Automatic Classification of Public Complaints Using Naive Bayes

The rapid advancement of information and communication technology has transformed the way humans interact and exchange information. Among various digital communication tools, email remains one of the most widely used; however, it is often exploited to send spam messages. Spam emails can contain phishing links, malware, or unsolicited advertisements, posing significant risks to individuals and organizations. Therefore, developing accurate and efficient spam detection methods is becoming increasingly important. This study proposes a lightweight and efficient spam email classification approach using the naïve Bayes algorithm combined with TF-IDF feature extraction and the synthetic minority oversampling technique (SMOTE) to address class imbalance. A series of preprocessing steps tokenization, lemmatization, stopword removal, and term frequency-inverse document frequency (TF-IDF) transformation were applied to normalize and vectorize email text data. The SMOTE technique was applied precisely to the training dataset to balance the class distribution and avoid data leakage during evaluation. Experimental results showed that the naïve Bayes model initially achieved 88% accuracy, 86% recall, 100% precision, and 92% F1 score. After proper application of SMOTE, the model achieved 100% accuracy, precision, recall, and F1 score, indicating perfect classification of spam and non-spam (ham) emails. These results confirm that proper class balancing significantly improves the model’s ability to detect spam emails. Overall, this study highlights the effectiveness of combining TF-IDF, naïve Bayes, and SMOTE as a robust yet computationally efficient solution for modern spam detection, particularly suited to real-time and resource-constrained environments.

The popularity of online games continues to increase, including Free Fire, which has gained more than one billion downloads and millions of user reviews on the Google Play Store. However, the variation and inconsistency of user comments make manual sentiment evaluation difficult. This study aims to compare the performance of Support Vector Machine (SVM) and Naïve Bayes in classifying user review sentiment on the Free Fire game. A total of 535 Indonesian-language reviews were collected using web scraping and processed through text cleaning, case folding, normalization, stopword removal, and stemming. Sentiment labels were assigned manually based on review content. The dataset was divided into training and testing using a 70:30 ratio, and feature extraction used Term Frequency–Inverse Document Frequency (TF-IDF). Two scenarios were implemented: a baseline without class balancing and a scenario using Synthetic Minority Oversampling Technique (SMOTE) to address class imbalance. Results show that SVM outperforms Naïve Bayes in both scenarios. In the baseline, SVM achieved 89.81% accuracy, while Naïve Bayes obtained 82.80%. After SMOTE, SVM improved to 91.08% accuracy and Naïve Bayes to 89.17%. These findings indicate that SVM, especially with SMOTE, provides a more effective and balanced performance for sentiment classification on Free Fire reviews. The study contributes to providing a more accurate understanding of user perception and strengthening model development for sentiment analysis on digital game applications.

According to the Indonesian Health Profile in 2017, diabetes is one of the causes of death for almost 70% in the world. The high mortality rate induces the need for making the effort to reduce the number of people with diabetes by conducting studies that lead to making a diagnosis so that can detect a person with diabetes accurately. This study tries to compare the performance of the K-Nearest Neighbors algorithm using Synthetic Minority Over-sampling Technique and the K-Nearest Neighbors algorithm without Synthetic Minority Over-sampling Technique in diagnosing diabetes on imbalanced datasets. The parameters tested are the k value of the K-Nearest Neighbors and Synthetic Minority Over-sampling Technique. The testing is carried out using the K-Fold Cross Validation strategy. The data used in this study were 3876 data from Pertamina Central Hospital. Based on the results of tests conducted, it shows that the value of accuracy produced in diagnosing diabetes by using Synthetic Minority Over-sampling Technique is better than the accuracy produced without using Synthetic Minority Over-sampling Technique with the highest accuracy increase of 8.25%. The highest average accuracy is obtained when the value of k = 3 in the K-Nearest Neighbors, k = 5 in the Synthetic Minority Over-sampling Technique, and fold = 10, which reaches 78.06%.

Landslides are a significant natural disaster threat in Indonesia, including Samarinda City. This study aims to evaluate the classification accuracy of landslides using the Naïve Bayes algorithm, combined with the Synthetic Minority Oversampling Technique (SMOTE) for class imbalance and Particle Swarm Optimization (PSO) for feature Selection and parameter optimization. Data were obtained from BPBD and BMKG Samarinda for 2022-2024, evaluated using 10-Fold Cross Validation and Confusion Matrix. The results show that SMOTE did not always improve model accuracy. Naïve Bayes without SMOTE achieved an average accuracy of 87.18%, while with SMOTE, it decreased to 70.55%. This indicates that SMOTE’s impact depends on the dataset. PSO, used for feature Selection and parameter optimization, gave mixed results. Naïve Bayes + PSO Feature Selection achieved an average accuracy of 68.25%, lower than the base model with SMOTE. Naïve Bayes + PSO Optimization achieved the best accuracy at 71.03%, though the improvement was minimal. The combination of Naïve Bayes + PSO Feature Selection + PSO Optimization had the lowest accuracy at 68.08%. This study emphasizes that the use of PSO for feature Selection and optimization needs careful consideration, especially when combined with SMOTE, as its effect on model performance is not always optimal.

