Kombinasi Algoritma Backpropagation Neural Network dengan Gravitational Search Algorithm Dalam Meningkatkan Akurasi

 (*)Miftahul Falah Mail (Universitas Sriwijaya, Palembang, Indonesia)
 Dian Palupi Rini (Universitas Sriwijaya, Palembang, Indonesia)
 Iwan Pahendra (Universitas Sriwijaya, Palembang, Indonesia)

(*) Corresponding Author

Submitted: November 19, 2020; Published: January 22, 2021

DOI: http://dx.doi.org/10.30865/mib.v5i1.2597


Predicting disease is usually done based on the experience and knowledge of the doctor. Diagnosis of such a disease is traditionally less effective. The development of medical diagnosis based on machine learning in terms of disease prediction provides a more accurate diagnosis than the traditional way. In terms of predicting disease can use artificial neural networks. The artificial neural network consists of various algorithms, one of which is the Backpropagation Algorithm. In this paper it is proposed that disease prediction systems use the Backpropagation algorithm. Backpropagation algorithms are often used in disease prediction, but the Backpropagation algorithm has a slight drawback that tends to take a long time in obtaining optimum accuracy values. Therefore, a combination of algorithms can overcome the shortcomings of the Backpropagation algorithm by using the success of the Gravitational Search Algorithm (GSA) algorithm, which can overcome the slow convergence and local minimum problems contained in the Backpropagation algorithm. So the authors propose to combine the Backpropagation algorithm using the Gravitational Search Algorithm (GSA) in hopes of improving accuracy results better than using only the Backpropagation algorithm. The results resulted in a higher level of accuracy with the same number of iterations than using Backpropagation only. Can be seen in the first trial of breast cancer data with parameters namely hidden layer 5, learning rate of 2 and iteration as much as 5000 resulting in accuracy of 99.3 % with error 0.7% on Backpropagation Algorithm, while in combination BP & GSA got accuracy of 99.68 % with error of 0.32%.


Combination; Accuracy; Artificial Neural Networks; Backpropagation; Gravitational Search Algorithm (GSA)

Full Text:


Article Metrics

Abstract View: 48 times | PDF View: 7 times


S. Bhaumik and M. Kamaraj, “Artificial neural network and multi-criterion decision making approach of designing a blend of biodegradable lubricants and investigating its tribological properties,” Proc. Inst. Mech. Eng. Part J J. Eng. Tribol., 2020, doi: 10.1177/1350650120965754.

R. M. Sadek et al., “Parkinson’s Disease Prediction Using Artificial Neural Network,” vol. 3, no. 1, pp. 1–8, 2019, [Online]. Available: http://dstore.alazhar.edu.ps/xmlui/handle/123456789/302.

J. J. Pangaribuan, “Mendiagnosis Penyakit Diabetes Melitus Dengan Menggunakan Metode Extreme Learning Machine,” J. ISD, vol. 2, no. 2, pp. 2528–5114, 2016.

P. Sehgal and K. Taneja, “Predictive Data Mining for Diagnosis of Thyroid Disease using Neural Network,” vol. 3, no. 2, pp. 75–80, 2015.

M. Durairaj, “PREDICTION OF DIABETES USING BACK PROPAGATION ALGORITHM,” vol. 1, no. 8, pp. 21–25, 2015.

T. Karayilan and Ö. Kiliç, “Prediction of Heart disease using neural network,” 2nd Int. Conf. Comput. Sci. Eng. UBMK 2017, pp. 719–723, 2017, doi: 10.1109/UBMK.2017.8093512.

A. Wanto, “Optimasi Prediksi Dengan Algoritma Backpropagation Dan Conjugate Gradient Beale-Powell Restarts,” J. Nas. Teknol. dan Sist. Inf., vol. 3, no. 3, pp. 370–380, 2018, doi: 10.25077/teknosi.v3i3.2017.370-380.

C. S. Chen and S. L. Su, “Resilient back-propagation neural network for approximation 2-D GDOP,” Proc. Int. MultiConference Eng. Comput. Sci. 2010, IMECS 2010, vol. II, no. 1, pp. 900–904, 2010.

