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All Journal AJARCDE (Asian Journal of Applied Research for Community Development and Empowerment)
Ani Dijah Rahajoe
Department of Computer Science, Universitas Pembangunan Nasional Veteran Jawa Timur, Surabaya, Indonesia,
Agriculture, Biological Sciences & Forestry Earth & Planetary Sciences Education Energy Environmental Science

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A Hybrid Stacking Ensemble Approach for Rainfall Time Series Forecasting Ani Dijah Rahajoe; Rangga Laksana Aryananda; Angelo A Beltran; Muhammad Suriansyah
AJARCDE (Asian Journal of Applied Research for Community Development and Empowerment) Vol. 10 No. 2 (2026)
Publisher : Asia Pacific Network for Sustainable Agriculture, Food and Energy (SAFE-Network)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29165/ajarcde.v10i2.1083

Abstract

Rainfall forecasting plays a crucial role in hydrology, agriculture, water resource management, and disaster mitigation. However, rainfall data typically exhibit fluctuating, seasonal, and nonlinear characteristics, which make the forecasting process quite complex. In this study, we propose a hybrid multi-model stacking ensemble to improve rainfall prediction accuracy in Kediri Regency. Our framework integrates statistical models—namely the Seasonal Autoregressive Integrated Moving Average (SARIMA) and Holt-Winters models—with machine learning and deep learning models, specifically Random Forest and Long Short-Term Memory (LSTM). We use Linear Regression as a meta-learner to combine predictions from all base models. The dataset contains monthly rainfall records from 2009 to 2022. Various preprocessing techniques are applied to the dataset, primarily normalization, lag feature construction, stationarity testing, and time-series data transformation, to enable deep learning. We use the Mean Absolute Error (MAE), Mean Squared Error (MSE), Root Mean Squared Error (RMSE), and Mean Absolute Percentage Error (MAPE) to evaluate each model's predictions. In the experiments, the ensemble stacking model outperformed the other models, with an MAE of 28.56, MSE of 1053.26, RMSE of 32.45, and MAPE of 13.05%. The results of the models used in the experiments, including the standalone SARIMA and Holt-Winters models, Random Forest, and LSTM, also showed inferior performance. Our model forecasted rainfall over the next 12 months while preserving historical seasons and data fluctuations, supporting the claim that the hybrid stacking ensemble method optimizes the accuracy, stability, and robustness of rainfall prediction for complex time-series data. Contribution to Sustainable Development Goals (SDGs):SDG 2 – Zero HungerSDG 6 – Clean Water and SanitationSDG 13: Climate Action
Oil Palm Crown Detection and Tree Counting Using Roboflow Detection Transformer on UAV Imagery Ani Dijah Rahajoe; Denisa Septalian Alhamda; Angelo A Beltran Jr; Muhammad Suriansyah
AJARCDE (Asian Journal of Applied Research for Community Development and Empowerment) Vol. 10 No. 2 (2026)
Publisher : Asia Pacific Network for Sustainable Agriculture, Food and Energy (SAFE-Network)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29165/ajarcde.v10i2.1151

Abstract

Oil palm plantations require accurate and timely inventory data to support plantation management, productivity assessment, and sustainable agricultural practices. However, manual tree inventory in large plantation areas is labor-intensive, time-consuming, and prone to human error. This study proposes an automated approach for oil palm crown detection and tree counting using high-resolution Unmanned Aerial Vehicle (UAV) imagery. To improve image quality, Contrast Limited Adaptive Histogram Equalization (CLAHE) was applied as a preprocessing step, and the Roboflow Detection Transformer (RF-DETR) was used as the object detection model. The proposed method was evaluated using 1,135 UAV images containing 56,547 annotated oil palm crowns collected from commercial plantations in West Kalimantan, Indonesia. Experimental results demonstrate that the proposed approach achieved mAP@50 of 97.5%, precision of 97.1%, recall of 96.0%, and F1-score of 96.6% for oil palm crown detection. In the tree-counting evaluation, the system successfully detected 1,105 of 1,120 ground-truth trees, achieving an overall accuracy of 98.5%. Furthermore, the proposed method achieved an average detection time of 16.1 ms, indicating high computational efficiency. These results demonstrate that the proposed framework provides an effective and practical solution for automated oil palm inventory and plantation monitoring using high-resolution UAV imagery. Contribution to Sustainable Development Goals (SDGs): SDG 2: Zero Hunger SDG 9: Industry, Innovation and Infrastructure SDG 12: Responsible Consumption and Production SDG 15: Life on Land
Co-Authors Angelo A Beltran Angelo A Beltran Jr Denisa Septalian Alhamda Muhammad Suriansyah Rangga Laksana Aryananda