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All Journal Riau Journal of Computer Science JURNAL MEDIA INFORMATIKA BUDIDARMA Jurnal Mantik Penusa JMM (Jurnal Masyarakat Mandiri) JURNAL EDUCATION AND DEVELOPMENT J-SAKTI (Jurnal Sains Komputer dan Informatika) JURIKOM (Jurnal Riset Komputer) JURTEKSI Jurdimas (Jurnal Pengabdian Kepada Masyarakat) Royal EDUMATIC: Jurnal Pendidikan Informatika Building of Informatics, Technology and Science Jurnal Mantik JOURNAL OF INFORMATION SYSTEM RESEARCH (JOSH) TIN: TERAPAN INFORMATIKA NUSANTARA Jurnal Tunas Jurnal Teknik Informatika (JUTIF) Journal of Applied Data Sciences JPM: JURNAL PENGABDIAN MASYARAKAT Jutsi: Jurnal Teknologi dan Sistem Informasi Journal of Informatics Management and Information Technology Journal of Social Responsibility Projects by Higher Education Forum Jurnal Sains Teknologi dan Sistem Informasi J-SAKTI (Jurnal Sains Komputer dan Informatika) Bulletin of Information Technology (BIT) Aptekmas : Jurnal Pengabdian Kepada Masyarakat Jurnal Bangun Abdimas Jurnal Informatika dan Teknologi Informasi Jurnal Teknologi Pendidikan Jurnal Pengabdian Harapan Bangsa Jurnal Teknologi Ilmu Komputer Jurnal Teknologi Pendidikan International Journal of Informatics and Data Science
Jeperson Hutahaean
Sekolah Tinggi Manajemen Informatika Dan Komputer Royal Kisaran, Asahan
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Journal : Journal of Applied Data Sciences

 1 
Stacking Ensemble with SMOTE for Robust Agricultural Commodity Price Prediction under Imbalanced Data Siagian, Yessica; Hutahaean, Jeperson; Mulyani, Neni
Journal of Applied Data Sciences Vol 6, No 4: December 2025
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v6i4.916

Abstract

The volatility of agricultural commodity prices presents a substantial obstacle in the agribusiness sector, especially in supporting timely and data-driven decision-making. This volatility is primarily caused by the imbalanced distribution of historical price data and the complex, often nonlinear nature of price patterns. To address this challenge, this study proposes a novel predictive modeling approach by integrating Stacking Ensemble Learning and Synthetic Minority Over-sampling Technique (SMOTE). The dataset used in this research consists of 5,558 records and 9 features, sourced from a publicly available Kaggle dataset. The target variable daily price was transformed into three classes: low, medium, and high, using a quartile-based discretization approach to enable multiclass classification. The main objective is to evaluate whether stacking combined with SMOTE can improve model performance compared to baseline models that use individual algorithms. A total of eight models were constructed and compared: four baseline models using SMOTE only, and four stacking models integrating SMOTE. The experimental results demonstrate that the proposed model Decision Tree Regression with Stacking and SMOTE achieved the highest performance, with 98.68% accuracy, an F1-score of 0.9868, Cohen’s Kappa of 0.9803, MCC of 0.9803, ROC-AUC of 0.9995, and a log loss of 0.0529. Other optimized models also performed well, such as Random Forest (98.37% accuracy) and Gradient Boosting (98.56%). In contrast, baseline models such as Linear Regression and Decision Tree without stacking achieved only around 67–68% accuracy, with log loss exceeding 0.97. The key contribution of this study is the empirical evidence that combining stacking and SMOTE significantly enhances classification accuracy and model robustness in imbalanced datasets. The novelty lies in applying a deep learning-optimized stacking framework specifically for agricultural commodity price classification, along with a comprehensive multiclass evaluation, offering new insights for practical implementation in agricultural decision support systems.
Comparative Analysis of Novel Deep Reinforcement Learning Methods for Food Distribution Optimization Hutahaean, Jeperson; Siagian, Yessica; Saputra, Endra
Journal of Applied Data Sciences Vol 6, No 4: December 2025
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v6i4.956

