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All Journal MATHunesa: Jurnal Ilmiah Matematika Jurnal GAMMA-PI
Baringbing, Meylani
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Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Environmental Science Mathematics Public Health

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PERAMALAN JUMLAH PRODUKSI PADI DI PROVINSI SUMATERA BARAT MENGGUNAKAN METODE ARIMA Baringbing, Meylani; Natasyah, Nabila; saumi, Fazrina
JURNAL GAMMA-PI Vol 6 No 1 (2024): Jurnal Gamma-Pi (Matematika dan Pendidikan Matematika)
Publisher : Program Studi Matematika, Fakultas Teknik, Universitas Samudra. Langsa, Aceh.

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33059/gamma-pi.v6i1.9306

Abstract

Rice farming has a major role in the economy and food security, including in Langsa City. The availability and stability of rice production plays an important role in meeting people's food needs. Therefore, forecasting rice production results is crucial in developing resource management strategies, budget allocations and more focused agricultural policies.The main objective of this research is to provide a solid foundation for strategic planning and decision making in the rice farming sector in West Sumatra. By understanding trends and growth patterns in rice production, it is hoped that this research will make a positive contribution to increasing agricultural productivity, farmer welfare, and food security at the local and regional levels.national. The data research method used in this research is annual data on the amount of rice production in West Sumatra Province (in tons). Data on the amount of rice production was obtained from the Central Statistics Agency (BPS). This study focuses on food availability and the agricultural sector in West Sumatra Province by utilizing rice production data from 2018 to 2022. The method applied in this research is the ARIMA method. The following are the steps taken in forecasting using the ARIMA method using Minitab software. The ARIMA(111) model is the best ARIMA model with the smallest MAPE value of 1% for forecasting rice production in West Sumatra Province. Forecasting rice production in West Sumatra Province in 2021 to 2023 is 272,931.95 tons, 271,883.11 respectively. tonnes and 19,554.74 tonnes. The forecast results are lower than the amount of rice production from the previous year, namely 2021 and 2022. Apart from that, from the forecast results it can be seen that rice production in 2023 is lower than in 2022, namely 19,554.74 tons
PENERAPAN BACKPROPAGATION NEURAL NETWORK DALAM MERAMALKAN PRODUKSI KOPI DI INDONESIA Riezky Purnama Sari; Ulya Nabilla; baringbing, meylani
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.p494-501

Abstract

Coffee is one of the most valuable agricultural commodities in the global market, ranking 4th among the ten largest coffee-producing countries in the world. In addition, coffee has the potential to drive the country's economic growth through exports, which can contribute to an increase in national foreign exchange. During the ten-year period from 2014 to 2023, the growth of coffee production was recorded to be lower, with an average increase of about 1.63% per year. The purpose of this research is to determine the forecast of coffee production in Indonesia from 2025 to 2029 using the Backpropagation Neural Network and the accuracy of the method in forecasting coffee production in Indonesia. Data was taken from the Secretariat of the Directorate General of Estates. The method used in this research is the backpropagation neural network method using 4 models of training and testing data, namely 50:50, 60:40, 70:30, and 80:20. Backpropagation Neural Network is a multilayer artificial neural network method that operates in a supervised manner and can be used for classification and forecasting. The results of this study show that the 80:20 model is the best model because the MAPE obtained is 7.672%, with the coffee production forecast in Indonesia for the years 2025 to 2029 being 698,979; 697,202; 696,081; 695,292; 694,700 (tons).With an accuracy level of 7.672%. This value indicates that this method is very good at forecasting coffee production in Indonesian. Keywords: Coffee, Backpropagation Neural Network, MAPE, Training-Test Data
Co-Authors Natasyah, Nabila Riezky Purnama Sari Saumi, Fazrina Ulya Nabilla