Peramalan Angka Inflasi Kota Samarinda Menggunakan Metode Double Exponential Smoothing (Studi Kasus : Badan Pusat Statistik Kota Samarinda)

Abstract

Inflasi merupakan suatu kondisi kenaikan harga-harga umum secara terus menerus yang berhubungan dengan mekanisme pasar dinilai berdsarkan indeks harga konsumen (IHK) oleh Badan Pusat Statistik. Masalah yang dihadapi oleh Badan Pusat Statistik yaitu belum terdapat sistem yang dapat melakukan peramalan angka inflasi untuk 1 tahun mendatang yang dijadikan acuan untuk mengambil tindakan pencegahan. Maka dari itu diperlukan sistem yang dapat melakukan peramalan angka inflasi secara otomatis. Metode yang digunakan yaitu Double Expenontial Smoothing (DES) untuk mendapatkan hasil peramalan angka inflasi yang diproses pada nilai alpha 0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, setelah didapatkan hasil peramalan kemudian dicari nilai error terkecil menggunakan Mean Squade Error (MSE) pada nilai alpha 0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9. Dihasilkan Sistem Peramalan Angka Inflasi yang dapat dijadikan acauan dalam pengendalian inflasi yang telah diuji menggunakan 60 data angka inflasi, Hasil peramalan terbaik dengan error MSE terkecil pada alpha 0.3 yaitu 0.485239.

Alfa Value Scalability on Single and Double Exponential Smoothing Comparatives

“…In this DES method, the calculation is done twice to calculate the level and the trend. Still using the same sales data, the following is the DES calculation for the level using equation (2). In contrast, the movement uses equation (3).…”

“…Forecasting the inflation rate using the double exponential smoothing method and looking for the slightest error value with MSE and alpha values of 0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9. This study was able to predict the 2018 Inflation Figure Forecast in the city of Samarinda based on inflation data for 2013-2017 with the smallest error value at an alpha value of 0.3, which was measured using the mean square error [2].Predicting the number of train passengers in 2018 using the Single Exponential Smoothing and Double Exponential Smoothing methods than using checking the error values of SSE, MSE, and MAPE at alpha 1 and 0.5 values using train passenger training data from January 2006 -December 2015 and January 2016 -October 2017 as test data. The SSE accuracy value of 157444122 is found in the Double Exponential Smoothing method, and it can be concluded that train passengers in Indonesia will increase every month in 2018 [3].…”

“…An estimate or prediction is a conjecture about the occurrence of an event in the future [1]. This estimate can be obtained based on testing past conditions to predict the situation [2]. There are many methods for predicting data, one of which is the Exponential Smoothing Method which is a method for forecasting time series data by performing continuous improvement procedures [2] [3].…”

“…This estimate can be obtained based on testing past conditions to predict the situation [2]. There are many methods for predicting data, one of which is the Exponential Smoothing Method which is a method for forecasting time series data by performing continuous improvement procedures [2] [3]. The Exponential Smoothing method is usually used for short-term forecasting [4].…”

Optimasi Prediksi Inflasi Dengan Neural Network Pada Tahap Windowing Adakah Pengaruh Perbedaan Window Size

“…Peramalan inflasi dapat dilakukan dengan berbagai metode di antaranya adalah extreme learning machine [6], support vector regression [7], exponential smoothing [8], fuzzy time series [9], multiple regression [10], ensembel [11], dan neural network [10], [12], [13]. Metode neural network sendiri adalah salah satu metode kecerdasan buatan yang banyak digunakan dalam peramalan termasuk dalam peramalan inflasi di dunia [14][15] dan beberapa penelitian yang pernah dilakukan di Indonesia [10], [12], [13].…”