PREDIKSI AWAL MUSIM HUJAN DI WAINGAPU PROVINSI NUSA TENGGARA TIMUR MENGGUNAKAN METODE ARIMA

Rama Dani Eka Putra, Amarta Sinardi
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Abstract

The beginning of the rainy season can be predicted by various methods such as the Autoregressive Integrated Moving Average (ARIMA). Occurrence of the onset of the rainy season (AMH), the erratic impact on various sectors, especially in the agricultural sector often results in crop failure. Therefore, the aim of this study is to improve the accuracy of predictions for the start of the rainy season. In this study daily rainfall data, the beginning of the rainy season data is obtained by calculating daily rainfall data using the Liebmann method. The best ARIMA model (3,1,0) with the equation y = 0,3162−1 + 0,1284−2−0,188−3 + 0,7434−4−0,934 is used for July, August and DMI data in August are considered as input and prediction error value ARIMA as a target. The beginning of the rainy season prediction results based on ARIMA, the results of testing and evaluation obtained values ​​of r = 0.14 and RMSE = 32.53.
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早期预测东努萨省瓦因盖普雨季的方法是阿里马方法
雨季的开始可以用各种方法来预测,如自回归综合移动平均(ARIMA)。雨季(AMH)的到来,对各个部门,特别是农业部门的影响不稳定,往往导致作物歉收。因此,本研究的目的是提高对雨季开始的预测准确性。在本研究的日降雨量数据中,雨季开始的数据是通过使用Liebmann方法计算日降雨量数据得到的。7月、8月采用方程y = 0,3162−1 + 0,1284−2−0,188−3 + 0,7434−4−0,934的最佳ARIMA模型(3,1,0),将8月的DMI数据作为输入,以ARIMA预测误差值为目标。基于ARIMA的雨季初预报结果,检验评价结果r = 0.14, RMSE = 32.53。
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RANCANG BANGUN APLIKASI BELAJAR ARAB UNTUK ANDROID MENGGUNAKAN JETPACK COMPOSE DAN KOTLIN PERANCANGAN INFRASTRUKTUR KUNCI PUBLIK DENGAN IMPLEMENTASI PEMBANGUNAN SISTEM UJIAN DARING BERBASIS WEB PERFORMANCE ANALYSIS BETWEEN INTERPRETED LANGUAGE-BASED (LARAVEL) AND COMPILED LANGUAGE-BASED (GIN) WEB FRAMEWORKS ANALISA POLA DATA PENYAKIT DI KLINIK GIGI RDC DENGAN MENERAPKAN METODE ASSOCIATION PERANCANGAN APLIKASI PERKANTORAN ELEKTRONIK DENGAN MENGGUNAKAN METODE OBJECT ORIENTED ANALYSIS DESIGN BERBASIS WEB PADA KJPP DAR
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