Abstract

Uneven food distribution across various regions in Indonesia often results in supply-demand imbalances, leading to price surges, stock shortages, and overall market instability. This challenge is compounded by the limitations of conventional distribution systems, which are ill-equipped to respond to rapidly changing market dynamics. In response, this study introduces a novel, AI-driven approach by implementing Deep Reinforcement Learning (DRL) to optimize food distribution policies using real-world data. Specifically, we perform a comparative evaluation of four emerging DRL models—Double Deep Q-Network (Double DQN), Dueling DQN, Proximal Policy Optimization (PPO), and Advantage Actor-Critic (A2C)—to determine their effectiveness in learning adaptive distribution strategies from national food logistics data provided by Indonesia’s Central Bureau of Statistics (BPS). Each model was trained within a custom simulation environment based on the Markov Decision Process (MDP) framework and evaluated using five core performance metrics: cumulative reward, average reward, success rate, sample efficiency, and best reward. The results reveal that A2C consistently outperformed the other models, delivering the highest average reward and most stable training performance, while PPO demonstrated strong efficiency and success rate. These findings underscore the potential of policy-gradient methods—particularly A2C—as robust and intelligent solutions for dynamic food logistics management. This research offers one of the first comparative benchmarks of DRL methods in the food distribution domain and highlights their applicability for future integration into national AI-powered logistics systems.
Co-Authors Aandanu Aandanu Abdul Karim Ade Mayhaky Afdawiah, Rabiatul afrisawati, Afrisawati Agi Candra Bramantia Ahmad Zein Hasibuan Akmal Nasution Alpionita , Ella Anggara, Nadia Ayu Agustri Arridha Zikra Syah Aulia Kartika Aulia Kartika Aulia Khairani Nasution Aulia khairani Nasution Aulia Putri Fahdrina, Jihan Ayu Ambarwati Ayu Handayani Azmi Dwi Andira Putri, Aulia Badaruddin, Muliati Bella Putri Cahyani Ben Rahman Cecep Maulana Cecep Maulana Dandi Irwansyah dermawan, ari Desyanti Dewi Harwini Efendi Hutagalung, Jhonson Eka Saputra, Anton Endra Saputra Erlin Windia Ambarsari Eska, Juna Eva Solita Pasaribu Evi Ariyanti Purba Fahdrina, Jihan Aulia Putri Farenza, Dinda Novri Fitri Hadanyani Gogor Christmass Setyawan Guntur Maha Putra Haryansyah Sitorus Hetty Rohayani Hommy Dorthy Ellyany Sinaga Hutagalung, Juniar Indah Kurnia Irawati, Novica Irene Hasian Iwan Adhicandra Jasmir Jhonson Efendi Hutagalung Jumaryadi, Yuwan Kifti, Wan Mariatul Marpaung, Nasrun Maulana, Cecep Muh Saleh Malawat Muhammad Amin Muhazir, Ahmad Muthia Dewi Nazrul Azizi Neny Mulyani Nofri Yudi Arifin Nofriadi Nofriadi Novita Sari Novrini Hasti Nuriadi Manurung Parini, Parini Parwan Harahap Putri Fahdrina, Jihan Aulia Putri Rahmadani, Putri Rachmad Andri Atmoko Rahayu, Elly ramadhani ramadhani Ramadhani, Andrew Riski Afdhalis Syahreza Rolly Yesputra Ruktiari Ismail, Rima Saludin Saludin SANTOSO SANTOSO Saputra, Endra Sartini Sartini Siagian, Yessica Siti Nuraisyah Suci Dewi Maharani Sianipar Sitorus, Lamhot Sri Amelia, Sri Sri Rezki Maulina Azmi Sri Wahyuni Sufi Lubis, Syafaat Suparmadi S Syafira, Shella Tengku Riza Zarzani N Tia Zulaika Aulia Pane Umbari Putri, Lia Wibowo, Gentur Wahyu Nyipto William Ramdhan Wily Julitawaty Winnugroho Wiratman, Manfaluthy Hakim, Tiara Aninditha, Aru W. Sudoyo, Joedo Prihartono Yesica Siagian Yessica Siagian Yuliana, Siska Zulfi Azhar Zulika Maduri