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All Journal CAUCHY: Jurnal Matematika Murni dan Aplikasi BAREKENG: Jurnal Ilmu Matematika dan Terapan
Vionita, Anggi Triya
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Computer Science & IT Control & Systems Engineering Economics, Econometrics & Finance Energy Engineering Mathematics Mechanical Engineering Physics Transportation

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PREDICTION OF AVERAGE TEMPERATURE IN BANYUWANGI REGENCY USING SARIMA Syahzaqi, Idrus; Sediono, Sediono; Dyaksa, Mega Kurnia; Vionita, Anggi Triya; Ghasani, Anisah Nabilah
BAREKENG: Jurnal Ilmu Matematika dan Terapan Vol 19 No 3 (2025): BAREKENG: Journal of Mathematics and Its Application
Publisher : PATTIMURA UNIVERSITY

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30598/barekengvol19iss3pp2207-2218

Abstract

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.
World Gold Price Prediction After United State Election Using Pulse Function Intervention Analysis Sediono, Sediono; Vionita, Anggi Triya; Renianti, Fayza Shafira
CAUCHY: Jurnal Matematika Murni dan Aplikasi Vol 10, No 2 (2025): CAUCHY: JURNAL MATEMATIKA MURNI DAN APLIKASI
Publisher : Mathematics Department, Universitas Islam Negeri Maulana Malik Ibrahim Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18860/cauchy.v10i2.33706

Abstract

The United States (US) election in November 2024 had a significant impact on global economic conditions, especially world gold prices. A key effect was the strengthening of the US dollar, leading to a sharp drop in gold prices to 2,582.1 USD. This study aims to model and forecast gold prices using the pulse function intervention analysis method. The analysis uses weekly data, with the intervention point set in the second week of November 2024 (t = 101). The best pre-intervention model was identified as ARIMA(0,2,1), while the best intervention model had orders b = 1, r = 0, s = 0, based on analysis of the Cross Correlation Function (CCF). The resulting model shows significant parameters and strong performance, with a MAPE of 1.51\%, AIC of -530.394, SBC of -525.030, and MSE of 0.0002037. Forecasts indicate gold prices are likely to increase again through the end of July 2025. These findings show that the pulse intervention model effectively captures external shocks, such as post-election dollar appreciation. The study improves our understanding of the dynamics of global gold prices and offers insights that can help policymakers develop strategies to mitigate the risks caused by fluctuations in the external market.
Co-Authors Dyaksa, Mega Kurnia Ghasani, Anisah Nabilah Idrus Syahzaqi Renianti, Fayza Shafira Sediono, Sediono