BRIEF RESEARCH REPORT article
Front. Agron.
Sec. Agroecological Cropping Systems
Modeling Rice Productivity Clustering with Random Forest: Implications of Regency Agribusiness in 1986–2023
Provisionally accepted
Ali Asgar1
Andjar Prasetyo2*Henky Henanto1Edi Priyo Pramono1Nenie Yustiningsih1Gigih Atmaji1Himawan Adinegoro1Primawati Yenni Fauziah1Darkam Musaddad1Suwarni Tri Rahayu1Raden Djoko Goenawan1Suharto Ngudiwaluyo1Subandrio Subandrio1Wahyu Purwanto1Amos Lukas1- 1Badan Riset dan Inovasi Nasional Republik Indonesia, Central Jakarta, Indonesia
- 2Bappeda Kota Magelang, Central Java, Indonesia, Magelang, Indonesia
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Rice productivity in Indonesia is strongly influenced by variations in environmental conditions, land management, and resource availability, creating disparities between high-and low-productivity areas. This study aims to segment regions based on rice productivity using data-driven clustering analysis to identify key patterns and influencing factors. A descriptive quantitative design was employed, applying Random Forest Clustering to annual rice productivity data (1986–2023) from 29 districts in Central Java, Indonesia, sourced from the Ministry of Agriculture. Data preprocessing, clustering, and visualization were conducted using JASP software. Model optimization used the Bayesian Information Criterion (BIC), and performance was evaluated via the Silhouette Score, Dunn Index, and Calinski-Harabasz Index. Three clusters emerged: high (mean = 6.8 quintals/ha), medium (4.5 quintals/ha), and low (2.9 quintals/ha). The model showed a Dunn Index of 0.396 and Calinski-Harabasz Index of 10.088, with Silhouette Scores ranging from 0.143 to 0.207, indicating moderate cluster separation. Results reveal a strong association between rice productivity and land management, environmental conditions, and agricultural inputs. This data-driven approach enables targeted interventions and supports evidence-based agribusiness strategies to optimize rice production in Indonesia.
Keywords: agribusiness, Central Java, Random forest clustering, Regional productivity, Rice Clustering, Rice productivity, spatial analysis
Received: 01 Oct 2025; Accepted: 27 Nov 2025.
Copyright: © 2025 Asgar, Prasetyo, Henanto, Pramono, Yustiningsih, Atmaji, Adinegoro, Fauziah, Musaddad, Rahayu, Goenawan, Ngudiwaluyo, Subandrio, Purwanto and Lukas. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
* Correspondence: Andjar Prasetyo
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