KOMPARASI METODE NEURAL NETWORK, SUPPORT VECTOR MACHINE DAN LINEAR REGRESSION PADA ESTIMASI KUAT TEKAN BETON
Keywords:
Metode Neural Network, Metode Support Vector Machine, Metode Linear Regression, Estimasi Kuat Tekan Beton
Abstract
Penggunaan beton sudah semakin meluas dikarenakan beton memiliki kuat tekan yang lebih tinggi dibandingkan dengan bahan lain. Para ahli melakukan prediksi kuat tekan beton dengan kurang efektif karena masih menggunakan aturan dan rumus standar tertentu. Banyak penelitian dilakukan dengan beberapa metode namun belum diketahui metode mana yang terbaik. Penelitian ini melakukan komparasi antara metode Neural Network (NN), Support Vector Machine (SVM) dan Linear Regression (LR) dengan menggunakan dataset concrete compressive strength dan slump. Pada dataset concrete compressive strength dengan menggunakan metode NN didapatkan RMSE 5,667, dengan menggunakan metode SVM didapatkan RMSE 5,165 dan dengan metode LR didapatkan RMSE 10,501. Sementara pada dataset slump dengan menggunakan metode NN didapatkan RMSE 0,422, dengan menggunakan metode SVM didapatkan RMSE 2,778 dan dengan menggunakan metode LR didapatkan RMSE 2,65. Setelah hasil tersebut dikomparasi dengan perangkingan didapatkan total ranking NN adalah 3, total rangking SVM adalah 4, dan total rangking LR adalah 5. Dari total rangking tersebut dapat disimpulkan bahwa kinerja NN lebih baik dibanding SVM dan LR.
References
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Yeh, I.-C. (1998). Modeling of strength of high-performance concrete using artificial neural networks. Cement and Concrete Research, 28(12), 1797–1808. https://doi.org/10.1016/S0008-8846(98)00165-3
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Alshihri, M. M., Azmy, A. M., & El-Bisy, M. S. (2009). Neural networks for predicting compressive strength of structural light weight concrete. Construction and Building Materials, 23(6). https://doi.org/10.1016/j.conbuildmat.2008.12.003
Burges, C. (1998). A tutorial on support vector machines for pattern recognition. Data Mining and Knowledge Discovery, 2, 2.
Chen, H., Zhang, J., Xu, Y., Chen, B., & Zhang, K. (2012). Performance comparison of artificial neural network and logistic regression model for differentiating lung nodules on CT scans. Expert Systems with Applications, 39(13), 11503–11509. https://doi.org/10.1016/j.eswa.2012.04.001
Chen, L. (2010). A Multiple Linear Regression Prediction of Concrete Compressive Strength Based on Physical Properties of Electric Arc Furnace Oxidizing Slag, 153–158.
Chopra, P. (2014). Regression models for the prediction of compressive strength of concrete with & without fly ash. International Journal of Latest Trends in Engineering and Technology (IJLTET), 3(4), 400–406.
Chou, J., & Pham, A. (2013). Enhanced artificial intelligence for ensemble approach to predicting high performance concrete compressive strength. Construction and Building Materials. https://doi.org/10.1016/j.conbuildmat.2013.08.078
Deshpande, N., Londhe, S., & Kulkarni, S. (2014). Modeling compressive strength of recycled aggregate concrete by Artificial Neural Network, Model Tree and Non-linear Regression. International Journal of Sustainable Built Environment, 3(2), 187–198. https://doi.org/10.1016/j.ijsbe.2014.12.002
Gorunescu, F. (2011). Data Mining: Concepts, models and techniques (2011th ed.). Springer.
Han, J., & Kamber, M. (2012). Data Mining: Concepts and Techniques (3rd ed).
Han, J., Kamber, M., & Pei, J. (2012). Data mining: concepts and techniques (3rd ed.). Morgan Kaufmann.
Handoko, Witarto, N. (2003). Support Vector Machine Teori dan Aplikasinya dalam Bioinformatika. Ilmu Komputer.com.
Khademi, F., Jamal, S. M., Deshpande, N., & Londhe, S. (2016). Predicting strength of recycled aggregate concrete using Artificial Neural Network, Adaptive Neuro-Fuzzy Inference System and Multiple Linear Regression. International Journal of Sustainable Built Environment, 5(2). https://doi.org/10.1016/j.ijsbe.2016.09.003
Londhe, S. N. (2009). Towards predicting water levels using artificial neural networks. In OCEANS ’09 IEEE Bremen: Balancing Technology with Future Needs. https://doi.org/10.1109/OCEANSE.2009.5278347
Magaji, A. S., Isah, A., Waziri, V. O., & Adeboye, K. R. (2013). A conceptual Nigeria stock exchange prediction: Implementation using Support Vector Machines-SMO model. World of Computer Science and Information Technology Journal (WCSIT), 3(4), 85–90.
McCormac, J. C. (2004). Desain Beton Bertulang-Edisi Kelima-jilid 2.
Mulyono, T. (2004). Teknologi Beton. Yogyakarta: Andi Publishing.
Nazari, A., & Pacheco Torgal, F. (2013). Predicting compressive strength of different geopolymers by artificial neural networks. Ceramics International, 39(3). https://doi.org/10.1016/j.ceramint.2012.08.070
Neter, J., Kutner, M., Nachtsheim, C., & Wasserman, W. (1996). Applied linear statistical models: Regression, analysis of variance, and experimental design.
Setiyorini, T. (2015). Penerapan Metode Bagging untuk Mengurangi Data Noise pada Neural Network untuk Estimasi Kuat Tekan Beton, 1(1), 37–42.
Setiyorini, T., & Asmono, R. T. (2018). Laporan Penelitan Mandiri. Jakarta.
Sudjana. (2005). Metode Statistika. Bandung: Transito.
Tinoco, J., Gomes Correia, A., & Cortez, P. (2014). Support vector machines applied to uniaxial compressive strength prediction of jet grouting columns. Computers and Geotechnics, 55. https://doi.org/10.1016/j.compgeo.2013.08.010
Vapnik, V. (1998). Statistical Learning Theory. Wiley-Interscience. https://doi.org/10.2307/1271368
Yan, K., & Shi, C. (2010). Prediction of elastic modulus of normal and high strength concrete by support vector machine. Construction and Building Materials, 24(8), 1479–1485. https://doi.org/10.1016/j.conbuildmat.2010.01.006
Yeh, I.-C. (1998). Modeling of strength of high-performance concrete using artificial neural networks. Cement and Concrete Research, 28(12), 1797–1808. https://doi.org/10.1016/S0008-8846(98)00165-3
Yeh, I.-C. (2007). Modeling slump flow of concrete using second-order regressions and artificial neural networks. Cement and Concrete Composites, 29(6), 474–480. https://doi.org/10.1016/J.CEMCONCOMP.2007.02.001
Zhao, W., Tao, T., & Zio, E. (2015). System reliability prediction by support vector regression with analytic selection and genetic algorithm parameters selection. Applied Soft Computing Journal, 30, 792–802. https://doi.org/10.1016/j.asoc.2015.02.026
Published
2018-03-15
How to Cite
Setiyorini, T., & Asmono, R. (2018). KOMPARASI METODE NEURAL NETWORK, SUPPORT VECTOR MACHINE DAN LINEAR REGRESSION PADA ESTIMASI KUAT TEKAN BETON. Jurnal Techno Nusa Mandiri, 15(1), 51-56. https://doi.org/10.33480/techno.v15i1.58
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