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All Journal IJCCS (Indonesian Journal of Computing and Cybernetics Systems) ILKOM Jurnal Ilmiah
Hamda, Hizbullah
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Computer Science & IT Control & Systems Engineering

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Prediction and Analysis of Rice Production and Yields Using Ensemble Learning Techniques Sulistya, Yudha Islami; Musdholifah, Aina; Sapuletea, Chrissandy; Br Bangun, Elsi Titasari; Hamda, Hizbullah; Anjani, Sarah; Septiadi, Abednego Dwi
ILKOM Jurnal Ilmiah Vol 16, No 2 (2024)
Publisher : Prodi Teknik Informatika FIK Universitas Muslim Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33096/ilkom.v16i2.1948.115-124

Abstract

This research focuses on predicting and analyzing rice production and yield throughout the world using ensemble learning techniques. The study applies and compares three methods: linear regression, ARIMA, and ensemble learning, to predict rice harvest yields. The results show that ensemble learning techniques significantly improve prediction performance. For instance, the ensemble model for predicting area harvested, combining Model 6 (linear regression) and Model 10 (ARIMA), achieved  of coefficient of determination outperforming the individual models. Similarly, for predicting yield, the ensemble model combining Model 4 (linear regression) and Model 9 (ARIMA) achieved  of coefficient of determination indicating superior prediction accuracy. For predicting production, the ensemble model combining Model 2 (linear regression) and Model 8 (ARIMA) achieved  of coefficient of determination. These results demonstrate the effectiveness of ensemble learning in enhancing prediction accuracy with lower MSE and RMSE values. By analyzing various factors influencing rice yields, this research provides valuable insights for increasing rice production and yield, supporting efforts to improve the efficiency and effectiveness of rice farming, and contributing to achieving the United Nations Sustainable Development Goals (SDGs).
Enhancing Image Classification Performance Using Multi CNN Feature Fusion Method Hamda, Hizbullah; Wibowo, Moh Edi
IJCCS (Indonesian Journal of Computing and Cybernetics Systems) Vol 19, No 3 (2025): July
Publisher : IndoCEISS in colaboration with Universitas Gadjah Mada, Indonesia.

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22146/ijccs.98531

Abstract

This research aims to overcome general challenges in the field of image pattern recognition using a convolutional neural network (CNN), which is still faced with the complexity and limitations of image data. Achieving high accuracy is essential because it significantly influences the effectiveness and success of numerous areas. Although deep learning technology, especially CNNs, offers the potential to improve accuracy, it is still limited to the 70–80% range for achieving the expected level of accuracy. In this research, a fusion method was developed that combines pre-trained models using concatenation techniques to increase accuracy. By utilizing pre-trained models such as ResNet50, VGG16, and MobileNet-v2, which were then adapted to various datasets and cross-validation techniques, researchers managed to achieve significant improvements in accuracy. The results of this study show an improvement in the accuracy of the Fusion Multi-CNN model for various datasets. On the fashion dataset, MNIST managed to achieve an accuracy of 0.87840, while on CIFAR-10 and Oxford-102, the accuracy was 0.81260 and 0.84004, respectively.
Co-Authors Abednego Dwi Septiadi Aina Musdholifah Anjani, Sarah Br Bangun, Elsi Titasari Sapuletea, Chrissandy Wibowo, Moh Edi Yudha Islami Sulistya