Istrazivanja i projektovanja za privreduJournal of Applied Engineering Science


DOI: 10.5937/jaes0-30175 
This is an open access article distributed under the CC BY 4.0
Creative Commons License

Volume 19 article 889 pages: 1083-1089

Budi Raharjo
Universitas STEKOM, Department of Information System, Semarang, Indonesia

Nurul Farida
Universitas Islam Balitar, Department of Management, Blitar, Indonesia

Purwo Subekti
Universitas Pasir Pengaraian, Department of Mechanical Engineering, Riau , Indonesia

Rima Herlina S Siburian
Universitas Papua, Department of Forestry, Papua Barat, Indonesia

Putu Doddy Heka Ardana
Universitas Ngurah Rai, Department of Civil Engineering, Denpasar, Indonesia

Robbi Rahim*
Sekolah Tinggi Ilmu Manajemen Sukma, Medan, Indonesia

The purpose of this study was to evaluate the back-propagation model by optimizing the parameters for the prediction of broiler chicken populations by provinces in Indonesia. Parameter optimization is changing the learning rate (lr) of the backpropagation prediction model. Data sourced from the Directorate General of Animal Husbandry and Animal Health processed by the Central Statistics Agency (BPS). Data is the population of Broiler Chickens from 2017 to 2019 (34 records). The analysis process uses the help of RapidMiner software. Data is divided into 2 parts, namely training data (2017-2018) and testing data (2018-2019). The backpropagation model used is 1-2-1; 1-25-1 and 1-45-1 with a learning rate (0.1; 0.01; 0.001; 0.2; 0.02; 0.002; 0.3; 0.03; 0.003). From the three models tested, the 1-45-1 model (lr = 0.3) is the best model with Root Mean Squared Error = 0.028 in the training data. With this model, the prediction results obtained with an accuracy value of 91% and Root Mean Squared Error = 0.00555 in the testing data.

View article

1. Karimi, H., Yousefi, F., 2012. Application of artificial neural network-genetic algorithm (ANN-GA) to correlation of density in nanofluids. Fluid Phase Equilibria 336: 79–83, Doi: 10.1016/j.fluid.2012.08.019.

2. Rahim, R., 2020. Educational Data Mining (EDM) on the use of the internet in the world of Indonesian education. TEM Journal, Doi: 10.18421/TEM93-39.

3. Šlibar, B., 2019. Predicting the number of downloads of open datasets by naive bayes classifier. TEM Journal 8(4): 1331–8, Doi: 10.18421/TEM84-33.

4. Rozaq, I., Satriyanto, E., 2018. Wireless Sensor Network (WSN) application for Monitoring the River Water Level. Proceedings of the The 1st International Conference on Computer Science and Engineering Technology Universitas Muria Kudus, EAI.

5. Wu, Y.K., Hong, J.S., 2007. A literature review of wind forecasting technology in the world. 2007 IEEE Lausanne POWERTECH, Proceedings, p. 504–9.

6. Li, Q., Zhang, X., Rigat, A., Li, Y., 2016. Parameters optimization of back propagation neural network based on memetic algorithm coupled with genetic algorithm. Proceedings - 2015 IEEE 12th International Conference on Ubiquitous Intelligence and Computing, 2015 IEEE 12th International Conference on Advanced and Trusted Computing, 2015 IEEE 15th International Conference on Scalable Computing and Communications, 20: 1359–64, Doi: 10.1109/UIC-ATC-ScalCom-CBDCom-IoP.2015.245.

7. Han, J.U.N., Sinne, S., 1996. Contributed Paper Optimization of Feedforward Neural Networks. Engineering Applications of Artificial Intelligence 9(2): 109–19.

8. Nikentari, N., Kurniawan, H., Ritha, N., Kurniawan, D., Maritim, U., Ali, R., 2018. Particle Swarm Optimization Untuk Prediksi Pasang Surut Air Optimization of Backpropagation Artificial Neural Network With Particle Swarm Optimization To Predict Tide Level. Jurnal Teknologi Informasi Dan Ilmu Komputer 5(5): 605–12, Doi: 10.25126/jtiik2018551055.

9. Rahman, W., Nguyen, P.T., Rusliyadi, M., Laxmi Lydia, E., Shankar, K., 2019. Network monitoring tools and techniques uses in the network traffic management system. International Journal of Recent Technology and Engineering 8(2 Special Issue 11): 4182–8, Doi: 10.35940/ijrte.B1603.0982S1119.

10. Moreira, M., Fiesler, E., 1995. Neural Networks with Adaptive Learning Rate and Momentum Terms. Technique Report 95 4: 1–29.

11. Susanto, E., Novitasari, Y., Rahman, W., Amane, A.P.O., 2019. Designing Software to Introduce the Musical Instruments. Journal of Physics: Conference Series, vol. 1364. Institute of Physics Publishing.

12. Elgimati, Y., 2020. Weighted Bagging in Decision Trees: Data Mining. JINAV: Journal of Information and Visualization, Doi: 10.35877/454ri.jinav149.

13. Sudirman., Windarto, A.P., Wanto, A., 2018. Data mining tools | rapidminer: K-means method on clustering of rice crops by province as efforts to stabilize food crops in Indonesia. IOP Conference Series: Materials Science and Engineering 420: 012089, Doi: 10.1088/1757-899X/420/1/012089.

14. Hizham, F.A., Nurdiansyah, Y., Firmansyah, D.M., 2018. Implementasi Metode Backpropagation Neural Network (BNN) dalam Sistem Klasifikasi Ketepatan Waktu Kelulusan Mahasiswa (Studi Kasus: Program Studi Sistem Informasi Universitas Jember). Berkala Sainstek 6(2): 97, Doi: 10.19184/bst.v6i2.9254.