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Trend Classification: Evidence from Leading Commodities in Indonesia  \nVirasanty Muslimah 1, *, Rezki 1, Wildan Abdul Jabar 1  \n1 Universitas Muhammadiyah Sorong, Indonesia  \n* Corresponding author: Virasanty Muslimah, Universitas Muhammadiyah Sorong, Indonesia  \n􀀍 virasanty@um-sorong.ac.id  \nCopyright: © 2026 by the authors  \nReceived: 6 November 2025 | Revised: 23 October 2025 | Accepted: 9 January 2025 | Published: 18 February 2026  \nAbstract  \nSorong City holds a strategic position in the export economy of Papua Barat Daya; however, its export performance remains volatile due to global price fluctuations, logistical constraints, and shifts in international demand. To address these challenges, this study applies machine learning-based classification to analyze and predict export trend dynamics of Sorong’s leading commodities. Specifically, the study compares the performance of Naïve Bayes and Random Forest classifiers within a quantitative experimental framework. The dataset comprises 874 export records (2023–2025), including HS Codes, export values, destination countries, exporters, and export types. The methodological workflow encompasses data preprocessing, trend labeling, normalization, label encoding, class balancing using SMOTE, and model evaluation via 80:20 train-test split and 10-fold cross-validation. Performance metrics include accuracy, precision, recall, F1-score, and ROC-AUC. Experimental results reveal that Random Forest outperforms Naïve Bayes, achieving 74% accuracy compared to 57%, and more effectively captures nonlinear feature relationships. Despite a reduction in ROC-AUC during cross-validation, Random Forest demonstrates greater robustness in export trend prediction. Overall, the findings highlight the potential of machine learning to enhance regional trade forecasting, inform evidence-based policy formulation, and strengthen data-driven export management in emerging regional economies.  \nKeyword: export; machine learning; naïve bayes; random forest; sorong city  \nTo cite this article: Muslimah, V., Rezki, R., & Jabar, W. A. (2026). Machine Learning Approaches for Export Trend Classification: Evidence from Leading Commodities in Indonesia. Edumatic:  Jurnal Pendidikan Informatika, 10(1), 11–20. [https://doi.org/10.29408/edumatic.v10i1.32949](https://doi.org/10.29408/edumatic.v10i1.32949)   \nINTRODUCTION  \nSorong City is a strategic economic hub in Southwest Papua and serves as the main gateway for international trade from eastern Indonesia due to its location on the western coast of Papua Island. Its economic strength is supported by abundant natural resources, including marine fisheries, processed wood, and plantation commodities such as nutmeg, coffee, and cocoa, which contribute significantly to regional income and national foreign exchange (Muzwardi & Nurhayati, 2025) . Despite this potential, Sorong’s export sector faces complex challenges amid intensifying global competition. Local governments and business actors are therefore required to adopt technology-driven and data-oriented strategies to remain competitive (Pratama & Hutajulu, 2022) . Moreover, global price volatility, logistical disruptions, and shifting trade policies of partner countries continue to affect regional economic stability (Harjanto et al., 2023; Sarangi et al., 2025) . Recent statistics even show short-term  \ngrowth accompanied by year-on-year decline, indicating unstable market dynamics that demand predictive and technology-based policy support (Harjanto et al., 2023) .  \nMachine learning has emerged as a promising analytical approach to address export uncertainty because of its ability to process large-scale datasets, capture complex nonlinear relationships, and generate reliable forecasts (Hathikal et al., 2020; Özden, 2022; Qi et al., 2025; Ramadhani, 2022) . Celik & Celik (2025) demonstrate that hybrid models such as LSTM, ARIMA, and GBM each have distinct advantages: LSTM excels du","cbCaij2rEQJ5D3nw","https://ap.wps.com/l/cbCaij2rEQJ5D3nw","pdf",840682,"English","# Introduction\n## Machine learning for export uncertainty\n## Prior studies on model selection and forecasting\n## Random Forest and Naïve Bayes in trade/commodity contexts","[{\"question\":\"What problem does the study address about Sorong City’s exports?\",\"answer\":\"Export performance in Sorong City is volatile due to global price changes, logistical constraints, and shifts in international demand.\"},{\"question\":\"Which machine learning models are compared in the experiments?\",\"answer\":\"The study compares Naïve Bayes and Random Forest classifiers for export trend classification and prediction.\"},{\"question\":\"How is the dataset prepared and how are models evaluated?\",\"answer\":\"The workflow includes preprocessing, trend labeling, normalization, label encoding, SMOTE class balancing, and evaluation using an 80:20 train-test split and 10-fold cross-validation with metrics such as accuracy and ROC-AUC.\"}]","Machine Learning Approaches for Export Trend Classification: Evidence from Leading Commodities in Indonesia | PDF",1789643591,25]