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All Journal Scientific Journal of Informatics Journal of Economic Education J-SAKTI (Jurnal Sains Komputer dan Informatika) Nusantara Science and Technology Proceedings J-SAKTI (Jurnal Sains Komputer dan Informatika) Recursive Journal of Informatics International Journal of Active Learning Indonesian Journal of Advocacy and Legal Services Journal of Advances in Information Systems and Technology
Anggyi Trisnawan Putra
Universitas Negeri Semarang
Agriculture, Biological Sciences & Forestry Chemical Engineering, Chemistry & Bioengineering Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Energy Engineering Law, Crime, Criminology & Criminal Justice Library & Information Science Materials Science & Nanotechnology Medicine & Pharmacology

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Journal : Recursive Journal of Informatics

 1 
Optimizing Random Forest for Predicting Thoracic Surgery Success in Lung Cancer Using Recursive Feature Elimination and GridSearchCV Putra, Deonisius Germandy Cahaya; Putra, Anggyi Trisnawan
Recursive Journal of Informatics Vol 2 No 2 (2024): September 2024
Publisher : Universitas Negeri Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15294/rji.v2i2.73154

Abstract

Abstract. Lung cancer is one of the deadliest forms of cancer, claiming numerous lives annually. Thoracic surgery is a strategy to manage lung cancer patients; however, it poses high risks, including potential nerve damage and fatal complications leading to mortality. Predicting the success rate of thoracic surgery for lung cancer patients can be accomplished using data mining techniques based on classification principles. Medical data mining involves employing mathematical, statistical, and computational methods. In this study, the prediction of thoracic surgery success employs the random forest algorithm with recursive feature elimination for feature selection. The feature selection process yields the top 8 features. The 8 best features include 'DGN', 'PRE4', 'PRE5', 'PRE6', 'PRE10', 'PRE14', 'PRE30', and 'AGE'. Hyperparameter using GridSearchCV is then applied to enhance classification accuracy. The results of this method implementation demonstrate a predictive accuracy of 91.41%. Purpose: The study aims to develop and evaluate a Random Forest model with a Recursive Feature Elimination feature selection and applies hyperparameter GridSearchCV for predicting thoracic surgery success rate. Methods: This study uses the thoracic surgery dataset and applies various data preprocessing techniques. The dataset is then used for classification using the Random Forest algorithm and applies the Recursive Feature Elimination feature selection to obtain the best features. GridSearchCV is used in this study for hyperparameter. Result: The accuracy using the Random Forest algorithm and Recursive Feature Elimination feature selection with hyperparameters tuning GridSearchCV resulted in an accuracy of 91,41%. The accuracy was obtained from the following parameters values: bootstrap set to false, criterion set to gini, n_estimator equal to 100, max_depth set to none, min_samples_split equal to 4, min_samples_leaf equal to 1, max_features set to auto, n_jobs set to -1, and verbose set to 2 with 10-fold cross validation. Novelty: This study comparison and analysis of various dataset preprocessing methods and different model configurations are conducted to find the best model for predicting the success rate of thoracic surgery. The study also employs feature selection to choose the best feature to be used in classification process, as well as hyperparameter tuning to achieve optimal accuracy and discover the optimal values for these hyperparameters.
Optimization of the Convolutional Neural Network Method Using Fine-Tuning for Image Classification of Eye Disease Wulandari, Vivi; Putra, Anggyi Trisnawan
Recursive Journal of Informatics Vol 2 No 1 (2024): March 2024
Publisher : Universitas Negeri Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15294/rji.v2i1.73625

Abstract

The eye is the most important organ of the human body which functions as the sense of sight. Most people wish they had healthy eyes so they could see clearly about life around them. However, some people experience eye health problems. There are many types of eye diseases ranging from mild to severe. With advances in technology, artificial intelligence can be used to classify eye diseases accurately, one of which is deep learning. Therefore, this study uses the Convolutional Neural Network (CNN) algorithm to classify eye diseases using the VGG16 architecture as a base model and will be combined using a fine-tuning model as an optimization to improve accuracy.
Image classification of Human Face Shapes Using Convolutional Neural Network Xception Architecture with Transfer Learning Adityatama, Resta; Putra, Anggyi Trisnawan
Recursive Journal of Informatics Vol 1 No 2 (2023): September 2023
Publisher : Universitas Negeri Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15294/rji.v1i2.70774

Abstract

Abstract. The development of information technology in facial recognition is influenced by a faster and more accurate authentication system. This allows the computer system to identify a person's face. Purpose: Similar to fingerprints and the retina of the human eye, each person's face has a different shape and contour. Since it is known that the human face provides a lot of information, as well as topics that attract attention make it studied intensively. Methods/Study design/approach: Several studies examining information from human faces are facial recognition. One of the approaches used to recognize facial imagery is through the use of a Convolutional Neural Network (CNN). CNN is a method in the field of Deep Learning that can be used to recognize and classify objects in digital images. In this study, the method used to implement facial image classification is the Xception architecture CNN algorithm with a transfer learning approach. Result/Findings: The dataset used in this study was obtained from Kaggle, namely the Face Shape Dataset which contains 5000 data. After testing, an accuracy rate of 96.2% was obtained in the training process and 81.125% in the validation process. This study also uses new data to test the model that has been made, and the results show an accuracy rate of 85.1% in classifying facial imagery. Novelty/Originality/Value: Therefore, it can be said that the model created in this study has the ability to classify images of facial shapes Human Face Shapes Using Convolutional Neural Network Xception Architecture with Transfer Learning.
Co-Authors Adityatama, Resta Akhmad Munawar Alamsyah - Alfath Yanuarto Amaliyah, Tazkiyatun Aeni Andika Enggal Ramadhan Avi Budi Setiawan Bhekti Kumorowati, Bhekti Cahya Wulandari Dinar Diaz Septian Emha, Zidney Ilma Fazaada Endang Sugiharti, Endang Hassan, Muhamad Sayuti Ilham Insani, Muhammad Ilham Insani, Muhammad Martanto Setyo Husodo Much Aziz Muslim Muh. Sholeh Priliani, Erlin Mega Priliani, Erlin Mega Putra, Deonisius Germandy Cahaya S.Pd. M Kes I Ketut Sudiana . Sugianto Sugianto Sutarti, Sri Sutarti, Sri uminar Vivi Wulandari Wicaksana, Tania Widyastuti, Ariyani