Prediksi Indeks Harga Konsumen Komoditas Makanan Berbasis Cloud Computing Menggunakan Multilayer Perceptron

Soffa Zahara, Sugianto Sugianto

Abstract

Teknik prediksi merupakan salah satu area dalam data mining dimana menemukan pola dari sekumpulan data yang mengarah pada prediksi di masa depan. Prediksi dalam bidang ekonomi merupakan prediksi yang mendominasi karena merupakan salah satu parameter berkembangnya suatu negara. Indeks Harga Konsumen menggambarkan tingkat konsumsi barang dan jasa pada masyarakat yang dapat dijadikan acuan nilai inflasi. Mayoritas penelitian yang melakukan prediksi nilai Indeks Harga Konsumen sebelumnya hanya melakukan prediksi menggunakan nilai Indeks Harga Konsumen itu sendiri sebagai nilai input dan output. Penelitian ini membangun model peramalan dengan memanfaatkan multi variabel input yaitu 28 jenis harga bahan pokok harian sebagai nilai input untuk meramal nilai Indeks Harga Konsumen di kota Surabaya periode 2014 sampai 2018 dimana keseluruhan pembangunan model prediksi dilakukan di lingkungan Amazon Cloud Services. Sistem prediksi dibangun dengan algoritma Multilayer Perceptron dengan variasi arsitektur jumlah neuron, epoch, dan hidden layer. Berdasarkan hasil pengujian, akurasi terbaik dengan nilai RMSE 3.380  dihasilkan oleh konfigurasi 2 hidden layer,  hidden layer pertama dan kedua mempunyai neuron masing-masing berjumlah 10 dengan epoch sebesar 1000.

Keywords

indeks harga konsumen; multilayer perceptron; cloud computing; prediksi multivariabel;

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DOI: https://doi.org/10.31328/jointecs.v6i1.1702

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