## 3 Methodology
Hence, the features performing at least as much as the average information gain and achieving the best gain ratio are selected. GR outperforms the information gain measure in terms of both accuracy and classifier complexity [20]. ### Conventional Machine Learning Methods

Machine learning is a broad discipline that spans information technology, statistics, probability, artificial intelligence, psychology, neurobiology, and many other disciplines. It also refers to teaching computers to think like humans while generating the field of statistics and fundamental statistical-computational theories of learning processes. Machine learning algorithms are categorized into groups, including supervised learning, unsupervised learning, semi-supervised learning, and reinforcement learning, depending on the way of seeking a solution to a problem [21; 22]. This study used the supervised learning method. Classes are manually separated and labeled beforehand in supervised learning algorithms. Mathematical models created with machine learning algorithms are trained and tested, aiming to achieve the highest prediction performance. This study utilized the Weka library to test conventional algorithms. It also used many algorithms in this library during the experimental design and selected the best-performing algorithms. Figure 1 presents a list of ML algorithms, and the following section briefly describes these algorithms. The Naive Bayes (NB) classifier is a powerful machine learning algorithm based on Bayes' theorem. It assumes independence from any condition or event, making it easy to implement and suitable for large datasets [23]. This method calculates the probability of a sample belonging to a class independently of others, identifying the highest probability class for classification. NB is widely used in text mining. Naive Bayes Multinomial (NBM) is an advanced version of the current Naive Bayes classifier that calculates the frequency of each word. It is well-established that frequency is highly effective in classifying the text into different categories. Therefore, the NBM algorithm is considered one of the best in text classification [24]. The Logistic Regression Tree (LRT) is a decision tree structure that integrates Logistic Regression principles. Each node in the tree uses a unique logistic algorithm, resembling conventional decision trees with child nodes. Predictions are made by performing logistic calculations at these nodes, comparing feature values to threshold values [25]. Sequential Minimal Optimization (SMO) is a popular classification algorithm used for training support vector machines in supervised machine learning. It resolves quadratic programming problems in SVM through an iterative algorithm that breaks the optimization task into smaller subproblems, which are solved analytically to avoid numerical QP optimization [26]. Random Forest (RF) is a machine learning algorithm that enhances prediction accuracy by utilizing multiple decision trees on different subsets of a dataset and averaging their results. Instead of relying on a single tree, it aggregates predictions from multiple trees. At each tree node, conditions are compared with one or more input data features. Each tree provides a class prediction, and the algorithm selects the most frequently predicted class as the final prediction. RF performs exceptionally well on unbalanced datasets, with very few classification errors [27]. Logistic Regression (LR) is a versatile technique used for classifying both linear and nonlinear data. It is particularly employed in models with binary responses, often represented as 0/1. In this representation, '1' signifies success, and '0' denotes failure. The values of 1 and 0 can vary based on the study's objectives. In binary classification, one class is labeled as '1', and the other is labeled as '0'. Logistic Regression is a machine learning algorithm that involves multiplying the input by weight values to make predictions [28].