Pendekatan Diagnostik Berbasis Extreme Learning Machine dengan Kernel Linear untuk Mengklasifikasi Kelainan Paru-Paru

  • Putu Prima Winangun Udayana University
  • I Made Oka Widyantara Udayana University
  • Rukmi Sari Hartati

Abstract

Abstract— an expert system can be used as a second opinion for comparison or supporting diagnosis from experts. Data mining is used to obtain information applied to this system. Whereas in conducting learning using Artificial Neural Networks which apply the Extreme Learning Machine method so that it can accelerate learning up to thousands of times. In this paper, software development is carried out to test the activation functions used in conducting learning and the variables used as input during learning. 

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References

[1] U. Gupta, V. Kumar and S. Sharma, "Improving medical image classification model using data mining technique," in Communication and computing systems, London, 2017.
[2] N. S and P. M. Goel Dr, "Comparison of Classification Techniques on Data Mining," International Journal of Emerging Technology and Innovative Engineering, vol. 5, no. 5, 2019.
[3] S. A. Lashari, R. Ibrahim, N. Senan and N. S. A. M. Taujuddin, "Application of Data Mining Techniques for Medical Data," in MATEC Web of Conferences 150, 2018.
[4] A. Vidyarthi and N. Mittal, "Texture based feature extraction method for classification of brain tumor MRI," Journal of Intelligent and Fuzzy Systems, vol. 32, no. 4, pp. 1-12, 2017.
[5] L. Armi and S. Fekri-Ershad, "Texture image analysis and texture classification methods - A Review," International Online Journal of Image Processing and Pattern Recognition, vol. 2, no. 1, pp. 1-29, 2019.
[6] T. Sharma, A. Sharma and P. Mansotra, "Performance Analysis of Data Mining Classification Techniques on Public Health Care Data," International Journal of Innovative Research in Computer and Communication Engineering.
[7] S. Makridakis, E. Spiliotis and V. Assimakopoulos, "Statistical and Machine Learning forecasting methods: Concerns and ways forward," PLOS ONE Statistical and ML forecasting methods, 2018.
[8] A. Bakhshipour and A. Jafari, "Evaluation of support vector machine and artificial neural networks in weed detection using shape features," Computers and Electronics in Agriculture, vol. 145, pp. 153-160, 2018.
[9] G.-B. Huang, H. Zhou, X. Ding and R. Zhang, "Robust Classification of Brain Tumor in MRI Images using Salient Structure Descriptor and RBF Kernel- SVM," TAGA Journal, vol. 14, pp. 718-737, 2018.
[10] V. Sharma, D. Baruah, D. Chutia and P. L. N. Raju, "An Assessment of Support Vector Machine Kernel Parameters using Remotely Sensed Satellite Data," in IEEE International Conference On Recent Trends In Electronics Information Communication Technology, India, 2016.
[11] P. K. Intan, "Comparison of Kernel Function on Support Vector Machine in Classification of Childbirth," Jurnal Matematika MANTIK, vol. 5, no. 2, pp. 90-99, 2019.
[12] P. B. S. Hendrayana, R. L. Rahardian and M. Sudarma, "Application of Neural Network Overview In Data Mining," International Journal of Engineering and Emerging Technology, vol. 2, pp. 94-96, 2017.
[13] J. Cao, K. Zhang, M. Luo, C. Yin and X. Lai, "Extreme learning machine and adaptive sparse representation for image classification," Neural Networks, 2016.
[14] N. Nietoa, F. Ibarrolaa , V. Petersonb , H. L. Rufinera and R. Spiesb, "Extreme Learning Machine design for dealing with unrepresentative features," Preprint submitted to Journal of LATEX Templates, 2019.
[15] Arash A. Amini and Zahra S. Razaee, "Concentration of kernel matrices with application to kernel spectral clustering," arxiv.org, 2019.
Published
2020-10-15
How to Cite
WINANGUN, Putu Prima; WIDYANTARA, I Made Oka; HARTATI, Rukmi Sari. Pendekatan Diagnostik Berbasis Extreme Learning Machine dengan Kernel Linear untuk Mengklasifikasi Kelainan Paru-Paru. Majalah Ilmiah Teknologi Elektro, [S.l.], v. 19, n. 1, p. 83-88, oct. 2020. ISSN 2503-2372. Available at: <https://ojs.unud.ac.id/index.php/JTE/article/view/57573>. Date accessed: 20 jan. 2021. doi: https://doi.org/10.24843/MITE.2020.v19i01.P12.