Particle Swarm Optimization For Feature Selection Research Articles

Naive Bayes Algorithm with Feature Selection Using Particle Swarm Optimization

The COVID-19 vaccine in Indonesia has led to the emergence of public opinion which is conveyed on social media such as Twitter. One of the analyses that can be done to produce various information from public opinion is sentiment analysis. Sentiment analysis is used to determine whether an opinion tends to be positive or negative. This study aims to classify the public opinion of the COVID-19 vaccine in Indonesia with sentiment analysis and to visualize the location of the sentiment of the COVID-19 vaccine tweet data in Indonesia. To achieve this aim, the Naïve Bayes algorithm with Particle Swarm Optimization (PSO) feature selection was used. This study uses opinions into positive and negative class sentiments towards 2,547 tweets related to the COVID-19 vaccine in Indonesia from January to June 2021. The results show that the distribution of positive and negative class sentiments is 2,328 and 219, respectively. In addition, the positive sentiment for the COVID-19 vaccine was dominated by people on the island of Java based on a random number matrix initialized by the PSO method. The classification of public opinion on Twitter media provides accurate and optimal performance results using a combination of the Naïve Bayes algorithm with PSO feature selection. The results of the combination of these methods have accuracy and F1 score values of 91.28% and 95.38%, respectively. The visualization of geo-spatial mapping showed that positive sentiments related to the COVID-19 vaccine exist in almost all regions in Indonesia but are dominated by the Jabodetabek area.

A machine learning-based framework using the particle swarm optimization algorithm for credit card fraud detection

The detection of fraudulent activities in credit cards transactions presents a significant challenge due to the constantly changing and unpredictable tactics used by fraudsters, who take advantage of technological advancements to evade security measures and cause substantial financial harm. In this paper, we suggested a machine learning based methodology to detect fraud in credit cards. The suggested method contains four key phases, including data normalization, data preprocessing, feature selection, classification. For classification artificial neural network, decision tree, logistic regression, naive bayes, random forest while for feature selection particle swarm optimization is employed. With the use of a dataset created from European cardholders, the suggested method was tested. The experimental results show that the suggested method beats the other machine learning techniques and can successfully classify frauds with a high detection rate.

Improving Breast Cancer Diagnosis Accuracy by Particle Swarm Optimization Feature Selection

Breast cancer has been one of the leading causes of death among women in the world. Early detection of this disease can save patient’s lives and reduce mortality. Due to the large number of features involved in the diagnosis of this disease, the breast cancer diagnosis process can be time consuming. To reduce cost and time and improving accuracy of breast cancer diagnosis, this paper propose a feature selection algorithm based on particle swarm optimization (PSO) combined with machine learning methods for selection the most effective features for breast cancer diagnosis among all features. In order to evaluate the efficiency of the proposed feature selection method, it was tested on three most common breast cancer datasets available in the University of California, Irvine (UCI) repository named: Coimbra dataset (CD), Wisconsin Diagnostic Breast Cancer dataset (WDBC) and Wisconsin Prognostic Breast Cancer dataset (WPBC). In the Coimbra dataset with all its 9 features and without PSO feature selection algorithm the highest obtained accuracy was 87% by Support Vector Machine method, while with PSO feature selection algorithm the accuracy reached to 91% and the number of features was reduced from 9 to 4. In the WDBC dataset with all its 30 features and without PSO feature selection algorithm the highest obtained accuracy was 99% by Random Forest method, while with PSO feature selection algorithm the accuracy reached to 100% and the number of features was reduced from 30 to 19. In the WPBC dataset with all its 33 features and without PSO feature selection algorithm the highest obtained accuracy was 94% by Support Vector Machine method, while with PSO feature selection algorithm the accuracy reached to 96% and the number of features was reduced from 33 to 17. The results of this paper indicated that the proposed feature selection algorithm based on PSO algorithm can improve the accuracy of breast cancer diagnosis. While it has selected fewer and more effective features than the total number of features in the original datasets.

