Article Per Year (5 Year)
p-Index From 2021 - 2026
3.403
P-Index
This Author published in this journals
All Journal Scientific Journal of Informatics Journal of Economic Education Jurnal Penelitian Pendidikan IPA (JPPIPA) J-SAKTI (Jurnal Sains Komputer dan Informatika) Nusantara Science and Technology Proceedings J-SAKTI (Jurnal Sains Komputer dan Informatika) Recursive Journal of Informatics Jurnal Penelitian Pendidikan International Journal of Active Learning Indonesian Journal of Advocacy and Legal Services Journal of Advances in Information Systems and Technology Jurnal Abdi Negeri Recursive Journal of Informatics Inventa: Journal of Science, Technology, and Innovation
Anggyi Trisnawan Putra
Universitas Negeri Semarang
Agriculture, Biological Sciences & Forestry Automotive Engineering Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Chemistry Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Industrial & Manufacturing Engineering Law, Crime, Criminology & Criminal Justice Library & Information Science Materials Science & Nanotechnology Mechanical Engineering Medicine & Pharmacology Physics

Published : 31 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 6 Documents
Search
Journal : Recursive Journal of Informatics

 1 
Implementation of Bidirectional Long-Short Term Memory (Bi-LSTM) and Attention to Detect Political Fake News Using IndoBERT and GloVe Embedding Adham Satria Firmansyah; Anggyi Trisnawan Putra
Recursive Journal of Informatics Vol. 3 No. 2 (2025): September 2025
Publisher : Universitas Negeri Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15294/rji.v3i2.159

Abstract

Abstract. Indonesian political news is now increasingly spread through various media, especially social and online media. However, a lot of fake news are spread to bring down political opponents or attract public sympathy in order to find their own supporters. Of course, this news need to be watched out for and preventive measures must be taken so as not to cause misunderstanding in the wider community. Purpose: This study was conducted to detect the political news whether it’s classified as hoax or fact by its narration. Also, understanding how to build the news detector using corresponding architecture and word embeddings. Methods/Study design/approach: The model architecture of Bi-LSTM and attention mechanism is used to reach the goals from this study’s purposes. Many studies have been conducted to detect hoaxes but have not yet paid attention to the context of sentences and the contribution of words in a news text so that this architecture is made to overcome this problem. It uses IndoBERT to optimize the model for Indonesian language and also GloVe to obtain the word weights from pre-trained embedding. Then, the tokenization process is performed with IndoBERT and keras to generate token id and attention mask. After receiving the token id and attention mask as input, the data training process is performed for three architectural scenarios with each configuration of 20 epochs, batch size of 32, and the learning rate is 0.00001. Result/Findings: The results of this study are defined by a confusion matrix which contains accuracy, recall, precision, and F1-score as the evaluation. The combination of Bi-LSTM + Attention + IndoBERT + GloVe obtains the best result of 97,71% of accuracy, 96,33% of precision, 97,93% of recall, and 97,72% of F1-score. Novelty/Originality/Value: This study combines two word embeddings in order to make sure the weight of words is completely defined and optimized into the Bi-LSTM and attention mechanism architecture.
Optimizing Heart Disease Classification Using the Support Vector Machine Algorithm with Hybrid Particle Swarm and Grey Wolf Optimization Luthfi Ilham Agus Pratama; Anggyi Trisnawan Putra
Recursive Journal of Informatics Vol. 3 No. 1 (2025): March 2025
Publisher : Universitas Negeri Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15294/rji.v3i1.737

Abstract

Abstract. Heart disease, also known as cardiovascular disease, is a condition that affects the heart and blood vessels, leading to complications such as coronary artery disease, heart failure, arrhythmias, and heart valve disorders. According to the World Health Organization (WHO), approximately 17.9 million people die from heart disease each year. Early detection plays a crucial role in reducing the number of cases and improving patient outcomes.Purpose: In the era of rapid technological advancements, machine learning has been widely utilized for early diagnosis of heart disease. This study aims to enhance classification accuracy by applying a hybrid PSOGWO (Particle Swarm and Grey Wolf Optimization) method for feature selection and a standard scaler for data balancing in SVM classification.Methods/Study design/approach: The research begins with obtaining a heart disease dataset, which undergoes preprocessing steps, including feature selection using hybrid PSOGWO and data normalization with a standard scaler. The dataset is then divided into training and testing sets, where the training data is classified using SVM. Performance evaluation is conducted using a confusion matrix to measure accuracy improvements.             Result/Findings: The proposed method successfully selects 10 significant features out of 13 in the dataset. By integrating hybrid PSOGWO with SVM, the classification accuracy improves to 93.66%, representing a 2.44% increase from the original 91.22% obtained using SVM without feature selection.              Novelty/Originality/Value: This research contributes to the development of more effective heart disease prediction models by optimizing feature selection and classification, leading to improved diagnostic accuracy and potential clinical applications.
Optimization Of K-Nearest Neighbor Algorithm Using Information Gain And Hyperparameter Tuning In Adult Male Fertility Classification Muhammad Zaenal Muttaqin; Anggyi Trisnawan Putra
Recursive Journal of Informatics Vol. 4 No. 1 (2026): March 2026
Publisher : Universitas Negeri Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15294/rji.v4i1.14868

