Analisis Perbandingan Akurasi Metode Moving Average dan Metode Exponensial Smoothing dalam Memprediksi Kapasitas Produksi Padi Nasional

Mardiansyah Mardiansyah; Firman Amir

doi:10.34010/komputa.v12i2.10602

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Affiliations

Mardiansyah Mardiansyah
Universitas Pamulang

Firman Amir
Universitas Pamulang

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Analisis Perbandingan Akurasi Metode Moving Average dan Metode Exponensial Smoothing dalam Memprediksi Kapasitas Produksi Padi Nasional

Mardiansyah Mardiansyah ,
Firman Amir

Vol 12 No 2 (2023): Komputa : Jurnal Ilmiah Komputer dan Informatika

DOI 10.34010/komputa.v12i2.10602

Abstract

Pengelolaan persediaan padi merupakan aspek penting yang perlu ditingkatkan oleh para pemangku kepentingan guna mencapai keseimbangan antara persediaan dan konsumsi beras. Bullwhip Effect (BE) telah menjadi perhatian khusus dalam rantai pasokan selama pandemi, terutama dengan adanya komponen permintaan musiman dan nonmusiman. Peramalan kebutuhan produksi padi diperlukan untuk mengatasi masalah dalam pengolahan data dan situasi di lapangan. Perangkat lunak seperti Production and Operations Management (POM) dapat digunakan untuk peramalan menggunakan logika fuzzy. Dalam era Industri 4.0, sustainable smart manufacturing menjadi hal yang penting. Proyeksi kebutuhan produksi beras nasional dilakukan dengan menggunakan metode moving average dan metode exponential smoothing. Pengujian akurasi dilakukan dengan peramalan menggunakan metode moving average dan exponential smoothing dengan data produksi padi tahun 2010-2019, kemudian hasil peramalan tahun 2020 dari kedua metode tersebut akan dibandingkan dengan data real dan akan diketahui metode mana yang paling mendekati data real. Tujuan utama penelitian ini adalah untuk membandingkan dua metode yaitu metode moving average dan metode exponential smoothing yang digunakan pada perangkat lunak berbasis fuzzy. Hasil pengujian akurasi peramalan produksi beras dengan menggunakan metode moving average dan exponential smoothing yang telah dilakukan menunjukkan bahwa metode moving average lebih akurat dengan selisih 1,0089% dari data sebenarnya, sedangkan metode exponential smoothing memiliki selisih 12,0051% dari data sebenarnya.