Pendekatan Diagnostik Berbasis Extreme Learning Machine dengan Kernel Linear untuk Mengklasifikasi Kelainan Paru-Paru
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https://doi.org/10.24843/MITE.2020.v19i01.P12
Abstrak
Intisari— Sebuah sistem pakar dapat digunakan sebagai opini kedua untuk pembanding atau pendukung diagnose dari pakar. Penggalian data digunakan untuk mendapatkan informasi diterapkan pada Sistem ini. Sedangkan dalam melakukan pembelajaran menggunakan Jaringan Saraf Tiruan yang menerapkan metode Extreme Learning Machine sehingga dapat mempercepat pembelajaran hingga ribuan kali lipat. Dalam makalah ini, pengembangan perangkat lunak yang dilakukan untuk menguji fungsi aktivasi yang digunakan dalam melakukan pembelajaran dan variable yang digunakan sebagai input pada saat pembelajaran.
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Referensi
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[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.
Diterbitkan
2020-10-15
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WINANGUN, Putu Prima; WIDYANTARA, I Made Oka; HARTATI, Rukmi Sari.
Pendekatan Diagnostik Berbasis Extreme Learning Machine dengan Kernel Linear untuk Mengklasifikasi Kelainan Paru-Paru.
Jurnal Teknologi Elektro, [S.l.], v. 19, n. 1, p. 83-88, oct. 2020.
ISSN 2503-2372.
Tersedia pada: <http://ojs.unud.ac.id/index.php/mite/article/view/57573>. Tanggal Akses: 14 aug. 2025
doi: https://doi.org/10.24843/MITE.2020.v19i01.P12.
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