IMPLEMENTASI MODEL AUTOREGRESSIVE INTEGRATED MOVING AVERAGE (ARIMA) UNTUK MELAKUKAN PREDIKSI HARGA BAWANG MERAH (Studi Kasus: Bahan Pangan di Provinsi Banten)

AGUNG, FATIMA PUSPA PERTIWI PUTRI (2024) IMPLEMENTASI MODEL AUTOREGRESSIVE INTEGRATED MOVING AVERAGE (ARIMA) UNTUK MELAKUKAN PREDIKSI HARGA BAWANG MERAH (Studi Kasus: Bahan Pangan di Provinsi Banten). S1 thesis, Universitas Mercu Buana Jakarta.

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Abstract

Shallots are one of the food commodities that experience periodic price fluctuations in Indonesia. The increase and decrease in prices that occur every month affect market stability and have a direct impact on retail traders in this sector. In this research, predictions and in-depth analysis were carried out on the fluctuation patterns of shallot food prices in Banten province. This research was conducted to identify and predict the behavior of shallot prices in the future. The research process involves a series of important steps, namely problem identification, collecting historical data on shallot food prices, data preprocessing, and applying the ARIMA (Auto Regressive Integrated Moving Average) method. The ARIMA method is a method that is often used to forecast patterned data. The research results show that the best ARIMA model for predicting shallot food prices is ARIMA (5,1,0)(5,1,0)10, using data from March 2021 to May 2024 as a benchmark. Evaluation of prediction quality is carried out by calculating the Root Mean Square Error (RMSE), which is a standard measure for evaluating the level of model prediction error. The RMSE results obtained were 29,36378. Based on this research, it can be concluded that although the ARIMA model provides acceptable predictions, there are variations between the predicted values and the actual observed values of shallot prices. This research shows the importance of continuing to develop and improve forecasting models by considering more variables and factors that influence future onion prices. Keywords: ARIMA, shallots, prediction, RMSE Bawang merah adalah salah satu komoditas pangan yang mengalami fluktuasi harga secara periodik di Indonesia. Kenaikan dan penurunan harga yang terjadi setiap bulannya mempengaruhi stabilitas pasar dan berdampak langsung pada pedagang eceran di sektor ini. Pada penelitian ini, dilakukan prediksi dan analisis mendalam terhadap pola fluktuasi harga pangan bawang merah di provinsi Banten. Penelitian ini dilakukan untuk mengidentifikasi dan memprediksi perilaku harga bawang merah di masa yang akan datang. Proses penelitian melibatkan serangkaian langkah yaitu identifikasi masalah, pengumpulan data historis harga pangan bawang merah, pre-processing data, dan penerapan metode ARIMA (Auto Regressive Integrated Moving Average). Metode ARIMA merupakan metode yang sering digunakan dalam melakukan peramalan pada data berpola. Hasil penelitian menunjukkan bahwa model ARIMA terbaik untuk memprediksi harga pangan bawang merah adalah ARIMA (5,1,0) (5,1,0)10. Evaluasi kualitas prediksi dilakukan dengan menghitung Root Mean Square Error (RMSE), yang merupakan ukuran standar untuk mengevaluasi tingkat kesalahan prediksi model. Hasil RMSE yang diperoleh sebesar 29,36378. Berdasarkan penelitian ini, dapat disimpulkan bahwa meskipun model ARIMA memberikan prediksi yang dapat diterima, terdapat variasi antara nilai yang diprediksi dan nilai aktual harga bawang merah yang diamati. Penelitian ini menunjukkan pentingnya terus mengembangkan dan memperbaiki model peramalan dengan mempertimbangkan lebih banyak variabel dan faktor yang memengaruhi harga bawang merah di masa depan. Kata kunci: ARIMA, bawang merah, prediksi, RMSE

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