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All Journal International Journal of Informatics and Computation International Journal of Informatics Engineering and Computing
Rike Pradila
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Computer Science & IT

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Implementation of Deep Learning for Classification of Mushroom Using CNN Algorithm Imam Mahfudz I'tisyam; Nurhadi Wijaya; Rike Pradila
International Journal of Informatics and Computation Vol. 5 No. 1 (2023): International Journal of Informatics and Computation
Publisher : University of Respati Yogyakarta, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35842/ijicom.v5i1.42

Abstract

Mushrooms are a type of low-level plant that lacks chlorophyll. One of the advantages of fungi is that they are commonly utilized as food items in the community. This paper discussed the implementation of CNN for the classification of mushrooms. The project aims to develop a robust system that can automate the labor-intensive task of mushroom classification. The CNN model will be trained on a large dataset of annotated mushroom images, learning to extract meaningful features and patterns for accurate categorization. To evaluate the performance of the developed system, a comprehensive set of metrics, including accuracy, precision, recall, and F1 score, will be used. The dataset will be split into training, validation, and testing sets to assess the model's generalization ability to unseen data. Based on the experimental result, the average accuracy rate in the Agaricus Portobello test was % -99.89 %, % -99.89 % in the Amanita Phalloides test, % -99.59 % in the Cantharellus Cibarius test, % -98.89 % in the Gyromitra Esculenta test, % -99.96 % in the Hygrocybe Conica, and % -99.93 % in the Omphalotus Orealius.
Effective Seashell Image Classification Using CNN Algorithm Pradila, Rike; Aprillia Sahuburua, Yuliana
International Journal of Informatics Engineering and Computing Vol. 1 No. 2 (2024): International Journal of Informatics Engineering and Computing
Publisher : ASTEEC

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70687/ijimatic.v1.i2.44

Abstract

Seashell classification presents significant challenges in image processing, particularly in distinguishing between blood shells (Anadara granosa) and feather mussels (Anadara antiquata). This study leverages deep learning and computer vision techniques to develop a classification model for seashell images using Convolutional Neural Networks (CNN). Additionally, we propose the RunCNN method to compare its performance with CNN. The research involves collecting a large dataset of blood shells and feather mussels, preprocessing the data, training the models, and evaluating their performance. Experimental results demonstrate that the CNN-based model achieves 87% accuracy, while the RunCNN method achieves 82% accuracy. Both models exhibit low loss, indicating their effectiveness in classifying seashell images. These findings highlight the potential of deep learning approaches for accurate and efficient seashell classification, with CNN outperforming RunCNN in this context.
Forest Fire Detection Model Using Dense Net Architecture Rike Pradila; Akhyar Bintang
International Journal of Informatics Engineering and Computing Vol. 2 No. 1 (2025): International Journal of Informatics Engineering and Computing
Publisher : ASTEEC

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70687/ijimatic.v2i1.93

Abstract

Forest and land fires in Indonesia are frequent events and cause significant losses in the health, ecological and social sectors. Human and natural factors play a role in triggering these fires. However, handling forest and land fires still faces obstacles in accurately predicting the location of hot spots, making optimal control difficult. Therefore, it is necessary to develop an intelligent system to detect forest and land fires more effectively. This research aims to create a model that is capable of detecting forest and land fires using a transfer learning approach, utilizing the DenseNet201 architecture to increase detection accuracy. The dataset used in this research comes from the Fire Forest Dataset on the Kaggle site. The feature extraction process was carried out using the DenseNet201 architecture, and the resulting model was tested using the confusion matrix method to classify images into two classes, namely fire and non-fire classes. Through training using the DenseNet201 architecture, an effective model was obtained in detecting forest and land fires. Test results using 380 test data show an accuracy level of 99% in recognizing images of forest and land fires. It is hoped that this research can provide a basis for the development of smart systems that are more sophisticated and effective in overcoming the problem of forest and land fires, as well as protecting the environment and public health in Indonesia.
Comparison of Hybrid CNN-LSTM Models for Stock Price Prediction Rike Pradila; Rafitajudin, Rafitajudin
International Journal of Informatics Engineering and Computing Vol. 2 No. 2 (2025): International Journal of Informatics Engineering and Computing
Publisher : ASTEEC

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70687/sdts5v08

Abstract

This study explores the application of deep learning techniques for stock price prediction by comparing Convolutional Neural Network (CNN), Long Short-Term Memory (LSTM), and hybrid CNN–LSTM architectures. We propose a hybrid deep learning model that integrates convolutional layers for local feature extraction with LSTM layers for capturing long-term temporal dependencies in financial time-series data. Historical stock price data of INDF.JK obtained from Yahoo Finance were used to train and evaluate the models. The dataset was preprocessed and transformed into sequential input using a sliding window approach to enable effective time-series learning. Model performance was evaluated using several regression metrics, including Root Mean Squared Error (RMSE), Mean Absolute Error (MAE), Mean Absolute Percentage Error (MAPE), and the coefficient of determination (R²). Experimental results demonstrate that the proposed hybrid CNN–LSTM model achieves superior prediction performance compared with standalone CNN and LSTM models. The hybrid model records an RMSE of 87.77, MAE of 63.97, and MAPE of 1.02%, while achieving the highest R² score of 0.9759. In comparison, the CNN model produces an RMSE of 96.18 and an R² score of 0.9711, whereas the LSTM model achieves an RMSE of 89.13 with an R² score of 0.9752. These results indicate that the hybrid architecture provides more accurate predictions and better captures the complex patterns in stock price movements. The findings confirm that combining CNN and LSTM architectures enables the model to learn both spatial and temporal representations of financial time-series data. CNN layers effectively identify local patterns within historical price sequences, while LSTM layers capture long-term dependencies that influence future stock prices. Consequently, the hybrid CNN–LSTM framework offers a reliable approach for financial forecasting and has strong potential for practical applications in stock market prediction systems. Future work may incorporate additional technical indicators, sentiment data, or attention-based mechanisms to further enhance prediction accuracy and robustness.
Co-Authors Akhyar Bintang Aprillia Sahuburua, Yuliana Imam Mahfudz I'tisyam Rafitajudin, Rafitajudin Wijaya, Nurhadi