Application of the Arima Method to Prediction Maximum Rainfall at Central Java Climatological Station

Authors

  • Zauyik Nana Ruslana Department of Information Technology, Fakultas Teknologi Informasi dan Industri, Universitas Stikubank Semarang City, Indonesia
  • Rudi Setyo Prihatin Department of Information Technology, Fakultas Teknologi Informasi dan Industri, Universitas Stikubank Semarang City, Indonesia
  • Sulistiyowati Department of Information Technology, Fakultas Teknologi Informasi dan Industri, Universitas Stikubank Semarang City, Indonesia
  • Kristiawan Nugroho Universitas Stikubank

DOI:

10.33395/sinkron.v8i4.13984

Keywords:

ARIMA; MAPE; sample; rainfall; prediction; maximum.

Abstract

The existence of extreme weather that is difficult to predict results in frequent hydrometeorological disasters. ARIMA is a prediction method that can capture trend patterns, seasonal cycles, and random fluctuations that are often found in patterned data. Although many samples of rain data collection points are needed to produce denser data, one point can be considered to represent an area that is not too large, such as Semarang City. This method is quite accurate for short-term forecasts, with the results of monthly maximum rainfall forecasts in 2023 showing varying MAPE values. For the 12-month forecast, prediction results range from fair to very accurate. The 7-month forecast also shows decent to very accurate results. However, the 5-month forecast shows less accurate results. This shows that ARIMA can be a useful method in forecasting monthly maximum rainfall, especially during the dry season. The application of ARIMA in Semarang City can help in planning hydrometeorological disaster mitigation, considering that the Semarang City area often experiences extreme weather that is difficult to predict. Thus, the use of ARIMA can provide significant benefits in preparing for and reducing the impact of hydrometeorological disasters in the region. In addition, with more accurate forecasts, the government and society can take preventative steps earlier, such as better water management, creating an adequate drainage system, and increasing public awareness of the threat of disasters. Therefore, this research emphasizes the importance of using reliable prediction methods such as ARIMA to improve preparedness in dealing with hydrometeorological disasters.

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References

Ariyanti, V. P., & Tristyanti Yusnitasari. (2023). Comparison of ARIMA and SARIMA for Forecasting Crude Oil Prices. Jurnal RESTI (Rekayasa Sistem Dan Teknologi Informasi), 7(2), 405–413. https://doi.org/10.29207/resti.v7i2.4895

Asy’ari, M. K., Sitanggang, V. S. S., & Musyafa’, A. (2023). Perancangan Sistem Prediksi Daya Listrik PLTB Sidrap Menggunakan Model Autoregressive. Seminar Nasional Teknik Elektro, Sistem Informasi, Dan Teknik Informatika, 144–151. http://ejurnal.itats.ac.id/snestik/article/view/4048

Ma, L., Hu, C., Lin, R., & Han, Y. (2018). ARIMA model forecast based on EViews software. IOP Conference Series: Earth and Environmental Science, 208(1). https://doi.org/10.1088/1755-1315/208/1/012017

Mardianto, I., Muhamad Ichsan Gunawan, Dedy Sugiarto, & Abdul Rochman. (2020). Comparison of Rice Price Forecasting Using the ARIMA Method on Amazon Forecast and Sagemaker. Jurnal RESTI (Rekayasa Sistem Dan Teknologi Informasi), 4(3), 537–543. https://doi.org/10.29207/resti.v4i3.1902

Misshuari, I. W., Kurniyaningrum, E., & Saily, R. (2023). Application of Arima Method for Rainfall Forecasting in Asahan Region. Indonesian Journal of Construction Engineering and Sustainable Development (Cesd), 6(2), 22–28. https://doi.org/10.25105/cesd.v6i2.18815

Mulyani, R., Sari, Y. P., & Sumantriyadi, S. (2022). Forecasting Produksi Perikanan Budidaya Di Kota Palembang Dengan Metode Autoregressive Integrated Moving Average (ARIMA). Sainmatika: Jurnal Ilmiah Matematika Dan Ilmu Pengetahuan Alam, 19(2), 163–174. https://doi.org/10.31851/sainmatika.v19i2.9164

Nabillah, I., & Rangadara, I. (2020). Mean Absolute Percentage Error untuk Evaluasi Hasil Prediksi Komoditas Laut. JOINS (Journal of Information System), 5(2), 250–255. https://doi.org/10.33633/joins.v5i2.3900

Nanda Tria Lestari, & Witanti, A. (2023). Analisis Prediksi Kasus DBD Berdasarkan Faktor Cuaca Dengan Multivariat ARIMA. Petir, 16(2), 228–236. https://doi.org/10.33322/petir.v16i2.2117

Nandarie, A. C. A., Al Badri, A. A., & Haryanto, Y. D. (2023). Optimalisasi Model ARIMA dalam Prakiraan Curah Hujan di Jambi. GEOGRAPHIA : Jurnal Pendidikan Dan Penelitian Geografi, 4(1), 39–43. https://doi.org/10.53682/gjppg.v4i1.5776