Feature Selection Techniques for Enhancing Credit Card Fraud Detection Performance: A Hybrid Metaheuristic Approach Using Nature-Inspired Algorithms

Abstract: The surge in credit card usage has led to a parallel increase in fraudulent transactions, necessitating advanced detection systems. Traditional methods encounter challenges with high-dimensional and imbalanced datasets. This paper proposes a comprehensive approach integrating metaheuristic algorithms and deep learning techniques to enhance fraud detection accuracy. The first contribution, the Rock Hyrax Swarm Optimization Feature Selection (RHSOFS) algorithm, draws inspiration from the collective behaviors of rock hyrax swarms to effectively select relevant features from high-dimensional datasets. RHSOFS identifies an optimal subset of features critical for fraud detection through supervised machine learning. Complementing this, the second contribution leverages a hybrid deep learning model. Beginning by organizing transactional data and constructing a Logical Graph of Behavior Profile (LGBP) to abstract transaction details, the Modified Butterfly Optimization Algorithm (MBOA) selects important features from the dataset. The hybrid model, integrating Convolutional Neural Networks (CNN) and Recurrent Neural Networks (RNN), establishes rational connections between transactional characteristics, enhancing detection performance. To evaluate the approach, comparative efficiency analyses against existing methods, including Differential Evolutionary Feature Selection (DEFS), Genetic Algorithm Feature Selection (GAFS), Particle Swarm Optimization Feature Selection (PSOFS), and Ant Colony Optimization Feature Selection (ACOFS), are conducted. Results demonstrate the superiority of the approach in terms of both reliability and recognition rate, validated through rigorous statistical testing. The proposed hybrid approach signifies a significant advancement in credit card fraud detection systems, offering enhanced accuracy and efficiency in combating fraudulent activities amidst the growing complexity of transactional datasets.

An oscillatory particle swarm optimization feature selection algorithm for hybrid data based on mutual information entropy

Hybrid data lead to overfitting in machine learning models, which may reduce the accuracy of classification. Feature selection can not only reduce the computational cost of processing hybrid data but also improve the accuracy of classification. The particle swarm optimization (PSO) algorithm has clear advantages in feature selection. This paper presents an oscillatory particle swarm optimization feature selection algorithm for hybrid data based on mutual information entropy. First, a new distance function on the object set of a hybrid information system (HIS) is built, which yields a tolerance relation on this object set. Then, mutual information entropy is presented to measure the uncertainty of the HIS. On this basis, the maximum-relevance and minimal-redundancy model (MRMR model) for the HIS is proposed. Based on the MRMR model, a feature selection algorithm (denoted as MRMR) for the HIS is naturally designed. As the integration of the MRMR model and PSO can effectively explore all possible feature subsets, an oscillatory particle swarm optimization algorithm based on the MRMR model (denoted as OPSO-MRMR) for the HIS is also designed. Moreover, the MRMR model is utilized to define a fitness function that evaluates the quality of particles. The particle position update process is modified by means of a two-order oscillatory equation. Finally, an experimental analysis is conducted to compare the two designed algorithms with five other algorithms. The statistical analysis of classification accuracy and F1 score shows that OPSO-MRMR improves precision by 5.8% and 10.7% compared to the other six algorithms, respectively.

Abnormal Heart Sound Detection using Time-Frequency Analysis and Machine Learning Techniques

Phonocardiogram (PCG) signals contains valuable information pertaining to heart valve functionality, rendering them potentially useful for early detection of cardiovascular diseases. Automated classification of heart sounds harbors great promise for identifying cardiac pathologies. This paper introduces a novel automated approach to classify normal and abnormal heart sounds. Our methodology involves partitioning heart sounds into four segments: S1, S2, systolic, and diastolic, followed by extraction of time–frequency and time-statistical features. Prior to data classification, we employ two techniques - particle Swarm optimization (PSO) and Sequential Forward Feature Selection (SFFS) - for efficient feature selection. We assess the performance of the proposed method on the Physionet Challenge 2016 database, utilizing the 10-fold cross-validation method. To address the issue of dataset imbalance, we apply the synthetic minority over-sampling technique (SMOTE) to create balanced datasets. Our approach surpasses existing methods in the literature, as evidenced by its superior accuracy, sensitivity, and specificity metrics. Specifically, our method achieves an accuracy of 98.03%, a sensitivity of 97.64%, and a specificity of 98.43% in distinguishing normal from abnormal heart sounds on the Physionet database. These findings outperform the results obtained by previously established methods evaluated on the Physionet 2016 challenge database.

