Pengukuran Perangkat Lunak Untuk Effort Estimation Dengan Teknik Pembelajaran Mesin

Authors

  • Maria Rosario Borroek Universitas Dinamika Bangsa, Jambi
  • Errissya Rasywir Universitas Dinamika Bangsa, Jambi
  • Yovi Pratama Universitas Dinamika Bangsa, Jambi

DOI:

https://doi.org/10.30865/mib.v4i2.2083

Keywords:

FPA, Mamdani, Sugeno, Fuzzy, Effort

Abstract

Software effort estimation is to estimate the amount of resources needed in developing the software. For that software effort estimation is important so need to see the effect of software measurement to software effort estimation which is done by machine learning technique. Based on this the researcher tries to build a system capable of measuring software. In this study experiments on software measurement techniques (FPA, FPA with Sugeno fuzzy and FPA with mamdani fuzzy). The three types of techniques are compared with the three project data for further software effort estimation. For evaluation, this study evaluates using the assessment of the Developeras Analyst of the Project. The results of the study that the LOC and effort values on a similar system can be different if calculated by the use of FPA, Fam Mamdany fuzzy and FPA Sugeno Fuzzy. The highest LOC and Effort values are generated by FPA Mamdany Fuzzy on Project DUMAS POLDA SUMSEL. While the lowest effort value and lowest LOC produced by FPA Sugeno Fuzzy. This can be traced from the calculation mechanisms performed by FPA Sugeno Fuzzy where this method does not count the input, output, file, query and interface values at all. The calculation of FPA Sugeno fuzzy is done by roughly judging only from the difficulty of making the system. To raise the price of a project in order to be rewarded higher FAT methods Mamdani Fuzzy is recommended

Author Biographies

Maria Rosario Borroek, Universitas Dinamika Bangsa, Jambi

Sistem Informasi, Program Studi Imu Komputer

Errissya Rasywir, Universitas Dinamika Bangsa, Jambi

Teknik Informatika , Program Studi Imu Komputer

Yovi Pratama, Universitas Dinamika Bangsa, Jambi

Teknik Informatika , Program Studi Imu Komputer

References

Y. Pratama and E. Rasywir, “Automatic Cost Estimation Analysis on Datawarehouse Project with Modified Analogy Based Method,†in Proceedings of 2018 International Conference on Electrical Engineering and Computer Science, ICECOS 2018, 2019, pp. 171–176.

R. Sarno and J. Sidabutar, “Comparison of Different Neural Network Architectures for Software Cost Estimation,†in International Conference on Computer, Control, Informatics and Its Applications Comparison, 2015, pp. 68–73.

F. Fachruddin and Y. Pratama, “Eksperimen Seleksi Fitur Pada Parameter Proyek Untuk Software Effort Estimation dengan K-Nearest Neighbor,†J. Inform. J. Pengemb. IT, vol. 2, no. 2, pp. 53–62, 2017.

B. Demir and L. Bruzzone, “A Novel Active Learning Method in Relevance Feedback for Content-Based Remote Sensing Image Retrieval,†Geosci. Remote Sensing, IEEE Trans., vol. 53, no. 5, pp. 2323–2334, 2015.

S. Kumari and S. Pushkar, “Comparison and Analysis of Different Software Cost Estimation Methods,†Int. J. Adv. Comput. Sci. Appl., vol. 4, no. 1, pp. 153–157, 2013.

J. W. S. L. L. Tang, “Software, Improve Analogy-based Estimation, Effort Principal, Using Analysis, Components Weighting,†in Software Engineering Conference, 2011, vol. 16, no. 2, pp. 11–12.

Y. F. Li, M. Xie, and T. N. Goh, “A study of project selection and feature weighting for analogy based software cost estimation,†J. Syst. Softw., vol. 82, no. 2, pp. 241–252, 2009.

M. Azzeh, “A replicated assessment and comparison of adaptation techniques for analogy-based effort estimation,†Empir. Softw. Eng., vol. 17, no. 1–2, pp. 90–127, 2012.

N. H. Chiu and S. J. Huang, “The adjusted analogy-based software effort estimation based on similarity distances,†J. Syst. Softw., vol. 80, no. 4, pp. 628–640, 2007.

A. Idri, F. A. Amazal, and A. Abran, “Analogy-based software development effort estimation: A systematic mapping and review,†Inf. Softw. Technol., vol. 58, pp. 206–230, 2015.

A. K. Bardsiri and S. M. Hashemi, “Software Effort Estimation : A Survey of Well-known Approaches,†Ijcse, vol. 3, no. 01, pp. 46–50, 2014.

S. Kaur, S. Assistant, J. Kaur, S. Faculty, N. Chandigarh, and S. Singh, “Effect of Data Preprocessing on Software Effort Estimation,†Int. J. Comput. Appl., vol. 69, no. 25, pp. 975–8887, 2013.

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Published

2020-04-25

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