Repository Universitas Pakuan

Detail Karya Ilmiah Dosen

Eneng Tita Tosida, Sufiatul Maryana, Hermawan Thaheer, Okta Hairlangga

Judul : CLASSIFICATION OF INDONESIAN TELEMATIC SERVICES MSMEs FEASIBILITY ASSISTANCE,USINGJ 48 ALGORITHM

Abstrak :
Feasibility assistance for Micro, Small and Medium Enterprises (SMEs) Indonesia telematics services need to be assessed for compliance. suitability parameters assistance can be seen from the characteristics of MSMEs. Telematics Services MSMEs characteristic data fully available on the National Economic Census data (Susenas). Optimization of Susenas as the basis for the feasibility study can be done through the implementation of a decision tree approach particularly algorithm J 48. The use ofJ 48 algorithms to determine the feasibility of Indonesian telematics services MSMEs capable of providing an alternative way to describe the characteristics of MSMEs are eligible to receive assistance. This is particularly useful for ministries such as the Ministry of Cooperatives and SMEs, in terms of selecting eligible SMEs are given assistance. Criteria for aid feasibility is based on the general condition of SMEs, access to information technology, economic conditions, partnerships and development plans. Evaluation of the performance of the system showed that the algorithm J 48 is able to achieve an accuracy of 64% on the training data and at 57% on the test data, resulting in a decision rule that representative. The low accuracy is caused by data that is not balanced, so it is a potential for the development of further research involving balancing data.

Tahun : 2017 Media Publikasi : Seminar Internasional

Kategori : Prosiding No/Vol/Tahun : - / 12 / 2017

ISSN/ISBN : 978-602-60736-7-9

PTN/S : Universitas Pakuan Program Studi : ILMU KOMPUTER

Bibliography :
REFERENCES

[1] E.T. Tosida, S Maryana, H Thaheer, F.A. Darmin, “Visualization model of small and medium enterprises (SMEs) telematics services potentiality map in Indonesia”, inProc. of 2015 International Conference on Information & Communication Technology and Systems (ICTS),Added to IEEExplorer January 14th 2016,doi : 10.1109/ICTS.2015.7379890, 2015.

[2] E.T. Tosida, S. Maryana, H. Thaheer, Hardiani, “Implementation of Self Organizing Map (SOM) as decision support : Indonesian telematics services MSMEs empowerment”, IOP Conf. Series : Material Science and Engineering 166 012017. doi:10.1088/1757-899X/166/1/012017, IORA-ICOR, August 27th 2017, IORA IOP Publishing, 2017.

[3] H.S.Kim, S.Y.Sohn,“Support vector machines for default prediction of smes based on technology credit”,European Journal of Operational Research 201 (3) : 838-846. Elsevier. doi:10.1016/j.ejor.2009.03.036, 2010.

[4] S.Y.Sohn, J.W.Kim,“Decision tree-based technology credit scoring for strart-up firms : Korean case”,J. Expert Systems with Applications 39 (4) : 4007-40112, Elsevier, doi:10.1016/j.eswa. 2011.09.075, 2012.

[5] Y.Y. Wibisono, “Performance Comparation of Decision Tree Algorithm, C5.0, CART, Dan CHAID: Case Status Credit Risk Prediction In Bank X”, Thesis,Industry Engineering Department, Katolik Parahyangan University,2007.

[6] J.Winesett, Web Application Development with Yii and PHP, Packt Publishing, Birmingham, Inggris, 2012.

[7] J. Han, M.Kamber, J. Pei, Data mining : Concepts and techniques. Third Edition. Morgan Kaufmann is an imprint of Elsevier, 225Wyman Street,Waltham, MA 02451, USA, 2012.

8]M. Sourbati,“The digital switchover as an information society initiative: The role of public policy in promoting access to digital ICTs”, J.Telematics and Informatics,Vol 28, Issue 4, pp 295–30. Nov 2011.

http://dx.doi.org/10.21107/kursor.v8i2.1299, Dec. 2015.

[10]M.J. Zaki, W.Meira Jr.,Data Mining and Analysis: Fundamental Concepts and Algorithms, Cambridge University Press, United Kingdom,2013.

