Optimizing Agricultural Commodity Price Forecasts Using an Ensemble Stacking Method Based on Market and Product Characteristics

Abdul Karim, Kusmanto Kusmanto — Mon, 31 Aug 2026 00:00:00 +0700

Commodity price prediction is a vital aspect of supporting decision-making because price fluctuations can create uncertainty for businesses and stakeholders. This study aims to compare the performance of several machine learning algorithms for commodity price prediction, namely Random Forest, XGBoost, Support Vector Regression (SVR), Gradient Boosting, and Stacking Ensemble. Model performance was evaluated using Mean Absolute Error (MAE), Root Mean Square Error (RMSE), Mean Absolute Percentage Error (MAPE), and the coefficient of determination (R²). The experimental results show that Gradient Boosting achieved the best overall performance, with an RMSE of 219.07 and an R² of 0.9968, while Random Forest had the lowest MAE of 65.98. The Stacking Ensemble also demonstrated competitive performance, achieving an RMSE of 253.07 and an R² of 0.9958. In contrast, SVR produced the lowest performance, with an RMSE of 1,919.65 and an R² of 0.7555. Based on these results, Gradient Boosting was selected as the most optimal model because it provided the lowest prediction error and the highest ability to explain data variation. These findings demonstrate that selecting an appropriate machine learning algorithm plays a crucial role in improving commodity price prediction performance.

Penerapan Algoritma Apriori pada Asosiasi Generasi Z dan Domisili Terhadap Partisipasi Pemilih Muda pada Pemilu dan Pilkada 2024

Mon, 31 Aug 2026 00:00:00 +0700

The 2024 General Election and Regional Election recorded a significant phenomenon in which young voters from Generation Z and millennials dominated the national permanent voter list, accounting for 56.45% of the total electorate. However, this high proportion was not necessarily followed by optimal participation rates, particularly at the regional level such as in Binjai City. This study aims to analyze the participation patterns of Generation Z in the 2024 General Election and Regional Election in Binjai City using the Apriori algorithm within the CRISP-DM framework. The dataset was obtained from the Binjai City KPU recapitulation, consisting of 1,201 Gen Z voter records with four attributes: voter age category, sub-district, general election attendance status, and regional election attendance status. The modeling process was applied with a minimum support of 0.04 and a minimum confidence of 0.5, generating 19 association rules with all lift values exceeding 1. The main findings indicate that Middle Gen Z (aged 20–23 years) is the most dominant group in attending the General Election, Binjai Barat Sub-district recorded the highest participation rate, and a selective participation pattern was identified in which non-attendance at the Regional Election does not necessarily reflect overall political apathy. The findings of this study are expected to serve as recommendations for the Binjai City KPU in designing more targeted strategies to improve young voter participation.

Journal Global Technology Computer

Optimizing Agricultural Commodity Price Forecasts Using an Ensemble Stacking Method Based on Market and Product Characteristics

Penerapan Algoritma Apriori pada Asosiasi Generasi Z dan Domisili Terhadap Partisipasi Pemilih Muda pada Pemilu dan Pilkada 2024