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All Journal MATRIK : Jurnal Manajemen, Teknik Informatika, dan Rekayasa Komputer
Didih Rizki Chandranegara
Universitas Muhammadiyah Malang, Malang, Indonesia
Computer Science & IT

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Automated Detection of Breast Cancer Histopathology Image Using Convolutional Neural Network and Transfer Learning Didih Rizki Chandranegara; Faras Haidar Pratama; Sidiq Fajrianur; Moch Rizky Eka Putra; Zamah Sari
MATRIK : Jurnal Manajemen, Teknik Informatika dan Rekayasa Komputer Vol 22 No 3 (2023)
Publisher : LPPM Universitas Bumigora

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30812/matrik.v22i3.2803

Abstract

cancer caused 2.3 million cases and 685,000 deaths in 2020. Histopathology analysis is one of the tests used to determine a patient’s prognosis. However, histopathology analysis is a time-consuming and stressful process. With advances in deep learning methods, computer vision science can be used to detect cancer in medical images, which is expected to improve the accuracy of prognosis. This study aimed to apply Convolutional Neural Network (CNN) and Transfer Learning methods to classify breast cancer histopathology images to diagnose breast tumors. This method used CNN, Transfer Learning ((Visual Geometry Group (VGG16), and Residual Network (ResNet50)). These models undergo data augmentation and balancing techniques applied to undersampling techniques. The dataset used for this study was ”The BreakHis Database of microscopic biopsy images of breast tumors (benign and malignant),” with 1693 data classified into two categories: Benign and Malignant. The results of this study were based on recall, precision, and accuracy values. CNN accuracy was 94%, VGG16 accuracy was 88%, and ResNet50 accuracy was 72%. The conclusion was that the CNN method is recommended in detecting breast cancer to diagnose breast cancer.
Stroke Prediction with Enhanced Gradient Boosting Classifier and Strategic Hyperparameter Dela Ananda Setyarini; Agnes Ayu Maharani Dyah Gayatri; Christian Sri Kusuma Aditya; Didih Rizki Chandranegara
MATRIK : Jurnal Manajemen, Teknik Informatika dan Rekayasa Komputer Vol 23 No 2 (2024)
Publisher : LPPM Universitas Bumigora

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30812/matrik.v23i2.3555

Abstract

A stroke is a medical condition that occurs when the blood supply to the brain is interrupted. Stroke can cause damage to the brain that can potentially affect a person's function and ability to move, speak, think, and feel normally. The effect of stroke on health emphasizes the importance of stroke detection, so an effective model is needed in predicting stroke. This research aimed to find a new approach that can improve the performance of stroke prediction by comparing four derivative algorithms from Gradient Boosting by adding hyperparameters tuning. The addition of hyperparameters was used to find the best combination of parameter values that can improve the model accuracy. The methods used in this research were Categorical Boosting, Histogram Gradient Boosting, Light Gradient Boosting, and Extreme Gradient Boosting. The research involved retrieving, cleaning, and analyzing data and then the model performance was evaluated with a confusion matrix and execution time. The results obtained were Light Gradient Boosting with Hyperparameter RandomSearchCV achieved the highest accuracy at 95% among the algorithms tested, while also being the fastest in execution. The contribution of this research to the medical field can help doctors and patients predict the occurrence of stroke early and reduce serious consequences.
Stroke Prediction with Enhanced Gradient Boosting Classifier and Strategic Hyperparameter Dela Ananda Setyarini; Agnes Ayu Maharani Dyah Gayatri; Christian Sri Kusuma Aditya; Didih Rizki Chandranegara
MATRIK : Jurnal Manajemen, Teknik Informatika dan Rekayasa Komputer Vol. 23 No. 2 (2024)
Publisher : LPPM Universitas Bumigora

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30812/matrik.v23i2.3555

Abstract

A stroke is a medical condition that occurs when the blood supply to the brain is interrupted. Stroke can cause damage to the brain that can potentially affect a person's function and ability to move, speak, think, and feel normally. The effect of stroke on health emphasizes the importance of stroke detection, so an effective model is needed in predicting stroke. This research aimed to find a new approach that can improve the performance of stroke prediction by comparing four derivative algorithms from Gradient Boosting by adding hyperparameters tuning. The addition of hyperparameters was used to find the best combination of parameter values that can improve the model accuracy. The methods used in this research were Categorical Boosting, Histogram Gradient Boosting, Light Gradient Boosting, and Extreme Gradient Boosting. The research involved retrieving, cleaning, and analyzing data and then the model performance was evaluated with a confusion matrix and execution time. The results obtained were Light Gradient Boosting with Hyperparameter RandomSearchCV achieved the highest accuracy at 95% among the algorithms tested, while also being the fastest in execution. The contribution of this research to the medical field can help doctors and patients predict the occurrence of stroke early and reduce serious consequences.
Co-Authors Agnes Ayu Maharani Dyah Gayatri Christian Sri Kusuma Aditya Dela Ananda Setyarini Faras Haidar Pratama Moch Rizky Eka Putra Sidiq Fajrianur Zamah Sari