• Desmarita Leni Universitas Muhammadiyah Sumatera Barat
  • Arwizet K Universitas Negeri Padang
  • Ruzita Sumiati Politeknik Negeri Padang
  • Haris Haris Politeknik Negeri Padang
  • Adriansyah Adriansyah Politeknik Negeri Padang



Machine Learning, Website, Yield Strength, Tensile Strength, Algorithm


The main objective of this research is to design a web-based machine learning model that can predict the mechanical properties of aluminum based on its chemical composition. By inputting nine variables of chemical elements such as Al, Mg, Zn, Ti, Cu, Mn, Cr, Fe, and Si, the model is able to provide predictions for two output data, Yield Strength (YS) and Tensile Strength (TS). The research aims to understand the relationship between chemical composition and mechanical properties of aluminum, and to develop a tool that can be used to predict these properties with a high level of accuracy. Overall, the goal of this study is to enhance the understanding of the properties of aluminum and how it can be utilized in various applications. This study designs a web-based machine learning modeling to predict the mechanical properties of aluminum in the percentage of chemical composition, where the input data in the modeling consists of 9 variables of chemical elements such as Al, Mg, Zn, Ti, Cu, Mn, Cr, Fe, Si, and has 2 output data consisting of Yield Strength (YS) and Tensile Strength (TS). The modeling machine learning is designed using the Python programming language and additional libraries such as Pandas, Numpy, Scikit-learn, and Streamlit. The modeling in this study uses three algorithms consisting of Decision Trees (DT), Random Forest (RF), and Artificial Neural Network (ANN). Each algorithm is optimized with the best search parameters, and where the RF algorithm has better performance than DT and JST. The best modeling uses the RF algorithm with optimal parameters of number of trees at 20 and maximum depth of 10, with MAE values of 11.44, RMSE of 14.282, and R of 0.93 for Yield Strength (YS) predictions, and for Tensile Strength (TS) predictions, MAE values are obtained. 21,669, RMSE 27,301, and R 0.871. 


Wisnujati, A., & Sepriansyah, C. (2018). Analisis Sifat Fisik dan mekanik paduan aluminium Dengan Variabel Suhu Cetakan Logam (dies) 450 dan 500 derajat celcius Untuk Manufaktur Poros Berulir (screw). Turbo : Jurnal Program Studi Teknik Mesin, 7(2).

Aluminium and Sodium , inflammatory-potential, Diakses: August 2022.

K. Suarsana, Ign Nitya Santhiarsa, Dnk Putra Negara. Pengaruh Perlakuan Temperatur Dan Media Pendinginan Terhadap Sifat Ketangguhan Baja Aisi 3215. Jurnal METTEK Volume 4 No 1 (2018) pp 23 – 30, ISSN 2502-3829.

Morini, A.A.; Ribeiro, M.J.; Hotza, D. Early-stage materials selection based on embodied energy and carbon footprint. Mater. Des. 2019, 178, 107861.

Krauss, G. Steels: Processing, Structure, and Performance; ASM International: Russell, OH, USA, 2015.

Luo, M.; Zhou, G.-Y. Shen, H. Wang, X.-T, Li, M.-C.,Zhang, Z.-H. Cao, G.-H. Effect of Tempering Temperature on Microstructure and Sulfide Stress Cracking of 125 Ksi Grade Casing Steel. Materials 2022, 15, 2589.

Valli Priyadharshini, K., Vijay, A., & Swaminathan, K. (2022). Materials property prediction using feature selection based machine learning technique. Materials Today: Proceedings.

Packwood, D., Nguyen, L. T., & Cesana, P. (2022). Machine Learning in Materials Chemistry: An invitation. Machine Learning with Applications, 8, 100265.

L. Qiao, Z. Wang, J. Zhu, Application of improved GRNN model to predict interlamellar spacing and mechanical properties of hypereutectoid steel, Mater. Sci. Eng. A 792 (2020), 139845.

Murphy, K.P. Machine Learning: A Probabilistic Perspective; MIT Press: Cambridge, MA, USA, 2012.

Warren, J. A. (2018). The materials genome initiative and Artificial Intelligence. MRS Bulletin, 43(6), 452–457.

Wei, J., Chu, X., & Sun, X. Y. (2019). Machine learning in materials science. InfoMat, 1(3), 338–358.

N. Sandhya, Valluripally Sowmya, Chennakesava Rao Bandaru, G. Raghu Babu. Prediction of Mechanical Properties of Steel using Data Science Techniques. International Journal of Recent Technology and Engineering (IJRTE) ISSN: 2277-3878, Volume-8 Issue-3, September 2019.

Agrawal, A.; Deshpande, P.D. Exploration of data science techniques to predict fatigue strength of steel from composition and processing parameters. Integr. Mater. Manuf. Innov. 2014, 3, 90–108.

Merayo, D.; Rodríguez-Prieto, A.; Camacho, A. Prediction of Physical and Mechanical Properties for Metallic Materials Selection Using Big Data and Artificial Neural Networks. IEEE Access 2020, 8, 13444–13456.

Arlot S, Celisse A. A survey of cross-validation procedures for model selection. Stat Surv. 2010;4:40-79.

Kwang-Hwi Choa Ktn, Scheraga Ha. A polarizable force field for water using an artificial neural network. J Mol Struct. 2002; 641:77-91.

IJRTE. (2022, August 8). Volume-9 issue-3, September 2020. International Journal of Recent Technology and Engineering (IJRTE). Retrieved September 28, 2022, from

Diao, Yupeng; Yan, Luchun; Gao, Kewei. Improvement of the machine learning-based corrosion rate prediction model through the optimization of input features. Materials & Design, 2021, 198: 109326.

Zhu, Zhenlong; Liang, Yilong; Zou, Jianghe. Modeling and composition design of low-alloy steel’s mechanical properties based on neural networks and genetic algorithms. Materials, 2020, 13.23: 5316.

Zhi, Yuanjie, et al. Prediction and knowledge mining of outdoor atmospheric corrosion rates of low alloy steels based on the random forests approach. Metals, 2019, 9.3: 383.

Leni, Desmarita, et al. Perbandingan Alogaritma Machine Learning Untuk Prediksi Sifat Mekanik Pada Baja Paduan Rendah. Jurnal Rekayasa Material, Manufaktur dan Energi, 2022, 5.2: 167-174.

Fushiki, Tadayoshi. Estimation of prediction error by using K-fold cross-validation. Statistics and Computing, 2011, 21.2: 137-146.

Wong, Tzu-Tsung; Yeh, Po-Yang. Reliable accuracy estimates from k-fold cross validation. IEEE Transactions on Knowledge and Data Engineering, 2019, 32.8: 1586-1594., Diakses: Agustus 2022.




How to Cite

Leni, D., K, A., Sumiati, R., Haris, H., & Adriansyah, A. (2023). PERANCANGAN METODE MACHINE LEARNING BERBASIS WEB UNTUK PREDIKSI SIFAT MEKANIK ALUMINIUM. Jurnal Rekayasa Mesin, 14(2), 611–626.