Price Prediction Of Basic Material Using ARIMA Forecasting Method Through Open Data Sumedang District
| Dublin Core | PKP Metadata Items | Metadata for this Document | |
| 1. | Title | Title of document | Price Prediction Of Basic Material Using ARIMA Forecasting Method Through Open Data Sumedang District |
| 2. | Creator | Author's name, affiliation, country | Kusnawi Kusnawi; Universitas Amikom Yogyakarta; Indonesia |
| 2. | Creator | Author's name, affiliation, country | M Andika Fadhil Eka Putra; Universitas Amikom Yogyakarta; Indonesia |
| 2. | Creator | Author's name, affiliation, country | Joang Ipmawati; Universitas Nahdlatul Ulama Yogyakarta |
| 3. | Subject | Discipline(s) | Sistem Informasi; Informatika; Ilmu komputer |
| 3. | Subject | Keyword(s) | |
| 4. | Description | Abstract | In the era of Industry 4.0, characterized by the abundance of data, there are many opportunities to carry out various data-related processes. One of these is the data forecasting process which has been widely used. By analyzing data, we can make predictions and make decisions automatically. For example, one of the problems that decision-makers, especially in Kabupaten Sumedang, must solve is the changes in the prices of basic commodities that are essential for society's consumption. The prices of these commodities in the market tend to fluctuate in the short or long term. By analyzing the available data, we can predict the direction of changes in the prices of basic commodities in the market. In this study, the ARIMA model is used, which is one of the time series models that can be used to predict the possibility of an increase or decrease in the prices of basic commodities in the market in Kabupaten Sumedang. The ARIMA model uses the previous day's price data as a benchmark to predict the prices of basic commodities in the future. After being analyzed, the results of the model will be in several ARIMA model forms. An efficient ARIMA model will be used to model the prices of basic food commodities. This research produced the three best ARIMA models, namely ARIMA(1-1-1) for broiler chicken meat, ARIMA(0-1-1) for shallots, and ARIMA(0-1-1) for garlic. The accuracy test results percentage error for the best model using MAPE show an average value below 10%. Keywords: Food staples, Forecasting, Time Series, ARIMA, MAPE |
| 5. | Publisher | Organizing agency, location | Program Studi Sistem Informasi Fakultas Teknik dan Ilmu Komputer |
| 6. | Contributor | Sponsor(s) | |
| 7. | Date | (YYYY-MM-DD) | 2023-05-31 |
| 8. | Type | Status & genre | Peer-reviewed Article |
| 8. | Type | Type | |
| 9. | Format | File format | |
| 10. | Identifier | Uniform Resource Identifier | https://sistemasi.org/index.php/stmsi/article/view/2282 |
| 10. | Identifier | Digital Object Identifier (DOI) | https://doi.org/10.32520/stmsi.v12i2.2282 |
| 11. | Source | Title; vol., no. (year) | SISTEMASI; Vol 12, No 2 (2023): Sistemasi: Jurnal Sistem Informasi |
| 12. | Language | English=en | id |
| 13. | Relation | Supp. Files | |
| 14. | Coverage | Geo-spatial location, chronological period, research sample (gender, age, etc.) | |
| 15. | Rights | Copyright and permissions |
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