• Sri Redjeki


DSS, Fuzzy Inference, Poverty alleviation fund, Tsukamoto method.


According to BPS (Statitics Indonesia), the number of poor citizens – citizens whose monthly per capita income is lower than the poverty line - in Indonesia in March 2016 was reaching 28,01 million of people or equivalent to 10,86 percent of the total population in Indonesia. The problem must be resolved by a systematic and structured immediately. By innovation in information technology systems so the poverty reduction can be measured with good response. This research attempts to provide a framework soft computing that can optimize decision
support system (DSS) to reduce poverty. Fuzzy inference by Tsukamoto method can be an alternative method to identificate and clustering poverty alleviation fund from government or private parties. This framework is integrated with spatial data poor families, with the location input and mapping as a feature for public audit. The result of this framework has been used by the government in Bantul District, Indonesia, for assisting distribution of poverty alleviation fund to the right people..


Alexander G.Flor, “ ICT and Poverty : The Indisputable Link”, Paper for Third Asia Development Forum on Regional Economic Cooperation in Asia and the Pasificorganised by Asian Development Bank, Bangkok, 11-14 June 2001.

Andy Sumner, Asep Suryahadi and Nguyen Thang, Poverty And Inequalities In Middle-Income Southeast Asia, 1, 3 April 2012

Cahyat, Ade, 2004, How is poverty measured? Some Model Calculation of Poverty in Indonesia, Governance Brief, CIFOR, No.2.

Chakravarty, S. R. (2006). ?An Axiomatic Approach to Multidimensional Poverty Measurement via Fuzzy Sets‘, in A. Lemmi and G. Betti (eds.), Fuzzy Set Approach to Multidimensional Poverty Measurement. Springer, 49–72.

Femi, Astuti, Fuzzy C-Means Implementation For Poverty People Clustering (Case Study: Kecamatan Bantul), Telkomatika, Vol 9, No 1 Juli 2016, ISSN: 1979-7656.

Foster et al. (2013): Foster, J. E., Seth S., Lokshin, M., and Sajaia Z. (2013). A Unified Approach to Measuring Poverty and Inequality: Theory and Practice. The World Bank.

Haughton, J. and Khandker, S. R. (2009) Handbook on Poverty and Inequality, The World Bank, Washington, D.C.

Hulme, D. and Shepherd, A. (2003) ‘Conceptualizing chronic poverty’, World Development 31(3): 403-423.

Li, Yimin. Yin, Haihong. Liu, Suhong. Relocation selection for poverty alleviation: Factor analysis and GIS modeling, June 2011, Volume 8, Issue 3, pp 466– 475 Journal of Mountain Science, Springer.

Redjeki,Sri. Anggoro,Pius. Guntara,M. Buwono, Robby Cokro, Selection and Monitoring System Poverty Alleviation Fund, Prosiding SENATKOM, 23 Oktober 2015, ISSN : 2460-4690

Redjeki, Sri, M. Guntara, and Pius Anggoro. "Naive Bayes Classifier Algorithm Approach for Mapping Poor Families Potential.", IJARAI, Volume 4 Issue 12, ISSN : 2165-4069 (online), ISSN : 2165-4050 (Print), A Publications of The Science and Information Organization.

Singhala, P., Shah, D. N., & Patel, B. (2014). Temperature Control using Fuzzy Logic. International Journal of Instrumentation and Control Systems (IJICS), 4(1), 1–10.

Sumarto, Sudarno. De Silva,Indunil. The SMERU Research Institute, TNP2K Beyond the Headcount: Examining the Dynamics and Patterns of Multidimensional Poverty in Indonesia, MPRA Paper No. 60379, 11. December 2014.

Widyanti, W., Suryahadi, A., Sumarto, S. and Yumna, A. (2009) ‘The relationship between chronic poverty and household dynamics: evidence from Indonesia', SMERU Working Paper, SMERU Institute, Jakarta.

Turban , Efraim., Decision Support and Business Intelligence Systems [et al.].8th ed.p.cm.2007

Zedini, Asma. Belhadj, Besma. Modeling uncertainty in monetary poverty: A possibility-based approach, Fuzzy Sets and Systems, Volume 293, 15 June 2016, Pages 113–126.

BPS, 2014, Data dan Informasi Kemiskinan Kabupaten/Kota 2014, url: https://www.bps.go.id/index.php /publikasi/1174

Bapenas, 2015, Kondisi Penanggulangan Kemiskinan Saat Ini, url: www.bappenas.go.id/index.php/download_file/ view/15712/4642/





How to Cite

Redjeki, S. (2020). A FRAMEWORK SOFT COMPUTING IN DSS FOR ALLEVIATION OF POVERTY OPTIMALIZATION IN INDONESIA. Proceedings of the Informatics Conference, 3(5), 1-5. Retrieved from https://ojs.journals.unisel.edu.my/index.php/icf/article/view/36



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