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All Journal JURNAL ILMIAH MATEMATIKA DAN TERAPAN MATHunesa: Jurnal Ilmiah Matematika Jurnal GAMMA-PI
Ulya Nabilla
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Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Environmental Science Mathematics Public Health

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Analisis Faktor-Faktor yang Mempengaruhi Volume Produksi Padi Provinsi Aceh Nurviana; Nirmala Sari; Ulya Nabilla
JURNAL ILMIAH MATEMATIKA DAN TERAPAN Vol. 20 No. 2 (2023)
Publisher : Program Studi Matematika, Universitas Tadulako

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22487/2540766X.2023.v20.i2.16609

Abstract

The agricultural sector has a very significant contribution to the achievement of the Sustainable Development Goals (SDG's) and is also the second largest contributor to Gross Domestic Product (GDP). Aceh's agricultural sector excels in various commodities such as rice, corn, soybeans, and chili. One of the main commodities in Aceh's agricultural sector is rice. Rice is a staple food commodity for the Indonesian people whose needs continue to increase along with the increase in population and also as a source of income for the farming community in meeting their needs. The volume of rice production from year to year fluctuates greatly, experiencing a decrease or increase. the realization of the rice harvest from January to December 2022 amounted to 271.75 thousand hectares, or decreased by around 25.31 thousand hectares (8.52 percent) compared to 2021 which reached 297.06 thousand hectares. The purpose of this study is to analyze the factors that affect the volume of rice production in Aceh Province. The results of the study obtained a regression model Y = 556505,224 + 3,081 X1-1376,14 X2 + 139,57 X3 + 92,797 X4-1,019 X5. The results of the simultaneous test show that the harvest area, rainfall, irrigation area, seed use, and labor together or simultaneously have a significant effect on rice production at a significance level of 0.05. Partially, the variable that has a positive and significant effect on rice production is the variable of harvest area. In addition, the variable area of irrigation and the use of seeds also have a positive effect on rice production but not significant. Meanwhile, the variables of average rainfall and labor have a negative effect on rice paddy production.
PERBANDINGAN MODEL MALTHUS DAN POPULASI LOGISTIK PADA JUMLAH EKSPOR MINYAK KELAPA SAWIT DI PT PELINDO MULTI TERMINAL BRANCH BELAWAN Tiara, Mutiara; Ulya Nabilla
JURNAL GAMMA-PI Vol 6 No 2 (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.v6i2.10085

Abstract

Indonesia adalah salah satu negara penghasil minyak kelapa sawit. Dengan meningkatnya permintaan pasar terhadap minyak kelapa sawit tentu harus diimbangi dengan produksi dalam negeri. PT Pelindo Multi Terminal merupakan salah satu Badan Usaha Milik Negara Indonesia yang memiliki komoditas ekspor unggulan yaitu minyak kelapa sawit, bungkil sawit, dan karet. Penelitian ini merupakan penelitian yang bertujuan untuk mengetahui perbandingan hasil perkiraan jumlah ekspor minyak kelapa sawit di PT Pelindo Multi Terminal Branch Belawan tahun 2019-2023 dengan menggunakan model Malthus dan Populasi Logistik. Data yang digunakan adalah data sekunder yang diperoleh dari PT Pelindo Multi Terminal Branch Belawan dari tahun 2019-2023. Data Jumlah ekspor minyak kelapa sawit di PT Pelindo Multi terminal mengalami kenaikan setiap tahunnya. Berdasarkan hasil perkiraan dengan model Malthus terdapat MAPE 5,46% dan model Populasi Logistik memiliki MAPE 2,81%. Hal ini menunjukkan bahwa model Populasi Logistik merupakan model terbaik yang dapat digunakan dalam memperkirakan jumlah ekspor minyak kelapa sawit di PT Pelindo Multi Terminal Branch Belawan dikarenakan memiliki total MAPE yang lebih rendah. Pada tahun 2024 diperoleh hasil peramalan jumlah ekspor minyak kelapa sawit di PT Pelindo Multi Terminal Branch Belawan Menggunakan model Populasi Logistik sebesar 282.850 ton.
Peramalan Jumlah Pendaftaran Tanah Menggunakan Metode Exponential Smoothing Pada Kantor Pertanahan Kota Langsa Nurhadija; Sari, Riezky Purnama; Ulya Nabilla; Nurviana; Fairus
JURNAL GAMMA-PI Vol 7 No 2 (2025): 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.v7i2.13370

Abstract

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 Baringbing, Meylani Fairus Nirmala Sari Nurhadija Nurviana Riezky Purnama Sari Sari, Riezky Purnama Tiara, Mutiara