Riska Yanu Fa’arifah and Z. Busrah, “Backpropagation Neural Network untuk Optimasi Akurasi pada Prediksi Financial Distress Perusahaan,” J. Inf. Sains dan Teknol., vol. 2, no. April, pp. 101–110, 2017.

I. Muzakkir, A. Syukur, and I. Novita Dewi, “Peningkatan Akurasi Algoritma Backpropagation Dengan Seleksi Fitur Particle Swarm Optimization Dalam Prediksi Pelanggan Telekomunikasi Yang Hilang,” Pseudocode, vol. 1, no. 1, pp. 1–10, 2015, doi: 10.33369/pseudocode.1.1.1-10.

R. Bala and D. Kumar, “Classification Using ANN : A Review,” vol. 13, no. 7, pp. 1811–1820, 2017.

S. M. K. Chaitanya and P. R. Kumar, Detection of Chronic Kidney Disease by Using Arti fi cial Neural Networks and Gravitational Search Algorithm. Springer Singapore.

E. Rashedi, H. Nezamabadi-pour, and S. Saryazdi, “GSA: A Gravitational Search Algorithm,” Inf. Sci. (Ny)., vol. 179, no. 13, pp. 2232–2248, 2009, doi: 10.1016/j.ins.2009.03.004.

V. Kumar, J. K. Chhabra, and D. Kumar, “Automatic cluster evolution using gravitational search algorithm and its application on image segmentation,” Eng. Appl. Artif. Intell., vol. 29, pp. 93–103, 2014, doi: 10.1016/j.engappai.2013.11.008.

E. Rashedi, H. Nezamabadi-Pour, and S. Saryazdi, “Filter modeling using gravitational search algorithm,” Eng. Appl. Artif. Intell., vol. 24, no. 1, pp. 117–122, 2011, doi: 10.1016/j.engappai.2010.05.007.

C. Purcaru, R. E. Precup, D. Iercan, L. O. Fedorovici, R. C. David, and F. Dragan, “Optimal robot path planning using gravitational search algorithm,” Int. J. Artif. Intell., vol. 10, no. 13 S, pp. 1–20, 2013.

P. Antwi et al., “Estimation of biogas and methane yields in an UASB treating potato starch processing wastewater with backpropagation artificial neural network,” Bioresour. Technol., vol. 228, pp. 106–115, 2017, doi: 10.1016/j.biortech.2016.12.045.

D. Huang and Z. Wu, “Forecasting outpatient visits using empirical mode decomposition coupled with backpropagation artificial neural networks optimized by particle swarm optimization,” PLoS One, vol. 12, no. 2, pp. 1–17, 2017, doi: 10.1371/journal.pone.0172539.

J. Tarigan, Nadia, R. Diedan, and Y. Suryana, “Plate Recognition Using Backpropagation Neural Network and Genetic Algorithm,” Procedia Comput. Sci., vol. 116, pp. 365–372, 2017, doi: 10.1016/j.procs.2017.10.068.

S. P. Siregar and A. Wanto, “Analysis of Artificial Neural Network Accuracy Using Backpropagation Algorithm In Predicting Process (Forecasting),” IJISTECH (International J. Inf. Syst. Technol., vol. 1, no. 1, p. 34, 2017, doi: 10.30645/ijistech.v1i1.4.

V. K. Ojha, P. Dutta, H. Saha, and S. Ghosh, “Detection of Proportion of Different Gas Components Present in Manhole Gas Mixture Using Backpropagation Neural Network,” Technology, vol. 37, no. Icint, pp. 11–15, 2012.

Bila bermanfaat silahkan share artikel ini

Berikan Komentar Anda terhadap artikel Kombinasi Algoritma Backpropagation Neural Network dengan Gravitational Search Algorithm Dalam Meningkatkan Akurasi


  • There are currently no refbacks.


Creative Commons License
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.

STMIK Budi Darma
Sekretariat : Jln. Sisingamangaraja No. 338 Telp 061-7875998
email : mib.stmikbd@gmail.com

Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 International License.