Implementation of Ensemble Classification in Horticultural Crops at Indonesia

DOI

10.2991/978-94-6463-926-1_54How to use a DOI?

Keywords

Classification; Ensemble; Holticultural; Machine Learning; Multi-Commodity

Abstract

Amid rising global demand for fresh produce and the central role of horticulture in Indonesia’s food security, this study develops a data-driven, machine-learning framework for multi-commodity classification to support evidence-based agricultural policy. We compile a national-scale panel (2009–2024) from the Ministry of Agriculture covering 108 horticultural commodities with annual harvested area, production, and productivity. The workflow includes data cleaning, normalization, categorical encoding, and a temporally faithful splittraining on 2009–2022 and testing on 2023–2024 to emulate real-world generalization. Five classifiers are compared Support Vector Machine (SVM), Random Forest (RF), eXtreme Gradient Boosting (XGBoost), AdaBoost, and Decision Tree (DT) using accuracy and macro F1-score to capture performance across majority–minority classes. XGBoost attains the best results (98.15% accuracy), followed by RF (97.22%), while SVM performs lowest (61.11%), confirming the advantage of ensemble methods (bagging/boosting) over single learners for multiclass and imbalanced agricultural data. Practically, the proposed model enables automated commodity mapping, anomaly detection in production records, and more precise, adaptive food-distribution policies aligned with regional conditions. We set a success criterion of ≥ 70% overall accuracy with macro F1 as the primary evaluation metric. Findings indicate that a well-designed ensemble pipeline can deliver high technical performance and tangible decision support for sustainable management of Indonesia’s horticultural sector.

Copyright

© 2025 The Author(s)

Open Access

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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TY  - CONF
AU  - Henny Yulianti
AU  - Mochamad Agung Wibowo
AU  - Budi Warsito
PY  - 2025
DA  - 2025/12/31
TI  - Implementation of Ensemble Classification in Horticultural Crops at Indonesia
BT  - Proceedings of the International Conference on Applied Science and Technology on Engineering Science 2025 (iCAST-ES 2025)
PB  - Atlantis Press
SP  - 480
EP  - 489
SN  - 2352-5401
UR  - https://doi.org/10.2991/978-94-6463-926-1_54
DO  - 10.2991/978-94-6463-926-1_54
ID  - Yulianti2025
ER  -