Weighted-SMOTE: A modification to SMOTE for event classification in sodium cooled fast reactors

Automatic assessment of concept map quality is an important challenge in the field of education, particularly in evaluating students' conceptual understanding objectively and efficiently. This study aims to compare the performance of two machine learning algorithms, namely Random Forest and Ordinal Regression, in classifying the quality of concept maps. The evaluation was conducted on three approaches to text feature representation: Term Frequency-Inverse Document Frequency (TF-IDF), Bidirectional Encoder Representations from Transformers (BERT), and a combination of both (TF-IDF + BERT). Additionally, this study compares the performance of the models under two dataset conditions: original data and data balanced using the Synthetic Minority Over-sampling Technique (SMOTE), to address the class imbalance that often occurs in educational data. The data used consists of a collection of propositions from students' concept maps that have been labeled with ordinal scores based on quality. Text representation is extracted using the TF-IDF and BERT approaches, and then used as input to build the classification model. Performance evaluation was conducted using the metrics of Accuracy, Precision, Recall, F1-score, Cohen’s Kappa, and MAE. The results show that the Ordinal Regression model with TF-IDF representation combined with SMOTE achieved the best performance, with an accuracy of 0.8777, an F1-score of 0.8773, and a Cohen’s Kappa of 0.7701. These results indicate that classical feature representations like TF-IDF remain effective in limited data scenarios, and that the SMOTE technique successfully improved the model's performance by reducing bias towards the majority class. This research contributes to the development of an automatic concept map assessment system and suggests optimal classification strategies for educational datasets with ordinal and imbalanced characteristics

Due to the large span of flight delay, numerous influencing factors and large number of flights, the collected flight delay data is characterized by large-scale and imbalance, which is considered as one of the largest challenges in balanced flight delay data classification by using conventional classification algorithms. To solve this problem, an adaptive multi-class classification approach of imbalanced flight delay data using synthetic minority over-sampling technique (SMOTE)-based convolutional neural network with sparrow search algorithm, namely SSA-LTCNN is proposed in this paper. Firstly, the SMOTE is used to reduce the imbalance between the classes of the flight delay data. Then, the qualitative analysis method is employed to determine the key parameters of the CNN, which are optimized by using SSA to establish an optimized CNN model. Finally, the optimized CNN model with more effective feature representation ability is employed to extract discriminative features from the balanced flight delay data automatically. The obtained features in the last fully connected layer of the optimized CNN model are used for flight delay classification. The MIT-BIH Arrhythmia Data is selected to verify the effectiveness of the proposed SSA-LTCNN method and the confusion matrix, classification accuracy, precision, recall and F1 score are also used to evaluate the performance of the proposed SSA-LTCNN method, which can get better performance than RF, DT, LR, CNN and QRSCNN. The actual flight delay data is processed to verify the effectiveness of the proposed classification approach, which can achieve higher accuracy than KNN, NB, UCNN, CNN, SSA-LCNN and SSA-TCNN in different time periods. The experimental results show that the SSA-LTCNN can fast and effectively realize the classification of MIT-BIH Arrhythmia Data and flight delay data.

Nowadays, information technology especially machine learning has been used to evaluate the feasibility of debtors. One of the challenges in this classification model is the occurrence of imbalanced datasets, especially in the German Credit Dataset. Another challenge is developing an optimal model for evaluating debtor eligibility. Based on these challenges, this study aims to develop an optimal model for evaluating debtor eligibility on the German Credit Dataset, using the decision trees, k-Nearest Neighbor (k-NN) and Synthetic Minority Oversampling Technique (SMOTE). SMOTE and k-NN is used to overcome challenges regarding imbalanced datasets. While the decision tree are applied to produce a debtor classification model. In general, the steps taken are preparing datasets, pre-processing data, dividing datasets, oversampling with SMOTE, and classification models using decision trees, and testing. Model performance evaluation is represented by accuracy values obtained from the confusion matrix and area under curve (AUC) values generated by the Receiver Operating Characteristic (ROC). Based on the tests that have been carried out, the best accuracy value in the test is obtained at 73.00% and the AUC value is 0.708, in parameters k = 3 and Max-Depth = 25. Based on the analysis produced, the proposed model can improve performance compared to if the dataset is not applied SMOTE.

