The Big Data Commodity Management Model for Rice for National Food Policy

Model Manajemen Big Data Komoditas Beras untuk Kebijakan Pangan Nasional

  • Eneng Tita Tosida IPB University
  • Fajar Delli Wihartiko IPB university
  • Irman Hermadi IPB university
  • Yani Nurhadryani Institut Pertanian Bogor
  • Feriadi
Keywords: Big Data Analytics, Clustering, Classification, National Rice Commodities, Food Security

Abstract

Rice is the main commodity in Indonesia, both for consumption and production. Rice production data are available at the Badan Pusat Statistika and at Kementrian Pertanian. The data is used to build a large data management model for Indonesia's rice trade. The model development strategy is done through analyzing agriculture big data analytic that is equipped with descriptive analysis, evaluation, predictive and prescriptive. The models and designs that are built discuss business processes, stakeholder networks and network management. Descriptive analysis results in the form of grouping and visualization of rice data. The results of the diagnostic process using classification approach produce a decision tree to see the results of the level of production in a province. In the predictive process produces a linear regression model to predict the results of the following year's production as well as in the analysis.

Downloads

Download data is not yet available.

References

McAfee, A. and Brynjolfsson E., 2012. Big data: the management revolution. Harvard business review, 2012(90): p. 60-6, 68, 128.

Fang, H., Zhang, Z., Wang, C. J., Daneshmand, M., Wang, C., and Wang, H., 2015. A survey of big data research. IEEE network, 29(5), 6–9. doi:10.1109/MNET.2015.7293298

Kung, H.-Y., Kuo, T.-H., Chen, C.-H., and Tsai, P.-Y,. 2016. Accuracy Analysis Mechanism for Agriculture Data Using the Ensemble Neural Network Method. Sustainability 2016, 8, 735.

Moshou, D., Bravo, C., West, J., Wahlen, S., McCartney, A., and Ramon, H., 2004. Automatic detection of “yellow rust” in wheat using reflectance measurements and neural networks. Comput. Electron. Agric., 44, 173–188.

Moshou, D., Bravo, C., Oberti, R., West, J., Bodria, L., McCartney, A., and Ramon, H., 2005. Plant disease detection based on data fusion of hyper-spectral and multi-spectral fluorescence imaging using Kohonen maps. Real-Time Imaging, 11, 75–83.

Su, Y., Xu, H., and Yan, L., 2017. Support vector machine-based open crop model (SBOCM): Case of rice production in China. Saudi J. Biol. Sci., 24, 537–547.

Chung, C.L., Huang, K.J., Chen, S.Y., Lai, M.H., Chen, Y.C., and Kuo, Y.F., 2016. Detecting Bakanae disease in rice seedlings by machine vision. Comput. Electron. Agric., 121, 404–411.

Maione, C., Batista, B.L., Campiglia, A.D., Barbosa, F., and Barbosa, R.M., 2016. Classification of geographic origin of rice by data mining and inductively coupled plasma mass spectrometry. Comput. Electron. Agric., 121,101–107.

Drake, University, 2011. "What is a food policy?". State and Local Food Policy Councils. Iowa Food Policy Councils. February 2011.

Azahari, DH, and Hadiutomo, K., 2019. Analisis Keunggulan Komparatif Beras Indonesia. Pusat Sosial Ekonomi dan Kebijakan Pertanian. Ditjjen Pengolahan, Pemasaran Hasil Pertanian. http:/repository.pertanian.go.id.

Hakim DB., Harianto, H, dan Nurmalina, R., 2019. Analisis Dampak Kebijakan Beras Sejahtera dan Kebijakan Program Bantuan Non Tunai terhadap Titik Ekuilibrium Rumahtangga Miskin Di Indonesia. JEPA, 3 (4) : 799-808. http:/jepa.ub.ac.id

Handayani SW., Kunarti, S., 2018. The Dinamics of Paddy Land Legal Policy in Ramlan Indonesia. SHS Web of Conferences 54, 03009. http:/shs-conferences.org

Lopulilsa CA., and Suryani, I., 2018. The Emerging Roles of Agricultural Insurance and Farmers Cooperatives on Sustainable Rice Productions in Indonesia. IOP Conf. Series : Earth and Environmental Science 157 (1), 012070.

Makbul Y., Ratnaningtyas S, Pradono., 2019. Integrating of Rice Prices at Producer, Wholesaler, and Urban – Rural Customer Markets with Paddy Prices at The Farm Gate. Achieve of Business Research 7 (3). http:/researchgate.net.

Perdinan P., Dewi, NWSP., and Dharma, AW., 2018. Lesson Learnt from Smart Rice Actions in Indonesia. Future of Food : Journal on Food, Agriculture and Society 6 (2) : 9-20. http/:fofj.org

Putri, DR., Hayatudin, A., and Ibrahim, MA., 2019. Tinjauan Penerapan Konsep Maslahah Mursalah terhadap Kebijakan Impor Beras di Indonesia. Prosiding Hukum Ekonomi Syariah,32-39.

