Analisis Optimasi Fungsi Pelatihan Machine Learning Neural Network dalam Peramalan Kemiskinan

Sahat Sonang Sitanggang, Sarjon Defit, Mukhlis Ramadhan

Abstract


Banyak metode fungsi pelatihan dalam Machine Learning Neural Network yang digunakan dalam menyelesaikan masalah komputasi yang berkaitan dengan prediksi. Fungsi pelatihan yang digunakan pada Machine Learning metoda algoritma backpropagation dapat menghasilkan prediksi  yang berbeda, yang dipengaruhi oleh parameter dan data yang digunakan. Tujuan dari penelitian dilakukan untuk menganalisa performance dan keakuratan algoritma backpropagation standard serta mengoptimalkan fungsi pelatihan dengan algoritma Bayesian Regulation, dan One Step Secant. Dalam proses analisis, penelitian ini menggunakan Dataset jumlah kemiskinan di Indonesia dalam jangka waktu 12 tahun (tahun 2009 - 2020) yang terdiri dari 34 provinsi. Data diperoleh dari website Badan Pusat Statistik (BPS) Indonesia https://www.bps.go.id/. Berdasarkan pelatihan, pengujian, dan analisa yang dilakukan diperoleh hasil dari penelitian, bahwa model jaringan 5-9-1 menggunakan fungsi pelatihan Bayesian Regulation mampu melakukan optimasi yang lebih baik dengan percepatan waktu pelatihan, MSE Pengujian, Performance lebih rendah dibandingkan denga 2 metode yang lain, dengan demikian disimpulkan bahwa model jaringan 5-9-1 menggunakan algoritma Bayesian Regulation dapat digunakan untuk prediksi kemiskinan di Indonesia.


Keywords


Optimasi; Machine Learning; Backpropagation; Bayesian Regulation; One Step Secant; Kemiskinan

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References


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DOI: http://dx.doi.org/10.26418/jp.v7i3.50092

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