A Proposed Feature Selection Particle Swarm Optimization Adaptation for Intelligent Logistics—A Supply Chain Backlog Elimination Framework

A Proposed Feature Selection Particle Swarm Optimization Adaptation for Intelligent Logistics—A Supply Chain Backlog Elimination Framework

Optimal Single-Valued Neutrosophic Sine Trigonometric Aggregation Operators for Accurate Financial Fraud Detection Model

Financial fraud may be regarded as any fraud targeting financial organisations including crypto exchanges, banks, fintech, and lending organizations, or any criminal activity associated with the payment process. Financial fraud detection cites protocol set prepared to circumvent the destruction produced by fraudulent activities happening in financial service suppliers. Ecological financial fraud detection (FD) includes the usage of ethical and sustainable performs within fraud actions recognition from the financial area. In recent times, DL and ML techniques have been used in CCF recognition owing to their ability to construct a robust mechanism to discover fraud businesses. Therefore, this study develops an Optimal Single Valued Neutrosophic Sine Trigonometric Aggregation Operator (O-SVNSTAO) for Accurate Financial Fraud Detection Model. The genetic-inspired particle swarm optimization (GIPSO) feature selection model efficiently discerns the relevant attribute from sophisticated financial databases, improving the model's discriminative power while alleviating dimensionality problems. Consequently, the SVNSTAO classifier leverages the features selected to discern complicated features inherent in fraudulent actions, which facilitates accurate diagnosis. Moreover, the COA parameter tuning mechanism enhances the SVNSTAO model's parameter, which ensures adaptability and optimum performance to varied fraud settings. Empirical analysis of real-time financial datasets demonstrates the superiority of O-SVNSTAO technique over classical methods, underlining its effectiveness in discovering financial fraud with exceptional efficiency and reliability

Implementation of Particle Swarm Optimization on Sentiment Analysis of Cyberbullying using Random Forest

Social media has exerted a significant influence on the lives of the majority of individuals in the contemporary era. It not only enables communication among people within specific environments but also facilitates user connectivity in the virtual realm. Instagram is a social media platform that plays a pivotal role in the sharing of information and fostering communication among its users through the medium of photos and videos, which can be commented on by other users. The utilization of Instagram is consistently growing each year, thereby potentially yielding both positive and negative consequences. One prevalent negative consequence that frequently arises is cyberbullying. Conducting sentiment analysis on cyberbullying data can provide insights into the effectiveness of the employed methodology. This research was conducted as an experimental research, aiming to compare the performance of Random Forest and Random Forest after applying the Particle Swarm Optimization feature selection technique on three distinct data split compositions, namely 70:30, 80:20, and 90:10. The evaluation results indicate that the highest accuracy scores were achieved in the 90:10 data split configuration. Specifically, the Random Forest model yielded an accuracy of 87.50%, while the Random Forest model, after undergoing feature selection using the Particle Swarm Optimization algorithm, achieved an accuracy of 92.19%. Therefore, the implementation of Particle Swarm Optimization as a feature selection technique demonstrates the potential to enhance the accuracy of the Random Forest method.

Sentiment Analysis of BNI Mobile Application Using The K-Nearest Neighbor Algorithm (KNN) With Particle Swarm Optimization (PSO) Feature Selection

Sentiment Analysis of BNI Mobile Application Using The K-Nearest Neighbor Algorithm (KNN) With Particle Swarm Optimization (PSO) Feature Selection

KLASIFIKASI EMOSI PUBLIK TERHADAP LARANGAN PENGGUNAAN OBAT SIRUP MENGGUNAKAN ALGORITMA NAIVE BAYES

In October 2022 BPOM withdrew the circulation of syrup drugs and banned the public from using syrup drugs due to the increasing cases of kidney failure in children with a mortality rate of up to 59%. The phenomenon of mass death has caused psychological trauma that threatens. The purpose of this study was to find out how public comments on Twitter and Instagram social media regarding the prohibition of using syrup drugs by classifying emotions using the Naive Bayes algorithm with Particle Swarm Optimization (PSO) feature selection. The methodology used is the AI Project Cycle which consists of problem scoping, data acquisition, data exploration, modeling and evaluation. The amount of data in this study was 1213 comments with 381 surprised comments, 318 angry comments, 277 sad comments, 137 scared comments, and 100 happy comments. Happy and afraid comments have fewer comments than other emotions, so the imbalance dataset will be handled using SMOTE. The results of this study are to compare the classification results of the application of PSO and SMOTE to the Naive Bayes algorithm to determine the best model. Based on the classification results, the Naive Bayes, PSO, and SMOTE models produced the highest accuracy values of 76.48%, with a recall value of 76.47%, a precision of 76.20%, and a fmeasure of 75.62%.

