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All Journal Jurnal Pamator : Jurnal Ilmiah Universitas Trunojoyo Madura JOURNAL OF APPLIED INFORMATICS AND COMPUTING
Alfan Rizaldy Pratama
Universitas Pembangunan Nasional "Veteran" Jawa Timur
Humanities Computer Science & IT Economics, Econometrics & Finance Social Sciences

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Improving Palm Oil Production Efficiency through Deep Learning Algorithms for Fruit Ripeness Detection in Digital Images Tsabita Rosyidah Putri; I Gede Susrama Mas Diyasa; Alfan Rizaldy Pratama
Jurnal Pamator : Jurnal Ilmiah Universitas Trunojoyo Vol 19, No 2: May - August 2026
Publisher : Universitas Trunodjoyo Madura

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21107/pamator.v19i2.33569

Abstract

Oil palm is a strategic commodity in Indonesia, and its production quality is greatly influenced by the ripeness of the fruit at harvest. Manual ripeness determination is still subjective and prone to errors due to variations in worker experience and environmental conditions. Advances in computer vision and deep learning technology offer a more objective and consistent automated solution. This study aims to develop and evaluate a model for detecting the ripeness level of palm oil fruit using the YOLOv12m algorithm based on digital images. The dataset used consists of 3,375 images with three ripeness classes (unripe, semi-ripe, ripe), which are divided into training, validation, and testing data with a ratio of 70:20:10. The model was trained for a maximum of 25 epochs with an early stopping mechanism. The evaluation was conducted using precision, recall, mAP@50, and mAP@50–95 metrics. The results showed excellent performance with precision of 0.958, recall of 0.946, mAP@50 of 0.985, and mAP@50–95 of 0.882. Class-by-class analysis shows the best performance in the raw and ripe classes, while the unripe class still poses challenges due to visual similarities between transition phases. Overall, the YOLOv12m model has proven to be effective and has the potential to be applied as a more objective and efficient harvest decision support system.
Implementation of a Hybrid TabNet–XGBoost Model Based on Radiosonde Data for Predicting Daily Rainfall Intensity in Surabaya Annabel Gracia Puryani; Aviolla Terza Damaliana; Alfan Rizaldy Pratama
Journal of Applied Informatics and Computing Vol. 10 No. 4 (2026): August 2026
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v10i4.13767

Abstract

Rainfall prediction plays an important role in supporting hydrometeorological disaster mitigation and weather-related decision-making. However, accurate rainfall prediction remains challenging because atmospheric processes are highly nonlinear and governed by complex interactions among multiple meteorological variables. This study proposes a Hybrid TabNet–XGBoost model for daily rainfall prediction using integrated radiosonde and surface meteorological observations collected at the BMKG Juanda Class I Meteorological Station. The dataset covers the period from 2019 to 2025 and consists of 2,551 daily observations. TabNet was employed to select the fifteen most informative atmospheric variables based on feature importance, while temporal dependencies were incorporated through lag features generated using Autocorrelation Function (ACF) and Partial Autocorrelation Function (PACF) analyses. Hyperparameter optimization was performed using Optuna with TimeSeriesSplit cross-validation prior to model training. Experimental results on the testing dataset achieved an RMSE of 19.1347 mm, an MAE of 11.9742 mm, a MAPE of 17.62%, and an R² of 0.0449. The proposed model was able to capture the general temporal pattern of daily rainfall and produced satisfactory predictions under the dominant rainfall conditions represented in the dataset. However, the model exhibited reduced sensitivity to high-intensity rainfall events, resulting in the underestimation of extreme rainfall and a relatively low R² value, primarily due to the imbalanced rainfall distribution and the complexity of rainfall processes. The optimized model was subsequently applied to generate daily rainfall projections for 2026 based on historical atmospheric observations. Since the corresponding observational data were unavailable at the time of this study, these projections should be interpreted as model-based forecasts rather than validated prediction results. Overall, the proposed Hybrid TabNet–XGBoost framework demonstrates the potential of integrating radiosonde and surface meteorological observations for daily rainfall prediction while highlighting the need for additional atmospheric and spatial information to improve the prediction of extreme rainfall events.
Co-Authors Annabel Gracia Puryani Aviolla Terza Damaliana I Gede Susrama Mas Diyasa Tsabita Rosyidah Putri