Article Per Year (5 Year)
p-Index From 2021 - 2026
0.23
P-Index
This Author published in this journals
All Journal The IJICS (International Journal of Informatics and Computer Science)
Rangga Pohan
STMIK Pelita Nusantara
Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering

Published : 1 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 1 Documents
Search

 1 
Random Forest Model for Food Security Index Prediction Using Climate and Socioeconomic Panel Data in North Sumatra Wira Apriani; Rizki Fadila Nasution; Rangga Pohan; Tyo Rizki Ramadhan
The IJICS (International Journal of Informatics and Computer Science) Vol. 10 No. 2 (2026): July
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/ijics.v10i2.9998

Abstract

This study develops and evaluates a Random Forest Regression model for predicting the Food Security Index (IKP) using climate and socioeconomic panel data from 33 regencies/cities in North Sumatra, with Deli Serdang Regency as the principal case discussed. The predictors comprise poverty rate, gross regional domestic product (GRDP) per capita, annual rainfall, and annual rainy days. The common modeling period was 2020–2021. Of 66 potential regency-year observations, eight lacked IKP values in 2021, leaving 58 complete observations. To account for the panel structure, model performance was evaluated using nested group-aware cross-validation, with regency/city as the grouping variable. Across five outer folds, Random Forest achieved RMSE 8.450 ± 2.703, MAE 6.289 ± 2.018, and R² −0.501 ± 1.500. Linear regression obtained RMSE 8.282 ± 2.431, MAE 6.401 ± 2.262, and R² −0.363 ± 1.192, while a single decision tree obtained RMSE 9.821 ± 3.482, MAE 8.030 ± 2.849, and R² −0.972 ± 1.871. Thus, no model demonstrated strong or stable out-of-group generalization. Repeated group-aware permutation analysis identified poverty rate as the most consistent predictor. The results are predictive and associational, not causal, and the present model should be regarded as an exploratory proof of concept requiring larger, spatially resolved data for robust regional forecasting.
Co-Authors Rizki Fadila Nasution Tyo Rizki Ramadhan Wira Apriani