In imbalanced network traffic, malicious cyberattacks can be hidden in a large amount of normal traffic, making it difficult for intrusion detection systems (IDS) to detect them. Therefore, anomaly-based IDS with machine learning is the solution. However, a single machine learning cannot accurately detect all types of attacks. Therefore, a hybrid model that combines long short-term memory (LSTM) and random forest (RF) in three layers is proposed. Building the hybrid model starts with Nearmiss-2 class balancing, which reduces normal samples without increasing minority samples. Then, feature selection is performed using chi-square and RF. Next, hyperparameter tuning is performed to obtain the optimal model. In the first and second layers, LSTM and RF are used for binary classification to detect normal data and attack data. While the third layer model uses RF for multiclass classification. The hybrid model verified using the CSE-CIC-IDS2018 dataset, showed better performance compared to the single algorithm. For multiclass classification, the hybrid model achieved 99.76% accuracy, 99.76% precision, 99.76% recall, and 99.75% F1-score.

Language serves as a crucial tool for human communication, facilitating the exchange of information through various mediums, including journalism. In Indonesia, Language serves as a crucial tool for human communication, facilitating the exchange of information through various mediums, including journalism. In Indonesia,

The development of technology in the field of journalism has grown very rapidly. However, in the field of journalism there are still frequent deviations from the language on online news portals. This can be seen from the aspect of spelling and word usage. Spelling mistakes that occur in the news can cause the information contained in the news to be unclear and ambiguous. Based on these problems, a study was conducted to create a model to detect type error in Indonesian. This model is created using the random forest algorithm. random forest is an algorithm that works by building several decision trees and then combining the decisions from each tree that has been built and taking the most votes from the predictions of each tree so that it will produce stable and accurate predictions. The results of the accuracy of the model in the research that has been done is 100%. However, it should be noted that this 100% result is that the model is able to detect words that are already contained in the dataset. Based on the evaluation results that have been carried out, because the detected word is contained in the dataset, the accuracy issued is 100%. The built model successfully detects type error in Tribunnews news articles.

The advent of Artificial Intelligence (AI) and Deep Learning, particularly in Natural Language Processing (NLP), has been transformative in journalism, targetin The advent of Artificial Intelligence (AI) and Deep Learning, particularly in Natural Language Processing (NLP), has been transformative in journalism, targetin

Journal of Medical Internet Research - International Scientific Journal for Medical Research, Information and Communication on the Internet #Preprint #PeerReviewMe: Warning: This is a unreviewed preprint. Readers are warned that the document has not been peer-reviewed by expert/patient reviewers or an academic editor, may contain misleading claims, and is likely to undergo changes before final publication, if accepted, or may have been rejected/withdrawn. Readers with interest and expertise are encouraged to sign up as peer-reviewer, if the paper is within an open peer-review period. Please cite this preprint only for review purposes or for grant applications and CVs (if you are the author). Background: Previous machine learning approaches for prostate cancer detection using gene expression data have shown remarkable classification accuracies. However, prior studies overlook the influence of racial diversity within the population and the importance of selecting outlier genes based on expression profiles to avoid overfitting caused by curse of dimensionality. Objective: This research aims to create a classification method to diagnose prostate cancer using gene expression in specific populations. Methods: This research uses Differential Gene Expression (DGE) analysis, Receiver Operating Characteristic (ROC) analysis, and MSigDB verification as a feature selection framework to identify genes for constructing Support Vector Machine (SVM) models. Results: As a result, 5 scenarios were created based on the number of feature selection genes, which consists of 4,7,9,13 and 139 features. The best performing model used 139 genes as features without oversampling achieved testing accuracies of 98% for the white race and 97% for the African American race. Conclusions: This research develops a race-specific diagnosis method for prostate cancer detection using enhanced feature selection and machine learning. The results emphasize the potential for developing unbiased diagnostic tools across diverse populations.

"Kata terikat" represent a commonly utilized word class in Indonesian journalistic articles, yet their usage is often erroneous. "Kata