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Moh. Diqi
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International Journal of Informatics Engineering and Computing
Published by ASTEEC Publisher
ISSN : -     EISSN : 30909112     DOI : https://doi.org/10.70687/ijimatic
Core Subject : Science,
Computer Science & IT
International Journal of Informatics Engineering and Computing (IJIMATIC) is an international, peer-reviewed, open-access journal that publishes original theoretical and empirical work on the science of informatics and its application in multiple fields. Our concept of informatics encompasses technologies of information and communication, as well as the social, linguistic, and cultural changes that initiate, accompany, and complicate their development. IJIMATIC aims to be an international platform to exchange novel research results in simulation-based science across all computer science disciplines.
Arjuna Subject : Ilmu Komputer (semua kategori) - Kecerdasan Buatan
Articles 5 Documents
 1 
Search results for , issue "Vol. 1 No. 2 (2024): International Journal of Informatics Engineering and Computing" : 5 Documents clear
Optimizing Machine Learning Algorithms to Accelerate Smoking Detection Fahrurrozi, Muhammad; Naru, Germanus
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.i2.42

Abstract

This study evaluates the performance of various classification algorithms, including Convolutional Neural Networks (CNN), Support Vector Machines (SVM), Decision Trees, K-Nearest Neighbors (KNN), Gaussian Naive Bayes (GNB), and Gradient Boosting (Gboost), on a binary classification task. The results reveal that CNN achieves perfect performance, with an accuracy of 1.00 and precision, recall, and F1-scores of 1.00 for both classes. Similarly, SVM, Decision Tree, KNN, and Gboost also demonstrate flawless performance across all metrics. In contrast, GNB underperforms significantly, with an accuracy of 0.78 and lower precision, recall, and F1-scores, particularly for the "no" class. These findings highlight CNN's robustness and reliability, positioning it as a top-performing algorithm for this classification task. The study underscores the effectiveness of CNN and other high-performing algorithms while identifying limitations in GNB. Future research could focus on optimizing computational efficiency and scalability for real-world applications.
Tomato Ripeness Identification Using Recurrent Neural Network Algorithm Hamdani, Dede; Wathan, M.Hizbul
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.i2.43

Abstract

Tomatoes undergo distinct ripeness stages, typically categorized into ripe, semi-ripe, and unripe phases. Traditional methods for assessing ripeness often face challenges in accuracy due to difficulties in comparing variables and subjective interpretations. This study proposes an innovative approach to classify tomato ripeness using a dataset of 200 tomato images and employs a Recurrent Neural Network (RNN) for precise classification. The experimental results demonstrate that the RNN-based model achieves a 95.0% accuracy rate in identifying ripeness stages, significantly outperforming conventional methods. This high level of accuracy highlights the model's potential to minimize errors and provide reliable assessments of tomato maturity. The proposed method offers a robust and efficient solution for agricultural applications, enabling improved quality control and harvest timing. Future research could explore the integration of additional data sources or advanced machine learning techniques to further enhance the model's performance and applicability across diverse agricultural contexts.
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.
Detecting Acute Liver Diseases Using CNN Algorithm Anjani, Sarah; Maria Yohana Jawa Betan
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.i2.45

Abstract

This study tackles the critical challenge of detecting Acute Liver Failure (ALF) using machine learning algorithms. The main goal is to assess the effectiveness of several algorithms, including Convolutional Neural Network (CNN), Support Vector Machine (SVM), Decision Tree, K-Nearest Neighbors (KNN), Gaussian Naive Bayes (GNB), and Gradient Boosting, in accurately classifying cases of ALF. For this purpose, a comprehensive dataset with 8,785 records and 30 features from Kaggle is utilized, involving thorough preprocessing steps like feature selection, data cleaning, and normalization. The research emphasizes achieving high precision in ALF detection. Results show that CNN outperforms other algorithms, achieving a precision score of 1.00 for identifying ALF cases, demonstrating its high reliability. This study highlights the importance of algorithm selection in complex medical diagnoses, showcasing the potential of deep learning methods in healthcare and paving the way for more accurate and timely ALF detection to improve patient outcomes.
Stepping up Onion Classification Model using CNN Algorithm Fan Hao Yi; Moh. K. Syed
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.i2.47

Abstract

Traditional shallot classification methods, relying on visual inspection or conventional image processing, face limitations in dataset identification. To address the issues, we propose a CNN model for classifying shallot types. The study involves collecting a large dataset, preprocessing, and training the model with optimized parameters to maximize accuracy. By adjusting hyperparameters, we achieve a balance between accuracy and performance time. With 50 epochs and a batch size of 64, the model achieves over 80% accuracy in classification tests. These results demonstrate the effectiveness of CNN in shallot classification, outperforming traditional methods. Future work could explore advanced architectures like Generative Adversarial Networks (GAN) and Graph Convolutional Networks (GCN) to further enhance the model's performance.

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