Penerapan Extreme Learning Machine (ELM) untuk Peramalan Laju Inflasi di Indonesia

Penulis

Adyan Nur Alfiyatin, Wayan Firdaus Mahmudy, Candra Fajri Ananda, Yusuf Priyo Anggodo

Abstrak

Inflasi merupakan salah satu indikator untuk mengukur perkembangan suatu bangsa. Apabila inflasi tidak terkontrol akan memberikan banyak dampak negative terhadap masyarakat disuatu negara. Ada banyak cara untuk mengendalikan inflasi, salah satunya dengan peramalan. Peramalan adalah suatu aktivitas untuk mengetahui kejadian di masa mendatang berdasarkan data masa lalu. Pada penelitian ini menggunakan metode kecerdasan buatan yakni extreme learning machine (ELM). Kelebihan ELM yaitu cepat dalam proses pembelajaran. Berdasarkan penggujian yang dilakukan metode ELM mendapatkan nilai kesalahan sebesar 0.0202008, lebih kecil dibandingkan dengan metode backpropagation sebesar 1.16035821. Hal tersebut membuktikan bahwa metode ELM sangat cocok digunakan untuk peramalan.

Abstract

Inflation is one indicator to measure the development of a nation. If inflation is not controlled will give many negative impacts to the people in a country. There are many ways to control inflation, one with forecasting. Forecasting is an activity to know future events based on past data. In this research using artificial intelligence method is extreme learning machine (ELM). The advantages of ELM are fast in the learning process. Based on ELM testing gets obtained an error value of 0.0202008, smaller than the backpropagation method of 1.16035821. It proves that ELM method is very suitable for forecasting.


 

Teks Lengkap:

PDF

Referensi


ANGGODO, Y.P. & CHOLISSODIN, I., 2017. Improve Interval Optimization of FLR using Auto-Speed Acceleration Algorithm. Telecomunication, Computing Electronics and Control (TEKOMNIKA), 16(1), pp.1–12.

ASCARI, G. & SBORDONE, A.M., 2014. The Macroeconomics of Trend Inflation. Journal of Economic Literature, 52(January 2012), pp.679–739. Available at: https://www.aeaweb.org/articles.php doi=10.1257/jel.52.3.679.

CHARNAVOKI, B.V. & DOLADO, J.J., 2017. American Economic Association The Effects of Global Shocks on Small Commodity-Exporting Economies : Lessons from Canada Author ( s ): Valery Charnavoki and Juan J . Dolado Published by : American Economic Association Stable URL :http://www.jstor.org/stab. , 6(2).

CHAVEZ-HURTADO, J.L. & CORTES-FREGOSO, J.H., 2013. Forecasting Mexican inflation using neural networks. 23rd International Conference on Electronics, Communications and Computing, CONIELECOMP 2013, pp.32–35.

HANDIKA, I.P.S., GIRIANTARI, I.A. & DHARMA, A., 2016. Perbandingan Metode Extreme Learning Machine dan Particle Swarm Optimization Extreme Learning Machine untuk Peramalan Jumlah Penjualan Barang. Teknologi Elektro, 15(1), pp.84–90.

HIDAYAT, R. & SUPRAPTO, 2012. Meminimalisasi Nilai Error Peramalan dengan Algoritma Extreme Learning Machine (ELM). Optimasi Sistem Industri, 11(1), pp.187–192.

HILL, T., CONNOR, M.O. & REMUS, W., 1996. Neural Network Models for Time Series Forecasts. Management Science, 42(7), pp.1082–1092.

HUANG, G. ET AL., 2015. Trends in extreme learning machines: A review. Neural Networks, 61, pp.32–48. Available at: http://dx.doi.org/10.1016/j.neunet.2014.10.001.

HUANG, G.-B., ZHU, Q.-Y. & SIEW, C.-K., 2004. Extreme learning machine: a new learning scheme of feed forward neural networks. 2004 IEEE International Joint Conference on Neural Networks (IEEE Cat. No.04CH37541), 2, pp.25–29.

HUANG, G. BIN, ZHU, Q.Y. & SIEW, C.K., 2006. Extreme learning machine: Theory and applications. Neurocomputing, 70(1–3), pp.489–501.

INDONESIA, B., 2017. Laporan Inflasi. Available at: http://www.bi.go.id/id/moneter/inflasi/data/Default.aspx.

NAFI’IYAH, N., 2016. Perbandingan Regresi Linear , Backpropagation Dan Fuzzy Mamdani Dalam Prediksi Harga Emas. Seminar Nasional Inovasi dan Aplikasi Teknologi di Industri, pp.291–296.

SARI, N.R., MAHMUDY, W.F., WIBAWA, A.P., ET AL., 2017. Enabling External Factors for Inflation Rate Forecasting using Fuzzy Neural System. International Journal of Electrical and Computer Engineering (IJECE), 7(5), pp.2746–2756.

SARI, N.R., MAHMUDY, W.F. & WIBAWA, A.P., 2016. Backpropagation on Neural Network Method for Inflation Rate Forecasting in Indonesia. International Journal Advance Soft Computing Application, 8(3).

SARI, N.R., MAHMUDY, W.F. & WIBAWA, A.P., 2017. The Effectiveness of Hybrid Backpropagation Neural Network Model and TSK Fuzzy Inference System for Inflation Forecasting. Journal of Telecommunication, Electronic and Computer Engineering, 9(2), pp.111–117.

SHETA, A.F., AHMED, S.E.M. & FARIS, H., 2015. A Comparison between Regression, Artificial Neural Networks and Support Vector Machines for Predicting Stock Market Index. International Journal of Advanced Research in Artificial Intelligence, 4(7), pp.55–63. Available at: http://thesai.org/Publications/ViewPaperVolume=4&Issue=7&Code=ijarai&SerialNo=10.

SUGIANTO, N.A., CHOLISSODIN, I. & WIDODO, A.W., 2018. Klasifikasi Keminatan Menggunakan Algoritme Extreme Learning Machine dan Particle Swarm Optimization untuk Seleksi Fitur ( Studi Kasus : Program Studi Teknik Informatika FILKOM UB ). Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer, 2(5), pp.1856–1865.

TIWARI, M., ADAMOWSKI, J. & ADAMOWSKI, K., 2016. Water demand forecasting using extreme learning machines. Journal of Water and Land Development, 28(1).

UTARI, G.A.D., ARIMURTI, T. & KURNIATI, I.N., 2012. Pertumbuhan Kredit Optimal. Buletin Ekonomi dan Perbankan, pp.3–36.

WERBOS, P.J., 1990. Backpropagation Through Time - What It Does and How to Do It. Proceedings of the IEEE, 78(10), pp.1550–1560.

ZHANG, L. & LI, J., 2012. Inflation Forecasting Using Support Vector Regression. 2012 Fourth International Symposium on Information Science and Engineering, pp.136–140. Available at: http://ieeexplore.ieee.org/document/6495313/.




DOI: http://dx.doi.org/10.25126/jtiik.201962900