Komparasi Metode Machine Learning dan Metode Non Machine Learning untuk Estimasi Usaha Perangkat Lunak

Ega Kartika Adhitya, Romi Satria Wahono, Hendro Subagyo

Abstract


Estimasi usaha adalah proses yang sangat penting dalam kesuksesan pelaksanaan suatu proyek perangkat lunak. Memilih metode estimasi yang sesuai dengan proyek yang akan dikerjakan diperlukan pemahaman yang jelas tentang metode-metode estimasi usaha yang salah satunya mengetahui kelemahan dan kelebihan dari masing - masing metode tersebut. Dalam penelitian ini dikaji dua kelompok besar metode estimasi biaya perangkat lunak yakni metode machine learning dan metode non machine learning untuk mengetahui metode mana yang paling baik. Pada penelitian pertama mengunakan metode machine learning  dapat kita ketahui bahwa K-NN(k-nearnest neigbhors) mempunyai nilai RSME yang paling baik.  Pada penelitian Kedua mengunakan metode non machine learning  Dari hasil tersebut dapat kita ketahui bahwa FP (fungsion point ) mempunyai nilai RSME yang paling baik. Pada Penelitian Ketiga diantara metode machine learning dan non machine learning didapatkan K-NN yang mempunyai nilai RSME yang paling baik. Pada penelitian Keempat penambahan seleksi atribut  forward selection mendapatkan hasil yang paling baik untuk digunakan pada estimasi usaha perangkat lunak.


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References


Albrecht, A. J., & Gaffney, J. E. (1983). Software Function, Source Lines of Code, and Development Effort Prediction: A Software Science Validation. IEEE Transactions on Software Engineering, SE-9(6), 639–648. doi:10.1109/TSE.1983.235271

Boehm, B. W., & Papaccio, P. N. (1988). Understanding and controlling software costs. IEEE Transactions on Software Engineering. doi:10.1109/32.6191

Choy, S. K., Tang, M. L., & Tong, C. S. (2011). Image segmentation using fuzzy region competition and spatial/frequency information. IEEE Transactions on Image Processing : A Publication of the IEEE Signal Processing Society, 20(6), 1473–84. doi:10.1109/TIP.2010.2095023

Danger, R., Segura-Bedmar, I., Martínez, P., & Rosso, P. (2010). A comparison of machine learning techniques for detection of drug target articles. Journal of Biomedical Informatics, 43(6), 902–13. doi:10.1016/j.jbi.2010.07.010

Dennis, A. (2012). Systems Analysis and Design: An Applied Approach. Wiley; 5 edition (January 18, 2012). Retrieved from http://www.philadelphia.edu.jo/it/cs/syllabus/731332.pdf

Dubois, S., Rasovska, I., & De Guio, R. (2009). Towards an automatic extraction of Generalized System of Contradictions out of solutionless Design of Experiments. In 3nd IFIP Working Conference on Computer Aided Innovation (CAI): Growth and Development of CAI. doi:10.1007/978-3-642-03346-9

El-Sebakhy, E. a. (2011). Functional networks as a novel data mining paradigm in forecasting software development efforts. Expert Systems with Applications, 38(3), 2187–2194. doi:10.1016/j.eswa.2010.08.005

Ian Sommerville. (2011). Software engineering 9. New York 1992. Addison-Wesley; 9 edition (March 13, 2010). doi:10.1109/MC.1987.1663532.

Kocaguneli, E., & Menzies, T. (2013). Software effort models should be assessed via leave-one-out validation. Journal of Systems and Software, 86(7), 1879–1890. doi:10.1016/j.jss.2013.02.053

Mehmood, A., S. Palli, A., & Khan, M. N. A. (2014). A Study of Sentiment and Trend Analysis Techniques for Social Media Content. International Journal of Modern Education and Computer Science, 6(December), 47–54. doi:10.5815/ijmecs.2014.12.07

Nassif, A. B., Capretz, L. F., & Hill, R. (1993). A Regression Model with Mamdani Fuzzy Inference System for Early Software Effort Estimation Based on Use Case Diagrams, 615–620.

Nassif, A. B., Capretz, L. F., & Ho, D. (2011). Estimating Software Effort Based on Use Case Point Model Using Sugeno Fuzzy Inference System. 2011 IEEE 23rd International Conference on Tools with Artificial Intelligence, 393–398. doi:10.1109/ICTAI.2011.64

Nassif, A. B., Capretz, L. F., & Ho, D. (2012). Estimating Software Effort Using an ANN Model Based on Use Case Points. 2012 11th International Conference on Machine Learning and Applications, 7, 42–47. doi:10.1109/ICMLA.2012.138

Nunes, N., Constantine, L., & Kazman, R. (2011a). IUCP: Estimating interactive-software project size with enhanced use-case points. IEEE Software, 28, 64–73. doi:10.1109/MS.2010.111

Nunes, N., Constantine, L., & Kazman, R. (2011b). IUCP: Estimating interactive-software project size with enhanced use-case points. IEEE Software. doi:10.1109/MS.2010.111

Oliveira, A. L. I., Braga, P. L., Lima, R. M. F., & Cornélio, M. L. (2010). GA-based method for feature selection and parameters optimization for machine learning regression applied to software effort estimation. Information and Software Technology, 52(11), 1155–1166. doi:10.1016/j.infsof.2010.05.009

Oliveira, A. L. I., Braga, P. L., Lima, R. M. F., & Cornélio, M. L. (2010). GA-based method for feature selection and parameters optimization for machine learning regression applied to software effort estimation. Information and Software Technology, 52(11), 1155–1166. doi:10.1016/j.infsof.2010.05.009

Papatheocharous, E., & Andreou, A. S. (2009). Hybrid Computational Models for Software Cost Prediction : An Approach Using Artificial Neural Networks and Genetic Algorithms, 87–100.

Shepperd, M., & MacDonell, S. (2012, August). Evaluating prediction systems in software project estimation. Information and Software Technology. Elsevier B.V. doi:10.1016/j.infsof.2011.12.008

Wang, S., Li, D., Song, X., Wei, Y., & Li, H. (2011). A feature selection method based on improved fisher’s discriminant ratio for text sentiment classification. Expert Systems with Applications, 38(7), 8696–8702. doi:10.1016/j.eswa.2011.01.077

Wang, W., & Zhou, Z. H. (2012). Learnability of multi-instance multi-label learning. Chinese Science Bulletin, 57, 2488–2491. doi:10.1007/s11434-012-5133-z






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