Nensi, A. I. E., Elevenny, R. Y., Sukarna, Kurniawati, I., & Muflihah. (2023). Perbandingan Akurasi ARIMA dan Backpropogation dalam Memprediksi Intensitas Curah Hujan Kota Makassar. Integrated Lab Journal, 11(02), 12–21. https://ejournal.uin-suka.ac.id/pusat/integratedlab/article/view/3205%0Ahttps://ejournal.uin-suka.ac.id/pusat/integratedlab/article/download/3205/2103

Novandro, D., Kawanda, A., Sipil, J. T., & Trisakti, U. (2023). METODE DINAMIK TERHADAP UJI BEBAN STATIK RELIABILITY ANALYSIS OF DYNAMIC METHOD DRIVEN PILE FOUNDATION BEARING CAPACITY AGAINST STATIC LOAD TEST. 01(02), 324–329.

Nurman, S., Nusrang, M., & Sudarmin. (2022). Analysis of Rice Production Forecast in Maros District Using the Box-Jenkins Method with the ARIMA Model. ARRUS Journal of Mathematics and Applied Science, 2(1), 36–48. https://doi.org/10.35877/mathscience731

Pangaribuan, J. J., Fanny, F., Barus, O. P., & Romindo, R. (2023). Prediksi Penjualan Bisnis Rumah Properti Dengan Menggunakan Metode Autoregressive Integrated Moving Average (ARIMA). Jurnal Sistem Informasi Bisnis, 13(2), 154–161. https://doi.org/10.21456/vol13iss2pp154-161

Rachmawati, M. D., & Anifah, L. (2019). Prediksi Curah Hujan Menggunakan Metode Average Based dan High Order Fuzzy Time Series di Bandar Udara Juanda. In Journal Information Engineering and Educational Technology (Vol. 03).

Recksy, G., Pratama, S., Pratama, I., Studi, P., Informasi, S., Informasi, F. T., & Buana, U. M. (2023). Penjadwalan Masa Tanam Padi Dan Jagung Berdasarkan Hasil Prediksi Curah Hujan Menggunakan Arima Di Wilayah Sleman. 11(3), 859–868. https://doi.org/http://dx.doi.org/10.23960/jitet.v11i3%20s1.3375

Ruslana, Z. N., Tresnawati, R., Rosyidah, R., Harmoko, I. W., & Siswanto, S. (2021). Reliabilitas Prediksi Curah Hujan Dasarian Pada Kejadian Curah Hujan Ekstrim Pemicu Banjir 26 Oktober 2020 di Kebumen: Model Statistik (HyBMG) versus Model Dinamik (ECMWF). Jurnal Geosains Dan Teknologi, 4(2), 83–100. https://doi.org/10.14710/jgt.4.2.2021.83-100

Safitri, B. A., Iriany, A., & Wardhani, N. W. S. (2021). Perbandingan Akurasi Peramalan Curah Hujan dengan menggunakan ARIMA, Hybrid ARIMA-NN, dan FFNN di Kabupaten Malang. Seminar Nasional Official Statistics, 2021(1), 245–253. https://doi.org/10.34123/semnasoffstat.v2021i1.853

Saragih, S. M., & Sembiring, P. (2022). Analisis Perbandingan Metode Arima Dan Double Exponential Smoothing Dari Brown Pada Peramalan Inflasi Di Indonesia. Journal of Fundamental Mathematics and Applications (JFMA), 5(2), 176–191. https://doi.org/10.14710/jfma.v5i2.15312

Susanti, L., Hasanah, P., & Winarni, W. (2020). Peramalan Suhu Udara dan Dampaknya Terhadap Konsumsi Energi Listrik di Kalimantan Timur. BAREKENG: Jurnal Ilmu Matematika Dan Terapan, 14(3), 399–412. https://doi.org/10.30598/barekengvol14iss3pp399-412

Susanti, R., & Adji, A. R. (2020). Analisis Peramalan Ihsg Dengan Time Series Modeling Arima. Jurnal Manajemen Kewirausahaan, 17(1), 97. https://doi.org/10.33370/jmk.v17i1.393

Zahrunnisa, A., Nafalana, R. D., Rosyada, I. A., & Widodo, E. (2021). Perbandingan Metode Exponential Smoothing Dan Arima Pada Peramalan Garis Kemiskinan Provinsi Jawa Tengah. Jurnal Lebesgue : Jurnal Ilmiah Pendidikan Matematika, Matematika Dan Statistika, 2(3), 300–314. https://doi.org/10.46306/lb.v2i3.91

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How to Cite

Ruslana, Z. N. ., Prihatin, R. S. ., Sulistiyowati, S., & Nugroho, K. (2024). Application of the Arima Method to Prediction Maximum Rainfall at Central Java Climatological Station. Sinkron : Jurnal Dan Penelitian Teknik Informatika, 8(4), 2135-2141. https://doi.org/10.33395/sinkron.v8i4.13984