Diagnosis of Heart Disease Using Optimized Naïve Bayes Algorithm with Particle Swarm Optimization and Gain Ratio

Abstract. 
 Purpose: This study aims to apply feature selection particle swarm optimization (PSO) and gain ratio to the naïve Bayes algorithm and gauging the level of accuracy before and after applying PSO feature selection and gain ratio to the naïve Bayes algorithm in the diagnosis of heart disease.Methods/Study design/approach: Data collection is done by using taking the Cleveland dataset obtained from the UCI machine learning repository. The data used in this study were 303 samples. The data is processed using the preprocessing stage. The naïve Bayes algorithm is used for a classifier, while PSO and gain ratio for feature selection.Result/Findings: The results of the study revealed that the classification accuracy of the naïve Bayes algorithm without the application of feature selection in the Cleveland dataset is 86.88%, while the results of the classification accuracy of the naïve Bayes algorithm after applying PSO and gain ratio in the Cleveland dataset is 93.44%. Application of PSO and gain ratio as feature selection algorithms can improve classification accuracy by 6.56%.Novelty/Originality/Value: This study combines the PSO feature selection and gain ratio on the naïve Bayes algorithm using the Cleveland dataset. The research model that was carried out was enriched by carrying out the preprocessing stages, namely data cleaning, changing the number of class labels, data normalization, and data discretization. This study shows that using a combination of the PSO feature selection algorithm and the gain ratio gives better accuracy to the naïve Bayes algorithm in diagnosing heart disease.

Implementation of Random Forest and Extreme Gradient Boosting in the Classification of Heart Disease using Particle Swarm Optimization Feature Selection

Heart disease is a condition that ranks as the primary cause of death worldwide. Based on available data, over 36 million people have succumbed to non-communicable diseases, and heart disease falls within the category of non-communicable diseases. This research employs a heart disease dataset from the UCI Repository, consisting of 303 instances and 14 categorical features. In this research, the data were analyzed using the classification methods XGBoost (Extreme Gradient Boosting) and Random Forest, which can be applied with PSO (Particle Swarm Optimization) as a feature selection technique to address the issue of irrelevant features. This issue can impact prediction performance on the heart disease dataset. From the results of the conducted research, the obtained values for the XGBoost (Extreme Gradient Boosting) model were 0.877, and for the Random Forest model, it was 0.874. On the other hand, in the model utilizing Particle Swarm Optimization (PSO), the obtained AUC values are 0.913 for XGBoost (Extreme Gradient Boosting) and 0.918 for Random Forest. These research results demonstrate that PSO (Particle Swarm Optimization) can enhance the AUC of heart disease prediction performance. Therefore, this research contributes to enhancing the precision and efficiency of heart disease patient data processing, which benefits heart disease diagnosis in terms of speed and accuracy.

Detection of Diabetes through Microarray Genes with Enhancement of Classifiers Performance.

Diabetes is a life-threatening, non-communicable disease. Diabetes mellitus is a prevalent chronic disease with a significant global impact. The timely detection of diabetes in patients is necessary for an effective treatment. The primary objective of this study is to propose a novel approach for identifying type II diabetes mellitus using microarray gene data. Specifically, our research focuses on the performance enhancement of methods for detecting diabetes. Four different Dimensionality Reduction techniques, Detrend Fluctuation Analysis (DFA), the Chi-square probability density function (Chi2pdf), the Firefly algorithm, and Cuckoo Search, are used to reduce high dimensional data. Metaheuristic algorithms like Particle Swarm Optimization (PSO) and Harmonic Search (HS) are used for feature selection. Seven classifiers, Non-Linear Regression (NLR), Linear Regression (LR), Logistics Regression (LoR), Gaussian Mixture Model (GMM), Bayesian Linear Discriminant Classifier (BLDC), Softmax Discriminant Classifier (SDC), and Support Vector Machine-Radial Basis Function (SVM-RBF), are utilized to classify the diabetic and non-diabetic classes. The classifiers' performances are analyzed through parameters such as accuracy, recall, precision, F1 score, error rate, Matthews Correlation Coefficient (MCC), Jaccard metric, and kappa. The SVM (RBF) classifier with the Chi2pdf Dimensionality Reduction technique with a PSO feature selection method attained a high accuracy of 91% with a Kappa of 0.7961, outperforming all of the other classifiers.

Evaluation and Exploration of Machine Learning and Convolutional Neural Network Classifiers in Detection of Lung Cancer from Microarray Gene-A Paradigm Shift.

