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Rathomi, Muhamad Radzi
Universitas Maritim Raja Ali Haji
Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Industrial & Manufacturing Engineering

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PENERAPAN METODE AVERAGE BASED LENGTH FUZZY TIME SERIES LEE UNTUK PREDIKSI KECEPATAN ANGIN DI KABUPATEN NATUNA Nifari, Dela; Nurfalinda, Nurfalinda; Rathomi, Muhamad Radzi
Faktor Exacta Vol 18, No 4 (2025)
Publisher : LPPM

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30998/faktorexacta.v18i4.27726

Abstract

Wind speed is a critical parameter that significantly affects the safety of maritime activities, particularly in Natuna Regency, a strategic region with substantial marine potential. This study aims to predict wind speed in Natuna Regency using the Average Based Length Fuzzy Time Series Lee method. The data utilized in this research consists of the average wind speed data for Natuna Regency obtained from the Meteorological Station of BMKG Ranai, covering the period from January 1, 2023, to December 31, 2023, comprising 365 data points. The interval length in the process significantly influences the prediction results. Based on testing, the model achieved a Mean Absolute Percentage Error (MAPE) of 24.42% with an accuracy rate of 78.05%, indicating that the Average Based Length Fuzzy Time Series Lee method demonstrates a reasonably good level of forecasting accuracy for predicting wind speed in Natuna Regency.
IMPLEMENTASI AUTOREGRESSIVE INTEGRATED MOVING AVERAGE (ARIMA) DALAM PREDIKSI JUMLAH PENDUDUK MISKIN KOTA TANJUNGPINANG Musliha, Musliha; Nurfalinda, Nurfalinda; Rathomi, Muhamad Radzi
Faktor Exacta Vol 18, No 4 (2025)
Publisher : LPPM

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30998/faktorexacta.v18i4.27731

Abstract

Poverty is one of the main development issues faced by developing countries, including Indonesia. In Tanjungpinang City, located in the Riau Islands Province, the poverty rate in 2022 reached 27.54 thousand people. Therefore, this study aims to predict the number of poor people in Tanjungpinang City using the Autoregressive Integrated Moving Average (ARIMA) method. The data used is monthly time series data from 2015 to 2022, totaling 96 data points. The analysis process begins with stationarity tests, differencing, and selecting the best ARIMA model. The best model identified is ARIMA (1,2,0). Evaluation results indicate that this model has a Mean Absolute Percentage Error (MAPE) of 10.6%, signifying a good level of prediction accuracy. The predicted number of poor people for January 2023 is 18.302 thousand. This research is expected to serve as a reference for the government in designing strategic policies to address poverty in Tanjungpinang City.
Co-Authors Musliha, Musliha Nifari, Dela Nurfalinda, Nurfalinda