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All Journal International Journal of Advances in Data and Information Systems Methods in Science and Technology Studies
Reski, Julia Mega
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Computer Science & IT Electrical & Electronics Engineering Engineering

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Classification of Crystallization Images of Pharmaceutical Raw Materials Using Convolutional Neural Network Algorithm Yudhana, Anton; Reski, Julia Mega
International Journal of Advances in Data and Information Systems Vol. 6 No. 3 (2025): December 2025 - International Journal of Advances in Data and Information Syste
Publisher : Indonesian Scientific Journal

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59395/ijadis.v6i3.1440

Abstract

The rapid advancement of artificial intelligence (AI) has opened new opportunities for automation in the pharmaceutical industry, particularly in the classification of raw drug materials. Manual classification methods are time-consuming and prone to human error, highlighting the need for reliable automated solutions. This study applied a deep learning approach for classifying crystallization images of pharmaceutical raw materials using a Convolutional Neural Network (CNN). A dataset of 300 crystallization images of Nicotinamide and Ferulic Acid was obtained through hot-stage microscopy, preprocessed with normalization, resizing, and augmentation, and divided into training, validation, and testing subsets. The CNN model was trained for 10 epochs and evaluated using a confusion matrix and standard performance metrics (accuracy, precision, recall, and F1-score). The model achieved perfect recall for Ferulic Acid and 90% recall with 100% precision for Nicotinamide, resulting in an overall accuracy of 95%. While these results are promising, the relatively small dataset may limit generalization, and further validation with larger or external datasets is required. The findings indicate that CNN-based methods hold strong potential for automating crystallization classification, improving pharmaceutical quality control, and reducing reliance on manual assessment, in line with recent advances in medical and pharmaceutical image analysis.
Machine Learning Approach for Heart Failure Patient Classification Using K-Nearest Neighbors Algorithm Masitha, Alya; Lonang, Syahrani; Reski, Julia Mega
Methods in Science and Technology Studies Vol. 1 No. 2 (2025): December
Publisher : PT. Teknologi Futuristik Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.64539/msts.v1i2.2025.44

Abstract

Heart failure is a cardiovascular disease with a high mortality rate and tends to increase every year. Therefore, a method is needed that can help the process of classifying heart failure quickly and accurately. This study aims to design and implement a heart failure classification system using the K-Nearest Neighbor (K-NN) machine learning method. The dataset used consists of 918 patient data with eleven input variables and two output classes, namely patients diagnosed with heart failure and patients not diagnosed with heart failure. The research stages include data loading, dividing training data and test data, implementing the K-NN algorithm with various K values, and evaluating model performance using accuracy, precision, recall, and F1-score metrics. The test results show that variations in the K value have a significant effect on the performance of the classification model. The K value = 9 produces the best performance with an accuracy of 93.48%, a recall of 96.36%, and an F1-score of 94.64%, which indicates a good balance between precision and recall. Based on these results, the K-NN method with a value of K = 9 is recommended as the optimal configuration in the classification of heart failure disease in this study.
Co-Authors Anton Yudhana Syahrani Lonang