This study investigates sentiment analysis on user reviews from Bukalapak, a major Indonesian e-commerce platform, using the Multinomial Naïve Bayes (MNB) classifier. The study focuses on tackling the challenge of data imbalance and the linguistic complexities of Indonesian, such as slang, affixes, and negation, which are common in user reviews. Data was collected through web scraping from Bukalapak's app on the Google Play Store, resulting in a dataset of 19,999 reviews. A structured preprocessing pipeline was employed, including text normalization, tokenization, stopword removal, stemming, and term frequency-inverse document frequency (TF-IDF) weighting to prepare the data. The sentiment analysis results show that the model performs well in categorizing neutral reviews (accuracy 81%), but struggles with positive and negative sentiments due to data imbalance, leading to lower accuracy for these categories. The study highlights the effectiveness of Multinomial Naïve Bayes in large-scale sentiment analysis tasks in the e-commerce domain, particularly for platforms with large volumes of user-generated content. The study also introduces SMOTE (Synthetic Minority Over-sampling Technique) for handling data imbalance and k-fold cross-validation for model evaluation, significantly improving the model’s reliability. The research concludes that sentiment analysis can greatly benefit e-commerce platforms by improving customer service, informing product management decisions, and providing valuable insights for business strategies.

Social media platforms and microblogging websites have gained accelerated popularity during the past few years. These platforms are used for expressing views and opinions about products, personalities, and events. Often during discussions and debates, fights take place on social media platforms which involves using rude, disrespectful, and hateful comments called toxic comments. The identification of toxic comments has been regarded as an essential element for social media platforms. This study introduces an ensemble approach, called regression vector voting classifier (RVVC), to identify the toxic comments on social media platforms. The ensemble merges the logistic regression and support vector classifier under soft voting criteria. Several experiments are performed on the imbalanced and balanced dataset to analyze the performance of the proposed approach. For data balance, the synthetic minority oversampling technique (SMOTE) is used on the imbalanced dataset. Furthermore, two feature extraction approaches are utilized to investigate their suitability such as term frequency-inverse document frequency (TF-IDF) and bag-of-words (BoW). The performance of the proposed approach is compared with several machine learning classifiers using accuracy, precision, recall, and F1-score. Results suggest that RVVC outperforms all other individual models when TF-IDF features are used with SMOTE balanced dataset and achieves an accuracy of 0.97.

In the digital age, online shopping has become prevalent, with platforms like the Google Play Store enabling users to download and review mobile applications. This study aims to analyze the sentiment of user reviews for the Blibli application on the Google Play Store using the Naive Bayes Classifier, a simple yet effective algorithm for text classification tasks. A total of 2500 recent reviews were scraped using Google Colaboratory and the Python programming language. Data preprocessing steps included cleaning, stopword removal, tokenization, and stemming, followed by addressing class imbalance using the Synthetic Minority Over-sampling Technique (SMOTE). The dataset was divided into training and testing sets in an 80:20 ratio. The Naive Bayes algorithm with SMOTE was employed for sentiment classification, yielding an accuracy of 90%, precision of 90%, recall of 92%, and an F1-score of 91%. These results demonstrate the model's reliability in distinguishing between positive and negative sentiments, with a slight bias towards positive sentiments. Additionally, word cloud visualizations were generated to highlight frequently occurring words in both positive and negative reviews. The findings provide valuable insights for Blibli application developers and stakeholders, aiding in the assessment of user satisfaction and identification of areas for improvement. This research underscores the efficacy of the Naive Bayes Classifier in sentiment analysis and the utility of Google Colaboratory for data processing tasks.

