Comparison of Single Exponential Smoothing and Double Moving Average Algorithms to Forecast Beef Production
Beef is considered a high-value commodity as it is an important source of protein. Interest in beef continues to rise. Beef production has risen sharply in the past decade, but declined by 7,240.68 tons in 2020 amid coronavirus lockdowns. After that, in 2021, production reached 16,381.81 tons and co...
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| Format: | Article |
| Language: | English |
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State Islamic University Sunan Kalijaga
2024-10-01
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| Series: | IJID (International Journal on Informatics for Development) |
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| Online Access: | https://ejournal.uin-suka.ac.id/saintek/ijid/article/view/4663 |
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| author | Tundo Rachmat Hidayat Insani Rasiban Untung Suropati |
| author_facet | Tundo Rachmat Hidayat Insani Rasiban Untung Suropati |
| author_sort | Tundo |
| collection | DOAJ |
| description | Beef is considered a high-value commodity as it is an important source of protein. Interest in beef continues to rise. Beef production has risen sharply in the past decade, but declined by 7,240.68 tons in 2020 amid coronavirus lockdowns. After that, in 2021, production reached 16,381.81 tons and continued to increase in 2022 and 2023. A precise method is required to forecast beef production. One way to predict beef production in Jakarta is using the Single Exponential Smoothing and Double Moving Average methods. The two algorithms are compared to get the lowest error rate. The methodology used in this research is the SEMMA (Sample, Explore, Modify, Model, and Assess) methodology. According to SAS Institute Inc., there are five stages in developing a system using the SEMMA methodology. After analyzing using MAPE, it is found that the algorithm with the smallest error value is the Single Exponential Smoothing algorithm with a percentage in the monthly period of 16% while for the annual period, it is 27% compared to other algorithms. The forecasting is quite accurate because the MAPE value for each algorithm used has an error of less than 31%. |
| format | Article |
| id | doaj-art-9cd64a7ec2af4654935a2cae371b4d2b |
| institution | DOAJ |
| issn | 2252-7834 2549-7448 |
| language | English |
| publishDate | 2024-10-01 |
| publisher | State Islamic University Sunan Kalijaga |
| record_format | Article |
| series | IJID (International Journal on Informatics for Development) |
| spelling | doaj-art-9cd64a7ec2af4654935a2cae371b4d2b2025-08-20T03:18:05ZengState Islamic University Sunan KalijagaIJID (International Journal on Informatics for Development)2252-78342549-74482024-10-0113144845910.14421/ijid.2024.46634287Comparison of Single Exponential Smoothing and Double Moving Average Algorithms to Forecast Beef ProductionTundo0Rachmat Hidayat Insani1Rasiban2Untung Suropati3Sekolah Tinggi Ilmu Komputer Cipta Karya Informatika (STIKOM CKI)Sekolah Tinggi Ilmu Komputer Cipta Karya Informatika (STIKOM CKI)Sekolah Tinggi Ilmu Komputer Cipta Karya Informatika (STIKOM CKI)Sekolah Tinggi Ilmu Komputer Cipta Karya Informatika (STIKOM CKI)Beef is considered a high-value commodity as it is an important source of protein. Interest in beef continues to rise. Beef production has risen sharply in the past decade, but declined by 7,240.68 tons in 2020 amid coronavirus lockdowns. After that, in 2021, production reached 16,381.81 tons and continued to increase in 2022 and 2023. A precise method is required to forecast beef production. One way to predict beef production in Jakarta is using the Single Exponential Smoothing and Double Moving Average methods. The two algorithms are compared to get the lowest error rate. The methodology used in this research is the SEMMA (Sample, Explore, Modify, Model, and Assess) methodology. According to SAS Institute Inc., there are five stages in developing a system using the SEMMA methodology. After analyzing using MAPE, it is found that the algorithm with the smallest error value is the Single Exponential Smoothing algorithm with a percentage in the monthly period of 16% while for the annual period, it is 27% compared to other algorithms. The forecasting is quite accurate because the MAPE value for each algorithm used has an error of less than 31%.https://ejournal.uin-suka.ac.id/saintek/ijid/article/view/4663beefdouble moving averageforecastingmapesingle exponential smoothing |
| spellingShingle | Tundo Rachmat Hidayat Insani Rasiban Untung Suropati Comparison of Single Exponential Smoothing and Double Moving Average Algorithms to Forecast Beef Production IJID (International Journal on Informatics for Development) beef double moving average forecasting mape single exponential smoothing |
| title | Comparison of Single Exponential Smoothing and Double Moving Average Algorithms to Forecast Beef Production |
| title_full | Comparison of Single Exponential Smoothing and Double Moving Average Algorithms to Forecast Beef Production |
| title_fullStr | Comparison of Single Exponential Smoothing and Double Moving Average Algorithms to Forecast Beef Production |
| title_full_unstemmed | Comparison of Single Exponential Smoothing and Double Moving Average Algorithms to Forecast Beef Production |
| title_short | Comparison of Single Exponential Smoothing and Double Moving Average Algorithms to Forecast Beef Production |
| title_sort | comparison of single exponential smoothing and double moving average algorithms to forecast beef production |
| topic | beef double moving average forecasting mape single exponential smoothing |
| url | https://ejournal.uin-suka.ac.id/saintek/ijid/article/view/4663 |
| work_keys_str_mv | AT tundo comparisonofsingleexponentialsmoothinganddoublemovingaveragealgorithmstoforecastbeefproduction AT rachmathidayatinsani comparisonofsingleexponentialsmoothinganddoublemovingaveragealgorithmstoforecastbeefproduction AT rasiban comparisonofsingleexponentialsmoothinganddoublemovingaveragealgorithmstoforecastbeefproduction AT untungsuropati comparisonofsingleexponentialsmoothinganddoublemovingaveragealgorithmstoforecastbeefproduction |