12] Bromiley, P. A., & Thacker, N. A. (2010). Shannon Entropy , Renyi Entropy , and Information, (2004). Statistics and Segmentation Series (2008-001)
[13] Mathews JH, Kurtis DF. Numerical Methods using Matlab. New Jersey: Pearson Education International. 2004.