Sigmoid Kernel Research Articles

Performance Analysis of Neighborhood Component Analysis on Support Vector Machine in Greenhouse Gas Emission Classification

The heatwave phenomenon has hit several countries in various parts of the world, caused by climate change. Climate change leads to greenhouse gas emissions increasing beyond the limits set by the Intergovernmental Panel on Climate Change (IPCC) Fourth Assessment Report Global Warming Potentials. This final project uses a combination of Neighborhood Component Analysis (NCA) and Support Vector Machine (SVM) methods with linear, polynomial, Radial Basis Function (RBF), and sigmoid kernel functions. The purposes of this final project are to evaluate the performance of NCA on SVM and to determine the best kernel function in this combination. Based on the analysis, it was found that classification using a combination of NCA and SVM methods can reduce variables, with the best kernel function being the Polynomial kernel function. This is because the analysis using the Polynomial kernel function achieved the highest accuracy values for training data, testing accuracy, and F1-Score, which are 98,96%, 99,15%, and 98,98% respectively. Additionally, the training analysis time and testing analysis time were the shortest at 0,15 seconds and 0,04 seconds.

Evaluation of geological hazard susceptibility in Xixia Xichuan area based on support vector machine

Abstract Geological hazards seriously affect the personal and property safety of regional residents, in the Xichuan area, a real-time monitoring and early warning system could have provided timely alerts before the landslide, potentially averting the tragic loss of life and destruction.So it is very important to carry out geological hazard susceptibility assessment and hazard prevention. The Xixia area of Xichuan is selected as the study area, and the support vector machine model is used to evaluate the susceptibility of geological hazards in the study area. Through Kendall factor correlation analysis, the susceptibility evaluation indexes of the study area are selected, the evaluation index system of the study area is established, and the thematic layers and attribute data of the evaluation indexes are extracted through GIS technology. The MATLAB platform was used to establish linear kernel function (LN-SVM), polynomial kernel function (PL-SVM), radial basis kernel function (RBF-SVM), sigmoid kernel function (Sigmoid-SVM) based on the four support vector machine models of the study area were used to evaluate and classify the susceptibility of geological hazards. ROC curves were plotted to check the accuracy of the evaluation results of the four kernel functions, and the most suitable kernel function model for the study area was selected. The results show that the RBF-SVM model has high evaluation accuracy and is most suitable for the evaluation of geological hazard susceptibility in the study area. Finally, GIS technology was applied to partition the evaluation results into susceptibility zones. The evaluation results show that 86.14% of the geological hazards in Xixia Xichuan area are of medium and high susceptibility, and the evaluation results have certain practical significance for the prevention and control of geological hazards in Xixia Xichuan area.

SKT-IDS: Unknown attack detection method based on Sigmoid Kernel Transformation and encoder–decoder architecture

Intrusion Detection Systems (IDS) are crucial in cybersecurity for monitoring network traffic and identifying potential attacks. Existing IDS research largely focuses on known attack detection, leaving a significant gap in research regarding unknown attack detection, where achieving a balance between false alarm rate (identifying normal traffic as attack traffic) and recall rate of unknown attack detection remains challenging. To address these gaps, we propose a novel IDS based on Sigmoid Kernel Transformation and Encoder-Decoder architecture, namely SKT-IDS, where SKT stands for Sigmoid Kernel Transformation. We start with pre-training an attention-based encoder for coarse-grained intrusion detection. Then, we use this encoder to build an encoder–decoder model specifically for 0-day attack detection, training it solely on known traffic using the cosine similarity loss function. To enhance detection, we introduce a Sigmoid Kernel Transformation for feature engineering, improving the discriminative ability between normal traffic and 0-day attacks. Finally, we conducted a series of ablation and comparative experiments on the NSL-KDD and CSE-CIC-IDS2018 datasets, confirming the effectiveness of our proposed method. With a false alarm rate of 1%, we achieved recall rates for unknown attack detection of 65% and 69% on the two datasets, respectively, demonstrating significant performance improvements compared to existing state-of-the-art models.