Microarray gene expression-based detection and classification of medical conditions have been prominent in research studies over the past few decades. However, extracting relevant data from the high-volume microarray gene expression with inherent nonlinearity and inseparable noise components raises significant challenges during data classification and disease detection. The dataset used for the research is the Lung Harvard 2 Dataset (LH2) which consists of 150 Adenocarcinoma subjects and 31 Mesothelioma subjects. The paper proposes a two-level strategy involving feature extraction and selection methods before the classification step. The feature extraction step utilizes Short Term Fourier Transform (STFT), and the feature selection step employs Particle Swarm Optimization (PSO) and Harmonic Search (HS) metaheuristic methods. The classifiers employed are Nonlinear Regression, Gaussian Mixture Model, Softmax Discriminant, Naive Bayes, SVM (Linear), SVM (Polynomial), and SVM (RBF). The two-level extracted relevant features are compared with raw data classification results, including Convolutional Neural Network (CNN) methodology. Among the methods, STFT with PSO feature selection and SVM (RBF) classifier produced the highest accuracy of 94.47%.

Artificial intelligence for diagnosis of mild–moderate COVID-19 using haematological markers

Objective The persistent spread of SARS-CoV-2 makes diagnosis challenging because COVID-19 symptoms are hard to differentiate from those of other respiratory illnesses. The reverse transcription-polymerase chain reaction test is the current golden standard for diagnosing various respiratory diseases, including COVID-19. However, this standard diagnostic method is prone to erroneous and false negative results (10% -15%). Therefore, finding an alternative technique to validate the RT-PCR test is paramount. Artificial intelligence (AI) and machine learning (ML) applications are extensively used in medical research. Hence, this study focused on developing a decision support system using AI to diagnose mild-moderate COVID-19 from other similar diseases using demographic and clinical markers. Severe COVID-19 cases were not considered in this study since fatality rates have dropped considerably after introducing COVID-19 vaccines. Methods A custom stacked ensemble model consisting of various heterogeneous algorithms has been utilized for prediction. Four deep learning algorithms have also been tested and compared, such as one-dimensional convolutional neural networks, long short-term memory networks, deep neural networks and Residual Multi-Layer Perceptron. Five explainers, namely, Shapley Additive Values, Eli5, QLattice, Anchor and Local Interpretable Model-agnostic Explanations, have been utilized to interpret the predictions made by the classifiers. Results After using Pearson’s correlation and particle swarm optimization feature selection, the final stack obtained a maximum accuracy of 89%. The most important markers which were useful in COVID-19 diagnosis are Eosinophil, Albumin, T. Bilirubin, ALP, ALT, AST, HbA1c and TWBC. Conclusion The promising results suggest using this decision support system to diagnose COVID-19 from other similar respiratory illnesses.

Implementation of Particle Swarm Optimization Feature Selection on Naïve Bayes for Thoracic Surgery Classification

Thoracic surgery is among the operations that are most often performed on patients with lung cancer. Naive Bayes is one of the data mining classification techniques that may be used to handle thoracic surgery data. Therefore, the goal of this study is to assess the precision of all research models using Naive Bayes with and without Particle Swarm Optimization. This study's methodology includes the dataset used, the Naive Bayes algorithm theory, the particle swarm optimization algorithm, test validation using split validation, and performance assessment using the confusion matrix and AUC evaluation approaches. In this inquiry, secondary data are retrieved via the UCI Repository website. Thoracic surgery weight optimization accuracy is increased using particle swarm optimization. The test results of the Naive Bayes technique utilizing the thoracic surgery dataset showed the highest accuracy of 81.91% at a ratio of 80:20 and an AUC value of 0.620. The highest accuracy score is 93.62% with an AUC value of 0.773 at a ratio of 90:10, with three characteristics, namely PRE6, PRE14, and PRE17, having zero weight. This accuracy score was achieved when Particle Swarm Optimization was used to refine feature selection for attribute weighting. As a consequence, Naïve Bayes accuracy in thoracic surgery has increased as a result of attribute weighting on feature selection utilizing Particle Swarm Optimization. In turn, this research contributes to increasing the precision and efficiency with which thoracic surgical data are processed, which benefits lung cancer diagnosis in both speed and accuracy.