Wardani, C., Jamhari, Hardyastuti, S., and Suryantini, A. 2019. Kinerja Ketahanan Beras di Indonesia : Komparasi Jawa dan Luar Jawa Periode 2005-2017. Jurnal Ketahanan Nasional, 25 (1), 107-131. http:/journal.ugm.ac.id

Wolfert, S.,, Lan Ge., Cor, V., and Marc-JB., 2017. Review Big Data in Smart Farming – A review. Agricultural Systems 153 (2017) 69–80.

Jennex, M.E., 2009. Re-visiting the knowledge pyramid. In 2009 42nd Hawaii International Conference on System Sciences (pp. 1-7). IEEE.

Big Data Predictive and Prescriptive Analytics Ganesh Chandra Deka (Government of India, India)., 2016. Source Title: Big Data: Concepts, Methodologies, Tools, and Applications. Copyright: © 2016. |Pages: 26 DOI: 10.4018/978-1-4666-9840-6.ch002

Lambert, D.M., and Cooper, M.C. 2000. Issues in Supply Chain Management. Ind. Mark. Manag. 29, 65-83.

Chen, M., Mao, S., and Liu, Y., 2014. Big Data: a survey. Mobile Netw Appl 19, 171–209.

Miller, H.G., Mork, P., 2013. From data to decisions: a value chain for Big Data. IT Professional 15, 57–59

Bekker, A, 2018. 4 Types of Data Analytics to Improve Decision-Making https://www.scnsoft.com/blog/4-types-of-data-analytics

Bohanec, M., Kljaji´c Borštnar, M., and Robnik-Šikonja, M., 2017. Explaining machine learningmodels in sales predictions.

Davenport, T.H., and Short, J.E., 1990. The new industrial-engineering - information technology and business process redesign. Sloan Management Review 31, 11–27.

Davenport, T.H., 1993. Process Innovation: Reengineering Work through Information Technology. Harvard Business School Press, Boston, Massachusetts.

Porter, M.E., 1985. Competitive Advantage: Creating and Sustaining Superior Performance. 1985. FreePress, New York.

Beer, S., 1981. Brain of the Firm. second ed. John Wiley, London and New York.

Bantacut T and Fadhil, R., 2018. LOGISTIK 4.0 dalam Manajemen Rantai Pasok Beras Perum BULOG. Jurnal Pangan 27 (2), 141-154. http;/jurnalpangan.com

Gates, M., 2017. Blockchain : Ultimate guide to understanding blockchain, bitcoin, cryptocurrencies, smart contracts and the future of money. Wise Fox Publishing and Mark Gates.

Kamilaris A, Fonts, A., Prenafeta-Boldu, FX., 2019. The Rice of Blockchain Technology in Agriculture and Food Supply Chains. Trends in Food Science & Technology 91, 640-652. http:/sciencedirect.com

Fonseca, CM., and Fleming, PJ., 1993. Genetic Algorithms for Multiobjective Optimization : Formulation, Discussion and Generalization. Genetic Algorithms: Proceedings of the Fifth International Conference (S. Forrest, ed.), San Mateo, CA: Morgan Kaufmann.

Alexandre H, Dias, F., and de Vasconcelos, JA.,. 2002. Multiobjective Genetic Algorithms Applied to Solve Optimization Problems. IEEE Transactions On Magnetics, Vol. 38, NO. 2, March 2002, 1133-1136.

Wang, Chia-Nan, Nguyen, VT., Duong, DH., and Do, HT., 2018. A Hybrid Fuzzy Analytic Network Process (FANP) and Data Envelopment Analysis (DEA) Approach for Supplier Evaluation and Selection in the Rice Supply Chain.

Ray, D., 2015. Jharkhand readies ‘Smart Village’ scheme diakses https://timesofindia.indiatimes.com/india/Jharkhand-readies-smartvillageschemedraft/ articleshow/48581670. cms the times of india.

Holmes, J., Claudia, C., Chiurugwi, T., Cruickshank, H., Evans., S., Fennel, S., Hayhurst, R., Heap, B., Holmes, Hurley-Dépret, M., Jones, B., Mutschler, R, Patel, N., Polman, K., Prabhu, J., Price, M., Safdar, T., Thomas, M., Terry van Gevelt, Welland, A., and Zhang, Y., 2017. The Smart Villages Initiative: Findings 2014-2017. Diakses melalui http://www.interacademies.org/File. aspx?id=49151&v=c09dc792.

Sagar, BM., and Cauvery, NK ., 2018. Agriculture Data Analytics in Crop Yield Estimation: A Critical Review. Indonesian Journal of Electrical Engineering and Computer Science. Vol 12. No. 3 December 2018, pp. 1087-1093.

Published
2020-02-20
How to Cite
Tosida, E. T., Wihartiko, F. D., Hermadi, I., Yani Nurhadryani, & Feriadi. (2020). The Big Data Commodity Management Model for Rice for National Food Policy. Jurnal RESTI (Rekayasa Sistem Dan Teknologi Informasi), 4(1), 142 - 154. https://doi.org/10.29207/resti.v4i1.1520
Section
Information Technology Articles