PREDICTION OF AVERAGE TEMPERATURE IN BANYUWANGI REGENCY USING SARIMA
Climate change due to human activity has significantly impacted increasing global average temperatures, including in Banyuwangi Regency, East Java. The impact is felt in several sectors, such as agriculture, tourism, and health. As a preventive measure to minimize the adverse effects that will occur...
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Universitas Pattimura
2025-07-01
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| Series: | Barekeng |
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| Online Access: | https://ojs3.unpatti.ac.id/index.php/barekeng/article/view/17469 |
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| author | Idrus Syahzaqi Sediono Sediono Mega Kurnia Dyaksa Anggi Triya Vionita Anisah Nabilah Ghasani |
| author_facet | Idrus Syahzaqi Sediono Sediono Mega Kurnia Dyaksa Anggi Triya Vionita Anisah Nabilah Ghasani |
| author_sort | Idrus Syahzaqi |
| collection | DOAJ |
| description | Climate change due to human activity has significantly impacted increasing global average temperatures, including in Banyuwangi Regency, East Java. The impact is felt in several sectors, such as agriculture, tourism, and health. As a preventive measure to minimize the adverse effects that will occur in the future, an accurate prediction of the average temperature of Banyuwangi Regency is needed. This research used secondary data from the official website of the Central Statistics Agency (BPS) of Banyuwangi Regency per month from January 2012 to December 2023. Predictions are made using the seasonal autoregressive integrated moving average (SARIMA) approach. The best model is selected based on its fulfillment of stationarity, the significance of its parameters, and compliance with the assumptions of normality and white noise. From this method, the best model obtained to predict the average temperature of Banyuwangi Regency is the probabilistic SARIMA (1,0,0)(0,1,1)12. The probabilistic SARIMA model treats both parameters and forecasts as probability distributions. The average temperature of Banyuwangi Regency is obtained for the next year, namely from January 2023 to December 2023, with a MAPE of 1.63%. With an accuracy rate of 98.37%, it can be said that the probabilistic SARIMA (1,0,0)(0,1,1)12 model is accurate in predicting the average temperature of Banyuwangi Regency in the future. Thus, the prediction of the average temperature of Banyuwangi Regency is expected to help the community and government manage the impact of erratic climate change to improve the welfare of all Banyuwangi people. |
| format | Article |
| id | doaj-art-96deece9f01a4e4aab02ce3d0789b531 |
| institution | DOAJ |
| issn | 1978-7227 2615-3017 |
| language | English |
| publishDate | 2025-07-01 |
| publisher | Universitas Pattimura |
| record_format | Article |
| series | Barekeng |
| spelling | doaj-art-96deece9f01a4e4aab02ce3d0789b5312025-08-20T03:02:54ZengUniversitas PattimuraBarekeng1978-72272615-30172025-07-011932207221810.30598/barekengvol19iss3pp2207-221817469PREDICTION OF AVERAGE TEMPERATURE IN BANYUWANGI REGENCY USING SARIMAIdrus Syahzaqi0Sediono Sediono1Mega Kurnia Dyaksa2Anggi Triya Vionita3Anisah Nabilah Ghasani4Statistics Study Program, Faculty of Science and Technology, Universitas Airlangga, IndonesiaStatistics Study Program, Faculty of Science and Technology, Universitas Airlangga, IndonesiaStatistics Study Program, Faculty of Science and Technology, Universitas Airlangga, IndonesiaStatistics Study Program, Faculty of Science and Technology, Universitas Airlangga, IndonesiaStatistics Study Program, Faculty of Science and Technology, Universitas Airlangga, IndonesiaClimate change due to human activity has significantly impacted increasing global average temperatures, including in Banyuwangi Regency, East Java. The impact is felt in several sectors, such as agriculture, tourism, and health. As a preventive measure to minimize the adverse effects that will occur in the future, an accurate prediction of the average temperature of Banyuwangi Regency is needed. This research used secondary data from the official website of the Central Statistics Agency (BPS) of Banyuwangi Regency per month from January 2012 to December 2023. Predictions are made using the seasonal autoregressive integrated moving average (SARIMA) approach. The best model is selected based on its fulfillment of stationarity, the significance of its parameters, and compliance with the assumptions of normality and white noise. From this method, the best model obtained to predict the average temperature of Banyuwangi Regency is the probabilistic SARIMA (1,0,0)(0,1,1)12. The probabilistic SARIMA model treats both parameters and forecasts as probability distributions. The average temperature of Banyuwangi Regency is obtained for the next year, namely from January 2023 to December 2023, with a MAPE of 1.63%. With an accuracy rate of 98.37%, it can be said that the probabilistic SARIMA (1,0,0)(0,1,1)12 model is accurate in predicting the average temperature of Banyuwangi Regency in the future. Thus, the prediction of the average temperature of Banyuwangi Regency is expected to help the community and government manage the impact of erratic climate change to improve the welfare of all Banyuwangi people.https://ojs3.unpatti.ac.id/index.php/barekeng/article/view/17469average temperaturebanyuwangi regencysarima |
| spellingShingle | Idrus Syahzaqi Sediono Sediono Mega Kurnia Dyaksa Anggi Triya Vionita Anisah Nabilah Ghasani PREDICTION OF AVERAGE TEMPERATURE IN BANYUWANGI REGENCY USING SARIMA Barekeng average temperature banyuwangi regency sarima |
| title | PREDICTION OF AVERAGE TEMPERATURE IN BANYUWANGI REGENCY USING SARIMA |
| title_full | PREDICTION OF AVERAGE TEMPERATURE IN BANYUWANGI REGENCY USING SARIMA |
| title_fullStr | PREDICTION OF AVERAGE TEMPERATURE IN BANYUWANGI REGENCY USING SARIMA |
| title_full_unstemmed | PREDICTION OF AVERAGE TEMPERATURE IN BANYUWANGI REGENCY USING SARIMA |
| title_short | PREDICTION OF AVERAGE TEMPERATURE IN BANYUWANGI REGENCY USING SARIMA |
| title_sort | prediction of average temperature in banyuwangi regency using sarima |
| topic | average temperature banyuwangi regency sarima |
| url | https://ojs3.unpatti.ac.id/index.php/barekeng/article/view/17469 |
| work_keys_str_mv | AT idrussyahzaqi predictionofaveragetemperatureinbanyuwangiregencyusingsarima AT sedionosediono predictionofaveragetemperatureinbanyuwangiregencyusingsarima AT megakurniadyaksa predictionofaveragetemperatureinbanyuwangiregencyusingsarima AT anggitriyavionita predictionofaveragetemperatureinbanyuwangiregencyusingsarima AT anisahnabilahghasani predictionofaveragetemperatureinbanyuwangiregencyusingsarima |