.png)
Artificial Neural Network Pada Industri Non Migas Sebagai Langkah Menuju Revolusi Industri 4.0
Abstract
The research conducted aims to make predictions with artificial neural metwork (backpopagation) and sensitivity analysis in the non-oil processing industry for the value of industrial exports. Data was obtained from the Badan Pusat Statistik (BPS) in collaboration with the Ministry of Industry of the Republic of Indonesia in the last 7 years (2011-2017). The process is carried out by dividing the data into 2 parts (training and testing) to obtain the best architectural model. The data processing uses the help of Matlab 6.0 software. Model selection is done by try and try to get the best architectural model. In this study using 7 architectural models (15-2-1; 15-5-1; 15-10-1; 15-15-1; 15-2-5-1; 15-5-10-1 and 15- 10-5-1) who have been trained and tested. By using the help of Matlab 6.0 software, the best architectural model is obtained 15-2-1 with an accuracy rate of 93%, epoch training = 189,881, MSE testing = 0.001167108 and MSE training = 0,000999622. The best architecture will be continued to predict the non-oil industry based on the most dominant export value using sensitivity analysis. From the architectural model a prediction of 5 out of 15 non-oil and gas industries contributes: Food & Beverage Industry, Textile & Apparel Industry, Basic Metal Industry, Rubber Industry, Rubber and Plastic Goods and Metal Goods Industry, Not Machines and Equipment , Computers, Electronics and Optics.
Keywords
Full Text:
PDFReferences
B. N. P. D. S. E. dan Moneter, “Laporan Pemetaan Ekonomi Sektor Industri NonMigas,” Bank Indonesia, pp. 1–33, 2006.
K. Perindustrian, “Analisis Perkembangan Industri 2017,” Kementrian Perindustrian Republik Indonesia, pp. 1–32, 2017.
D. M. Sinaga, S. M. Dewi, and A. P. Windarto, “Penerapan Algoritma ELECTRE Pada Pemilihan Produk Skincare,” vol. 13, no. 2, 2018.
Sumijan, A. P. Windarto, A. Muhammad, and Budiharjo, “Implementation of Neural Networks in Predicting the Understanding Level of Students Subject,” Int. J. Softw. Eng. Its Appl., vol. 10, no. 10, pp. 189–204, 2016.
A. P. Windarto, S. Tinggi, I. Komputer, and T. Bangsa, “Implementation of Data Mining on Rice Imports by Major Country of Origin Implementation of Data Mining on Rice Imports by Major Country of Origin Using Algorithm Using K-Means Clustering Method,” no. November, 2017.
A. Wanto and A. P. Windarto, “Analisis Prediksi Indeks Harga Konsumen Berdasarkan Kelompok Kesehatan Dengan Menggunakan Metode Backpropagation,” J. Penelit. Tek. Inform., vol. 2, no. 2, pp. 37–44, 2017.
T. Imandasari and A. P. Windarto, “Sistem Pendukung Keputusan dalam Merekomendasikan Unit Terbaik di PDAM Tirta Lihou Menggunakan Metode Promethee,” J. Teknol. dan Sist. Komput., vol. 5, no. 4, p. 159, 2017.
A. P. W. Budiharjo and A. Muhammad, “Comparison of Weighted Sum Model and Multi Attribute Decision Making Weighted Product Methods in Selecting the Best Elementary School in Indonesia,” Int. J. Softw. Eng. Its Appl., vol. 11, no. 4, pp. 69–90, 2017.
A. P. Windarto, M. R. Lubis, and Solikhun, “Model Arsitektur Neural Network Dengan Backpropogation Pada Prediksi Total Laba Rugi Komprehensif Bank Umum Konvensional,” Kumpul. J. Ilmu Komput., vol. 5, no. 2, pp. 147–158, 2018.
T. Budiharjo, Soemartono, T., Windarto, A.P., Herawan, “Predicting school participation in indonesia using back-propagation algorithm model,” Int. J. Control Autom., 2018.
A. P. Windarto, M. R. Lubis, and Solikhun, “Implementasi Jst Pada Prediksi Total Laba Rugi Komprehensif Bank Umum Konvensional Dengan Backpropagation,” J. Teknol. Inf. dan Ilmu Komput., vol. 5, no. 4, pp. 411–418, 2018.
M. N. H. Siregar, “Neural Network Analysis With Backpropogation In Predicting Human Development Index ( HDI ) Component by Regency / City In North Sumatera,” I n t e r n a t i o n a l Jo u r n a l O f I n f o r m a t i o n S yst e m T e c h n o l ogy, vol. 1, no. 1, pp. 22–33, 2017.
Indrabayu, N. Harun, M. S. Pallu, and A. Achmad, “Prediksi Curah Hujan Di Wilayah Makassar Menggunakan Metode Wavelet - Neural Network,” J. Ilm. “Elektrikal Enjiniring” UNHAS, vol. 9, no. 2, p. 10, 2011.
M. M. Hidayat, D. Purwitasari, and H. Ginardi, “Analisis Prediksi Drop Out Berdasarkan Perilaku Sosial Mahasiswa Dalam Educational Data Mining Menggunakan Jaringan Saraf Tiruan,” 2013.
DOI: https://doi.org/10.30743/infotekjar.v4i1.1682
Refbacks
- There are currently no refbacks.
Copyright (c) 2019 Iin Parlina, Anjar Wanto, Agus Perdana Windarto
This work is licensed under a Creative Commons Attribution 4.0 International License.
InfoTekJar (Jurnal Nasional Informatika dan Teknologi Jaringan)
Program Studi Teknik Informatika - Universitas Islam Sumatera Utara
Website : http://jurnal.uisu.ac.id/index.php/infotekjar/index
Email : infotekjar@ft.uisu.ac.id
InfoTekJar : Jurnal Nasional Informatika dan Teknologi Jaringan) is licensed under a Creative Commons Attribution 4.0 International License