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All Journal Jurnal Publikasi Sistem Informasi dan Manajemen Bisnis Prismatika: Jurnal Pendidikan dan Riset Matematika
Juniasti Gulo
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Computer Science & IT Decision Sciences, Operations Research & Management

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PREDIKSI HARGA TANDAN BUAH SEGAR KELAPA SAWIT MENGGUNAKAN MODEL GATED RECURRENT UNIT DI PROVINSI JAMBI Juniasti Gulo; Yurinanda, Sherli; Sormin, Corry
Prismatika: Jurnal Pendidikan dan Riset Matematika Vol. 8 No. 1 (2025): Prismatika: Jurnal Pendidikan dan Riset Matematika
Publisher : Universitas Insan Budi Utomo

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33503/prismatika.v8i1.2138

Abstract

Oil palm is a leading commodity that plays a strategic role in Indonesia’s agricultural sector. Jambi Province is one of the main producers of fresh fruit bunches (FFB) is the main harvest product of oil palm. Although production levels are high and global demand continues to increase, FFB prices tend to fluctuate, creating income uncertainty for both farmers and industry players. These fluctuations are influenced by various factors and require a modeling approach capable of capturing historical patterns in time series data that are nonlinear and volatile. This study aims to apply the Gated Recurrent Unit (GRU) model to predict oil palm FFB prices in Jambi Province. GRU is selected because it is effective in processing sequential data and retaining long-term information through its update gate and reset gate mechanisms. The data used consist of daily FFB prices over the past three years. The research process includes preprocessing, normalization using the min–max scaler, data splitting (80% training and 20% testing), model training with various hyperparameter combinations, and evaluation using Mean Squared Error (MSE) and Mean Absolute Percentage Error (MAPE). The results show that the GRU model successfully captures the historical patterns of FFB prices. The best configuration was obtained with a learning rate of 0.01, hidden size of 128, batch size of 16, window size of 5, and 100 epochs. The model achieved an MSE of 0.0418 on the training data and a MAPE of 12.82% for the 50-day forecast. These values indicate a good level of accuracy, suggesting that the GRU model is suitable as a data-driven decision support tool to enhance price stability and assist plantation business planning in Jambi Province.
Identifikasi Faktor Penentu Kemiskinan di Indonesia Melalui Pendekatan Regresi Linier Berganda Juniasti Gulo
Jurnal Publikasi Sistem Informasi dan Manajemen Bisnis Vol. 4 No. 2 (2025): Jurnal Publikasi Sistem Informasi dan Manajemen Bisnis
Publisher : Pusat Riset dan Inovasi Nasional

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55606/jupsim.v4i2.4157

Abstract

Poverty in Indonesia remains a significant challenge despite various mitigation programs. This condition is influenced by the complex interaction of social and economic factors, including regional disparities and limited access to basic services. This study aims to identify and analyze the influence of six main factors on poverty levels, namely unemployment rate, average years of schooling, access to clean water, access to healthcare services, per capita GRDP, and population density, using multiple linear regression. The analysis results show that unemployment rate, access to clean water, and healthcare services significantly affect poverty, where higher unemployment and limited access to these services tend to increase poverty levels. Meanwhile, average years of schooling, per capita GRDP, and population density have no significant partial effect. These findings emphasize the importance of policy focus on reducing unemployment and improving access to basic services to effectively lower poverty rates. This study provides an empirical basis for formulating more targeted development strategies to address poverty in Indonesia.
Co-Authors Sormin, Corry Yurinanda, Sherli