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All Journal Sinkron : Jurnal dan Penelitian Teknik Informatika JURNAL TEKNOLOGI DAN ILMU KOMPUTER PRIMA (JUTIKOMP)
Halawa, Nestina
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Computer Science & IT

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Classification of Breast Cancer with Transfer Learning on Convolutional Neural Network Models Wijaya, Bayu Angga; Hulu, Mesrawati; Resel, Resel; Halawa, Nestina; Tarigan, Angki Angkota
Sinkron : jurnal dan penelitian teknik informatika Vol. 8 No. 3 (2024): Research Artikel Volume 8 Issue 3, July 2024
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/sinkron.v8i3.13792

Abstract

Breast cancer is a serious medical condition and a leading cause of death among women. Early and accurate diagnosis is crucial for improving patient outcomes. This study explores the use of Convolutional Neural Networks (CNNs) with Transfer Learning using DenseNet121 and ResNet50 models to enhance breast cancer classification via mammography. Transfer Learning enables CNN models to leverage knowledge learned from larger datasets such as ImageNet to improve performance on specific breast cancer datasets. The dataset comprised medical images with three breast variations: benign, malignant, and normal, totaling 531 data points. Data was split with a 70% training and 30% validation ratio. Two CNN models, AlexNet and ResNet50, were evaluated to compare their performance in classifying these breast cancer types. The experimental results show that AlexNet achieved a training accuracy of 98.01%, while ResNet50 achieved 64.07%. AlexNet demonstrated superior performance in identifying complex patterns in mammography images, resulting in more accurate classification of different breast cancer types. These findings highlight the potential of deep learning applications to support more precise and effective medical diagnostics for breast cancer. This research contributes significantly to the development of AI technologies in healthcare aimed at improving early detection of breast cancer. The implications of this study could expand our understanding of Transfer Learning applications in medical contexts, driving further advancements in this field to enhance patient care and prognosis
Supply Chain Analysis in the Health Sector Using Gradient Boosting Regression Algorithm Wijaya, Bayu Angga; Halawa, Nestina
JURNAL TEKNOLOGI DAN ILMU KOMPUTER PRIMA (JUTIKOMP) Vol. 8 No. 1 (2025): Jurnal Teknologi dan Ilmu Komputer Prima (JUTIKOMP)
Publisher : Fakultas Teknologi dan Ilmu Komputer Universitas Prima Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34012/jutikomp.v8i1.6822

Abstract

Supply chain analysis in healthcare is a crucial aspect in ensuring efficient and optimized resource distribution. This study uses the Gradient Boosting Regression algorithm to predict demand in healthcare supply chains to improve the accuracy of stock planning and management trained using supply datasets from hospitals. The model evaluation results show that most of the predictions are close to the actual values, as seen from the points clustered around the reference line. Despite the slight deviations, the Mean Absolute Error (MAE) value of 157.16 indicates that the average prediction error is relatively small compared to the demand scale which ranges from 0 to 14,000. This indicates that the Gradient Boosting Regression model performs reasonably well in estimating supply chain demand in the healthcare sector. Thus, this approach has the potential to be used in more accurate decision-making, in order to improve the efficiency of distribution and availability of health resources
Co-Authors Bayu Angga Wijaya Hulu, Mesrawati Resel, Resel Tarigan, Angki Angkota