URL : proceedings.p-adri.or.id/2017/10/03/international-conferences-adri-12th/

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Detail Karya Ilmiah Dosen

Eneng Tita Tosida, Sufiatul Maryana, Hermawan Thaheer, Okta Hairlangga

Judul	:	CLASSIFICATION OF INDONESIAN TELEMATIC SERVICES MSMEs FEASIBILITY ASSISTANCE,USINGJ 48 ALGORITHM
Abstrak	:	Feasibility assistance for Micro, Small and Medium Enterprises (SMEs) Indonesia telematics services need to be assessed for compliance. suitability parameters assistance can be seen from the characteristics of MSMEs. Telematics Services MSMEs characteristic data fully available on the National Economic Census data (Susenas). Optimization of Susenas as the basis for the feasibility study can be done through the implementation of a decision tree approach particularly algorithm J 48. The use ofJ 48 algorithms to determine the feasibility of Indonesian telematics services MSMEs capable of providing an alternative way to describe the characteristics of MSMEs are eligible to receive assistance. This is particularly useful for ministries such as the Ministry of Cooperatives and SMEs, in terms of selecting eligible SMEs are given assistance. Criteria for aid feasibility is based on the general condition of SMEs, access to information technology, economic conditions, partnerships and development plans. Evaluation of the performance of the system showed that the algorithm J 48 is able to achieve an accuracy of 64% on the training data and at 57% on the test data, resulting in a decision rule that representative. The low accuracy is caused by data that is not balanced, so it is a potential for the development of further research involving balancing data.
Tahun	:	2017	Media Publikasi	:	Seminar Internasional
Kategori	:	Prosiding	No/Vol/Tahun	:	- / 12 / 2017
ISSN/ISBN	:	978-602-60736-7-9
PTN/S	:	Universitas Pakuan	Program Studi	:	ILMU KOMPUTER
Bibliography	:	REFERENCES [1] E.T. Tosida, S Maryana, H Thaheer, F.A. Darmin, “Visualization model of small and medium enterprises (SMEs) telematics services potentiality map in Indonesia”, inProc. of 2015 International Conference on Information & Communication Technology and Systems (ICTS),Added to IEEExplorer January 14th 2016,doi : 10.1109/ICTS.2015.7379890, 2015. [2] E.T. Tosida, S. Maryana, H. Thaheer, Hardiani, “Implementation of Self Organizing Map (SOM) as decision support : Indonesian telematics services MSMEs empowerment”, IOP Conf. Series : Material Science and Engineering 166 012017. doi:10.1088/1757-899X/166/1/012017, IORA-ICOR, August 27th 2017, IORA IOP Publishing, 2017. [3] H.S.Kim, S.Y.Sohn,“Support vector machines for default prediction of smes based on technology credit”,European Journal of Operational Research 201 (3) : 838-846. Elsevier. doi:10.1016/j.ejor.2009.03.036, 2010. [4] S.Y.Sohn, J.W.Kim,“Decision tree-based technology credit scoring for strart-up firms : Korean case”,J. Expert Systems with Applications 39 (4) : 4007-40112, Elsevier, doi:10.1016/j.eswa. 2011.09.075, 2012. [5] Y.Y. Wibisono, “Performance Comparation of Decision Tree Algorithm, C5.0, CART, Dan CHAID: Case Status Credit Risk Prediction In Bank X”, Thesis,Industry Engineering Department, Katolik Parahyangan University,2007. [6] J.Winesett, Web Application Development with Yii and PHP, Packt Publishing, Birmingham, Inggris, 2012. [7] J. Han, M.Kamber, J. Pei, Data mining : Concepts and techniques. Third Edition. Morgan Kaufmann is an imprint of Elsevier, 225Wyman Street,Waltham, MA 02451, USA, 2012. 8]M. Sourbati,“The digital switchover as an information society initiative: The role of public policy in promoting access to digital ICTs”, J.Telematics and Informatics,Vol 28, Issue 4, pp 295–30. Nov 2011. http://dx.doi.org/10.21107/kursor.v8i2.1299, Dec. 2015. [10]M.J. Zaki, W.Meira Jr.,Data Mining and Analysis: Fundamental Concepts and Algorithms, Cambridge University Press, United Kingdom,2013.
URL	:	proceedings.p-adri.or.id/2017/10/03/international-conferences-adri-12th/