Skripsi

PERBANDINGAN METODE ARIMA, SARIMA, DAN LSTM DALAM MEMPREDIKSI HARGA BAWANG MERAH DI KOTA PALEMBANG

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Shallots are a food commodity that often experiences price fluctuations and is one of the contributors to inflation in the city of Palembang. This study compares the ARIMA, SARIMA, and LSTM methods for predicting shallot prices using daily data from January 2020 to October 2025. The research stages include data collection, preprocessing, visualization and decomposition, division of training and testing data with an 80:20 ratio, modeling, and performance evaluation using RMSE, MAE, and MAPE metrics. The results show that the ARIMA(1,1,1) method provides the most optimal performance, as indicated by the lowest error values compared to the other two methods. The SARIMA(1,1,1)(2,1,1)_7 model produces slightly higher error values but still outperforms the LSTM method. Meanwhile, the LSTM method yields the highest error in this study. These findings indicate that the pattern of shallot prices in Palembang tends to follow linear and seasonal trends that are not overly complex, meaning that classical statistical approaches remain superior to deep learning models in capturing these data patterns. This research provides practical contributions as a decision support tool for the government and business stakeholders in planning the distribution and stabilization of shallot prices in the city of Palembang.

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Detail Information

Series Title

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Call Number

T1942892026

Publisher

Indralaya : Prodi Sistem Informasi, Fakultas Ilmu Komputer Universitas Sriwijaya., 2026

Collation

92 hlm.; ilus.; tab.; 29 cm.

Language

Indonesia

ISBN/ISSN

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Classification

519.550 7

Content Type

Text

Media Type

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Carrier Type

-

Edition

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Subject(s)

Analisis Deret Waktu
Prodi Sistem Informasi

Specific Detail Info

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Statement of Responsibility

TUTI

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