Abstract

Abstract. Male fertility plays an important role in reproductive capability and global population dynamics. Male infertility can be caused by lifestyle, health conditions, and sperm quality. This research develops a male fertility classification model with an optimized K-Nearest Neighbor (KNN) algorithm using Information Gain feature selection and hyperparameter tuning with GridSearchCV. The main problems encountered are low accuracy in prediction and high computational complexity due to many irrelevant features. To overcome this, feature selection and hyperparameter optimization methods were used. The dataset used in this research comes from the UCI Machine Learning Repository, consisting of 100 data with 10 attributes. The KNN algorithm was chosen for its simplicity and ability to classify data with multiple classes and uneven distribution. However, its accuracy is highly dependent on the proper selection of features and parameters. The Information Gain method is used for selection of significant features against the target variable, reducing model complexity and computation time. Hyperparameter tuning is performed using GridSearchCV to find the best combination of parameters. The results showed that the application of Information Gain and GridSearchCV successfully improved the classification accuracy of KNN. The final model achieved 94% accuracy, better than the previous conventional method which only reached 84%. This increase in accuracy shows that KNN optimization with this approach is effective in improving male fertility classification performance. This research is expected to contribute to the development of male fertility diagnostic technology and the implementation of more accurate prediction models in clinical practice. Purpose: The proposed model is a development based on previous research that focuses on developing the K-Nearest Neighbor algorithm with a model accuracy of 84%. this study uses feature selection techniques and hyperparameter tuning in the K-Nearest Neighbor (KNN) algorithm to improve the accuracy of the male fertility classification model. Methods/Study design/approach: To improve the curation of the male fertility classification model and to optimize the model from previous research, this study uses the feature selection technique and hyperparameter tuning technique. For this technique, 2 types of optimization are carried out, namely feature selection using Information Gain and GridSearchCV hyperparameter tuning to get the best parameter combination for the proposed model. The fertility dataset has also been used in previous studies, used in this study.   Result/Findings: The proposed model obtained a high accuracy of 94%, which surpassed the model in the previous study which had an accuracy of 85% for the classification of fertility levels in men.  Novelty/Originality/Value: The novelty in this research is the addition of hyperparameter tuning techniques to optimize and obtain optimal parameters in the fertility classification model. This research also aims to improve and increase the accuracy of the previous model.
Optimizing Random Forest for Predicting Thoracic Surgery Success in Lung Cancer Using Recursive Feature Elimination and GridSearchCV Deonisius Germandy Cahaya Putra; Anggyi Trisnawan Putra
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/cax5k765

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 Vivi Wulandari; Anggyi Trisnawan Putra
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: 10.15294/0xga4r13

Abstract

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. Purpose:To find out the accuracy results obtained in the fine-tuning optimization model on Convolutional Neural Network (CNN) method in classifying images in eye disease. Methods/Study design/approach: Combining the Convolutional Neural Network (CNN) method with fine-tuning optimization models for image classification in eye disease. The two methods will be compared to determine the best result. Result/Findings: The accuracy results obtained from testing the Convolutional Neural Network method with the VGG16 architecture were 82.63% while the accuracy results from testing the fine-tuning model were 94.13%. Novelty/Originality/Value: The test results on the fine-tuning model have better accuracy than the testing of the Convolutional Neural Network method. This can be seen in the fine-tuning model which has an increase in accuracy of 11.5%.
Image classification of Human Face Shapes Using Convolutional Neural Network Xception Architecture with Transfer Learning Resta Adityatama; Anggyi Trisnawan Putra
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/xnw7v590

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 Adham Satria Firmansyah Adityatama, Resta Aji Purwinarko, Aji Alamsyah - Alexander, Taripar Matius Amaliyah, Tazkiyatun Aeni Arif Widiyatmoko, Arif Avi Budi Setiawan Bhekti Kumorowati, Bhekti Budi Astuti Budi Prasetiyo, Budi Cahya Wulandari Darmawan, Melissa Salma Deonisius Germandy Cahaya Putra Dewi Mustikaningtyas Emha, Zidney Ilma Fazaada Endang Sugiharti, Endang Hakim, M. Faris Al Hassan, Muhamad Sayuti Hery Sutarto Husodo, Martanto Setyo Ilham Insani, Muhammad Ilham Insani, Muhammad Luthfi Ilham Agus Pratama Martanto Setyo Husodo Much Aziz Muslim Muh. Sholeh Muh. Sholeh Muhammad Zaenal Muttaqin Munawar, Akhmad Norma Zuhrotul Hayati Priliani, Erlin Mega Priliani, Erlin Mega Putra, Deonisius Germandy Cahaya Rakainsa, Senda Kartika Ramadhan, Andika Enggal Resta Adityatama Riza Arifudin S.Pd. M Kes I Ketut Sudiana . Septian, Dinar Diaz Subhan Subhan Sugianto Sugianto Sutarti, Sri Sutarti, Sri uminar Vivi Wulandari Vivi Wulandari Wicaksana, Tania Widyastuti, Ariyani Yanuarto, Alfath Zaenal Abidin