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RICE HARVEST AREA FORECASTING USING MOVING AVERAGE METHOD FOR FOOD SECURITY PLANNING Maharani, Andika Ellena Saufika Hakim; Andriani, Helmina; Robbaniyyah, Nuzla Af'idatur; Salwa; Fatanaya, Nafika
MATHunesa: Jurnal Ilmiah Matematika Vol. 13 No. 3 (2025)
Publisher : Universitas Negeri Surabaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26740/mathunesa.v13n3.p29-36

Abstract

Ensuring food security is a key part of sustainable development in Indonesia, especially since rice remains the country's staple crop. In regions like West Nusa Tenggara (NTB) Province, where rice harvest areas can vary significantly, having accurate forecasts is essential for effective planning. This study explores historical data on rice harvest areas in NTB to forecast future trends, uncover seasonal patterns, and assess long-term changes. To do this, we apply and compare three forecasting methods: Simple Moving Average (SMA), Weighted Moving Average (WMA), and Exponential Moving Average (EMA). Their performance is evaluated using accuracy measures such as Root Mean Squared Error (RMSE) and Mean Absolute Percentage Error (MAPE), with results also presented visually to support data-driven decision-making. Among the methods tested, EMA with a 3-period window (EMA-3) produced the most accurate forecasts. This is reflected in its lower RMSE and MAPE values compared to the other methods. Based on the MAPE results, EMA-3 proves to be a reliable method for forecasting rice harvest areas in NTB.
Analysis of Land Cover Change 2015-2023 in Bima Regency Using Google Earth Engine Asmawati, Ismi; Wulandari, Ika; Istiqomah, Nisa Ul; Robbaniyyah, Nuzla Af'idatur; Ulfa, Kurnia; Alfian, Muhammad Rijal
Jurnal Sains Natural Vol. 4 No. 1 (2026): Februari
Publisher : Puslitbang Sekawan Institute Nusa Tenggara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35746/jsn.v4i1.843

Abstract

Land cultivation is one of the activities related to land conversion, this conversion is an activity to change part or all of the land function into other functions. Bima Regency is one of the areas that has experienced land cover change. The research aims to analyze land cover in Bima Regency using Landsat Centinel-2A on the GEE platform. GEE is an alternative to image processing because it allows users to access and analyze large amounts of geospatial data with high efficiency. Land area data is obtained on the platform using the NDVI method, then the accuracy test with the help of the Python programming language, the accuracy results for land cover 2015-2016, 2016-2017, 2017-2018, 2018-2019, 2019-2020, 2020-2021, 2021-2022, 2022-2023 Overall accuracy are 9.73%, 34.12%, 14.61%, 10.77%, 4.95%, 12.72%, 72.5%, 0.06% respectively. Based on the results of the study, land cover change in Bima District did not occur significantly, where there was a change in land cover below 25% in 2015 to 2023, except in 2016-2017 and 2021-2022. The low accuracy value indicates the limitations of simple NDVI-based classification methods in detecting detailed land cover changes. Therefore, the results of this study need to be understood in the context of the limitations of the method and can be used as a basis for developing more complex methods in future studies.