A novel hybrid autoencoder and modified particle swarm optimization feature selection for intrusion detection in the internet of things network

The Internet of Things (IoT) represents a paradigm shift in which the Internet is connected to real objects in a range of areas, including home automation, industrial processes, human health, and environmental monitoring. The global market for IoT devices is booming, and it is estimated that there will be 50 billion connected devices by the end of 2025. This explosion of IoT devices, which can be expanded more easily than desktop PCs, has led to an increase in cyber-attacks involving IoT devices. To address this issue, it is necessary to create novel approaches for identifying attacks launched by hacked IoT devices. Due to the possibility that these attacks would succeed, Intrusion Detection Systems (IDS) are required. IDS' feature selection stage is widely regarded as the most essential stage. This stage is extremely time-consuming and labor-intensive. However, numerous machine learning (ML) algorithms have been proposed to enhance this stage to boost an IDS's performance. These approaches, however, did not produce desirable results in terms of accuracy and detection rate (DR). In this paper, we propose a novel hybrid Autoencoder and Modified Particle Swarm Optimization (HAEMPSO) for feature selection and deep neural network (DNN) for classification. The PSO with modification of inertia weight was utilized to optimize the parameters of DNN. The experimental analysis was performed on two realistic UNSW-NB15 and BoT-IoT datasets that are suitable for IoT environment. The findings obtained by analyzing the proposed HAEMPSO against the Generic attack in the UNSW-NB15 dataset gave an accuracy of 98.8%, and a DR of 99.9%. While the benign class revealed an accuracy of 99.9% and DR of 99.7%. In the BoT-IoT dataset, the DDoS HTTP attack revealed an accuracy of 99.22% and DR of 97.79%. While the benign class gave an accuracy of 97.54% and DR of 97.92%. In comparison with the state-of-the-art machine learning schemes, our proposed HAEMPSO-DNN achieved a competitive feat in terms of DR and accuracy.

Comparison of Classification Algorithms toward Sentiment Analysis on Claim of Old Age Guaranteed

The pros and cons of the Old Age Security (JHT) claim rules contained in the Minister of Manpower Regulation No. 2 of 2022 are unavoidable in Indonesia, especially on Twitter social media. Of course, this is something that deserves to be analyzed as government policy makers so as not to make a wrong move, one of which is using sentiment analysis techniques. The stages of the research carried out include data collection, pre-processing, the model used, experimentation and model assessment, as well as validation and evaluation of results. The researcher crawled 1000 Indonesian-language tweets on the pros and cons of claims related to Old Age Security using the R programming language and carried out the Text Cleaning Process. In the next stage, pre-processing is carried out using Case Folding, Stemming, Tokenizing and Stopword Removal. This research resulted in the sentiments of the Indonesian people being very happy with the president's statement asking for the regulation of the Minister of Manpower No. 2 of 2022 to be revised, then tested using 3 classification algorithms and the application of Particle Swarm Optimization feature selection. The results of the sentiment testing of the JHT benefit claim analysis obtained the highest value on the Support Vector Machine algorithm with the addition of Particle Swarm Optimization, namely the accuracy value of 82.92%, precision of 84.53%, recall of 80.90% and AUC value of 0.892.. Keywords: Support Vector Machine, Sentiment Analysis, Old Age Guarantee d sar 0.892

Analisis Sentimen Masyarakat Indonesia Dalam Konflik Rusia-Ukraina Di Twitter

Russia is a big superpower that has power and plays an important role in international politics, while Ukraine, a former Soviet Union country, became independent on December 1, 1991. In 2014 there was also a conflict between Russia and Ukraine which was a meeting of superpowers. In February 2022, Russia resumed armed conflict with Ukraine. The Russia-Ukraine conflict has garnered many responses in the form of tweets from various circles of society, resulting in many traces of tweets containing public opinion on the Russia- Ukraine conflict on Twitter social media. This study aims to determine the results of the positive or negative impact of the conflict between Russia and Ukraine on the economy in Indonesia and to determine the results of accuracy, precision, recall resulting from the use of the Naïve Bayes method and feature selection Particle Swarm Optimization in RapidMiner Studio software. Particle Swarm Optimization is an optimization method inspired by the behavior of fish and poultry flocks in search of food sources. The preprocessing stage in this research includes cleansing, removing duplicates, data selection, normalization, case folding, tokenizing, filtering, stopwords, stemming, and labeling. The classification results obtained by 55.11% of twitter users commented negatively and 44.89% of twitter users commented positively about the conflict between Russia and Ukraine. By looking at the results of the sentiment analysis data above, where the number of Twitter users who commented negatively is higher, it can be concluded that the Indonesian people are worried about the surge in prices of basic daily necessities, as indicated by one of the tweets commenting on rising oil and gas prices BBM