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All Journal Indonesian Journal of Artificial Intelligence and Data Mining Journal of Novel Engineering Science and Technology
Rambe, Khairul Hawani
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Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Environmental Science Mechanical Engineering

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Random Forest Optimization Using Recursive Feature Elimination for Stunting Classification Marpaung, Sophya Hadini; Sinaga, Frans Mikael; Rambe, Khairul Hawani; Simamora, Fandi Presly; Kelvin, Kelvin
Indonesian Journal of Artificial Intelligence and Data Mining Vol 8, No 1 (2025): March 2025
Publisher : Universitas Islam Negeri Sultan Syarif Kasim Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24014/ijaidm.v8i1.35295

Abstract

Stunting is still a major health problem in Indonesia, with a prevalence of 27% in toddlers in 2023, far from the WHO target of below 20%. RSU Mitra Medika Tanjung Mulia in Medan serves patients with various socio-economic backgrounds, which affects the quality of services, including stunting detection. Conventional methods are prone to bias and error. This study used the Random Forest algorithm and the Recursive Feature Elimination (RFE) feature selection method to improve the accuracy of stunting classification. After data preprocessing and feature selection, two main variables were identified, namely age and height. The initial Random Forest model achieved an accuracy of 94.38%, which increased to 94.42% after hyperparameter tuning. The results showed that this approach produced an accurate, efficient model that can be integrated into clinical systems, helping medical personnel identify children at risk of stunting quickly and accurately, increasing the effectiveness of interventions, and supporting government efforts to reduce the prevalence of stunting
Optimizing Rice Planting Schedules Based on Rainfall Prediction Using a BiLSTM Network Simamora, Fandi Presly; Rambe, Khairul Hawani; Marpaung, Sophya Hadini
Journal of Novel Engineering Science and Technology Vol. 5 No. 01 (2026): Journal of Novel Engineering Science and Technology
Publisher : The Indonesian Institute of Science and Technology Research

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56741/jnest.v5i01.1340

Abstract

This study addresses the critical challenge of optimizing rice planting schedules in Indonesia, where unpredictable rainfall threatens national and regional food security. To tackle this issue, a Bidirectional Long Short-Term Memory (BiLSTM) network is proposed to accurately predict rainfall patterns, with a specific focus on Deli Serdang Regency in North Sumatra. Utilizing a comprehensive weather dataset from 2013 to 2022 sourced from BMKG, a feature selection process was conducted to identify the 10 most influential features for rainfall. The BiLSTM model was then developed through several experimental scenarios, varying the data duration and architectural complexity. The best-performing model, achieved in a scenario using a double BiLSTM architecture and 10 years of data, yielded a Mean Absolute Error (MAE) of 11.2382 mm and a Root Mean Squared Error (RMSE) of 19.5650 mm. The resulting predictive capability provides a data-driven framework for optimizing planting schedules. Crucially, the study also reveals the limitations of current planting criteria, which can be misleading in regions prone to intense, short-duration rainfall, highlighting the need for more adaptive, region-specific guidelines. This work contributes to mitigating crop failure risks, enhancing crop resilience, and ensuring long-term regional food security.
Co-Authors Fandi Presly Simamora Kelvin Kelvin Sinaga, Frans Mikael Sophya Hadini Marpaung