Wearable electroencephalography (EEG) devices for seizure detection accuracy and reliability are deep learning (DL) applications in the field of epilepsy diagnosis. In this study, we sought to increase the accuracy of seizure detection using advanced DL algorithms on the Children’s Hospital Boston - Massachusetts Institute of Technology (CHB-MIT) EEG database. First, a fully convolutional network (FCN) was trained and assessed using accuracy and recall/precision metrics, and the early stopping technique was used to avoid overfitting. To assess the performance, the FCN was evaluated in terms of various metrics, including accuracy, precision, recall, F1-score, and receiver operating characteristic (ROC)-area under the curve (AUC). In addition, two-dimensional (2D) convolutional neural networks (CNNs) and long short-term memory (LSTM) models were used to model the database, and their performance was thoroughly measured using different metrics, graphs, and confusion matrices. Using LSTM variants, such as standard LSTM, bidirectional LSTM, stacked LSTM, and LSTM attention mechanisms, hybrid convolutional LSTM (ConvLSTM) models were trained and compared. The comparison was conducted based on the training and validation accuracy and loss, as well as the graphs resulting from the precision–recall curves. Apart from DL approaches, EEG signal analysis using time–frequency techniques, such as wavelet transform and short-time Fourier transform, has also been investigated. These methods assisted in the analysis of the time–frequency features of EEG signals in combination with DL models. This study demonstrates that the performance of wearable EEG devices can be augmented using a combination of DL and seizure signal processing techniques. The FCN achieved an accuracy of 92%, with a recall for seizures of 33%, an F1-score of 0.03, and strong ROC-AUC results. The 2D CNN achieved 96% accuracy, a seizure recall of 70%, an F1-score of 0.12, and an ROC-AUC score of 78%. The baseline LSTM struggled with effectiveness at 53% accuracy with a seizure recall of 18%. In contrast, the LSTM model, which incorporated synthetic minority oversampling technique (SMOTE) balancing, was able to reach improvements of up to 89% accuracy, with a precision of 91%, a recall of 86%, an F1-score of 0.89, and a strong ROC curve performance. Among the models, the LSTM with SMOTE was the best performer, with 89% accuracy, 91% precision, 86% recall, and an F1-score of 0.89. These results provide evidence that applying techniques for data balancing in combination with certain DL network architectures significantly improves the detection of seizures using wearable EEG devices worn on the body. We believe that real-time monitoring and high-performance systems are feasible using optimized DL frameworks. The analysis of the performance of different models allows for the understanding of the possibilities of optimizing the architectures of DL algorithms for the modern diagnosis of epilepsy in real time. The source code used to carry out the experiments is publicly available at CHB-MIT EEG Dataset Python Scripts (https://www.kaggle.com/code/adnankust/adnaneeg1).

Unbalanced datasets are a common problem in supervised machine learning. It leads to a deeper understanding of the majority of classes in machine learning. Therefore, the machine learning model is more effective at recognizing the majority classes than the minority classes. Naturally, imbalanced data, such as disease data and data networking, has emerged in real life. DDOS is one of the network intrusions found to happen more often than R2L. There is an imbalance in the composition of network attacks in Intrusion Detection System (IDS) public datasets such as NSL-KDD and UNSW-NB15. Besides, researchers propose many techniques to transform it into balanced data by duplicating the minority class and producing synthetic data. Synthetic Minority Oversampling Technique (SMOTE) and Adaptive Synthetic (ADASYN) algorithms duplicate the data and construct synthetic data for the minority classes. Meanwhile, machine learning algorithms can capture the labeled data's pattern by considering the input features. Unfortunately, not all the input features have an equal impact on the output (predicted class or value). Some features are interrelated and misleading. Therefore, the important features should be selected to produce a good model. In this research, we implement the recursive feature elimination (RFE) technique to select important features from the available dataset. According to the experiment, SMOTE provides a better synthetic dataset than ADASYN for the UNSW-B15 dataset with a high level of imbalance. RFE feature selection slightly reduces the model's accuracy but improves the training speed. Then, the Decision Tree classifier consistently achieves a better recognition rate than Random Forest and KNN.

Advanced hydrogeochemical facies classification: A comparative analysis of Machine Learning models with SMOTE in the Tawi basin

In this research, XGBoost algorithm and the SMOTETomek approach are employed with the objective of enhancing the accuracy of diabetes classification. The study utilises 2,000 patient data points, comprising demographic and medical information, sourced from Kaggle. The dataset employed in this study comprises a number of variables, including pregnancies, glucose levels, blood pressure, skin thickness, insulin levels, Body Mass Index (BMI), diabetes pedigree function, age, and an outcome variable. The latter is a binary classification label, taking on the values 0 and 1. A value of 0 indicates that the patient is not affected by diabetes, whereas a value of 1 indicates that the patient has diabetes. Diabetes represents a significant public health concern in Indonesia. A significant challenge in this study was the imbalanced nature of the dataset, which included a disproportionate number of non-diabetic samples relative to diabetic samples. To address this class imbalance, the researchers employed the SMOTETomek method. SMOTETomek integrates the SMOTE (Synthetic Minority Over-sampling Technique) and Tomek links algorithms to oversample the minority class and remove borderline samples, thereby balancing the class distributions. The SMOTETomek method achieved higher accuracy (95.01%) than SMOTE and the original data (both 92.13%), highlighting the benefits of combining SMOTE with Tomek Links for XGBoost. During testing, SMOTETomek slightly reduced the minority class accuracy (0.97 vs. 0.99 for SMOTE and original data) but maintained strong F1-score and precision, indicating effective handling of data imbalance despite minor trade-offs.