Revealing the Impact of the Combination of Parameters on SVM Performance in COVID-19 Classification

Non-linear SVM functions to modify the kernel in the SVM. Each kernel function in linear and non-linear SVMs has several parameters that are used in the classification process. SVM is a method that has advantages in classification, but there are still obstacles in selecting optimal parameters. This research investigates the effect of parameter variations on SVM classification performance on the COVID-19 dataset, using linear, RBF, Sigmoid and polynomial kernels. The analysis shows that the polynomial kernel is superior with the highest performance compared to other kernels. The highest accuracy of 77.57% was achieved with a combination of C values ??of 0.75 and Gamma of 0.75, and an F1-Score value of 76.67% indicating an optimal balance between precision and recall. The performance stability produced by the polynomial kernel provides advantages in classifying the COVID-19 dataset, with more controlled fluctuations compared to other kernels. The interaction between the C and Gamma parameters shows that a Gamma value of 0.75 consistently provides good results, while adjusting the C parameter shows more controlled performance variations. This confirms that appropriate Gamma parameter settings are key in improving the accuracy and consistency of SVM model predictions in this case.

Enhancing microalgae classification accuracy in marine ecosystems through convolutional neural networks and support vector machines

Accurately classifying microalgae species is vital for monitoring marine ecosystems and managing the emergence of marine mucilage, which is crucial for monitoring mucilage phenomena in marine environments. Traditional methods have been inadequate due to time-consuming processes and the need for expert knowledge. The purpose of this article is to employ convolutional neural networks (CNNs) and support vector machines (SVMs) to improve classification accuracy and efficiency. By employing advanced computational techniques, including MobileNet and GoogleNet models, alongside SVM classification, the study demonstrates significant advancements over conventional identification methods. In the classification of a dataset consisting of 7820 images using four different SVM kernel functions, the linear kernel achieved the highest success rate at 98.79 %. It is followed by the RBF kernel at 98.73 %, the polynomial kernel at 97.84 %, and the sigmoid kernel at 97.20 %. This research not only provides a methodological framework for future studies in marine biodiversity monitoring but also highlights the potential for real-time applications in ecological conservation and understanding mucilage dynamics amidst climate change and environmental pollution.

Sigmoid distance metric-based spline adaptive filters for nonlinear adaptive noise cancellation

This article devises a spline adaptive filter (SAF) algorithm with robustness for nonlinear adaptive noise cancellation (NANC), called the SAF-SDM, which is built on a distance-based metric with a sigmoid kernel. The presented SAF-SDM delivers insignificant coefficient updates when the system is subjected to remarkable outliers, which leads to low steady-state errors. Additionally, we thoroughly analyze the mean behaviour and mean-square performance of the SAF-SDM at the theoretical level and validate the analysis results using nonlinear system identification (NSI) simulations. To fulfill the higher computational efficiency when a longer linear filter order is selected in the SAF, an improved SAF-SDM is developed through a frequency domain (FD) filtering program, called the FDSAF-SDM. Then, we provide an analysis of the calculation complexity of the two presented algorithms and compare them with those of several similar algorithms. Finally, simulation and experiment results demonstrate the excellence of the presented algorithms in NSI and NANC.

Firefly Algorithm for SVM Multi-class Optimization on Soybean Land Suitability Analysis

Soybean is the primary source of vegetable protein nutrition, containing fat and vitamins that Indonesian people widely consume. The decline in soybean production in Indonesia every year is due to the reduced area of soybean cultivation, thereby increasing dependence on imports from other countries. Land suitability maps can provide directions for priority locations for soybean cultivation based on land characteristics and weather to produce optimal production. The SVM multi-class algorithm has been applied to classify land suitability data to create a land suitability map but has yet to obtain optimal accuracy, especially for sigmoid kernels. The objective of this study is to enhance the performance of the sigmoid kernel SVM by utilizing the firefly algorithm. The study focuses on evaluating the suitability of soybean cultivation in Bogor and Grobogan Regencies. The results of the tests indicate that the firefly algorithm-optimized SVM (FA-SVM) significantly improves accuracy compared to the SVM without optimization. The accuracy achieved by FA-SVM is 89.95%, while the SVM without optimization only achieves an accuracy of 65.99%. The best parameters produced by the firefly algorithm are C=2.33 and σ=0.45 obtained from firefly customization, and the number of generations is 10. Based on this, the optimization algorithm can be used to produce an optimal model. The best optimal model obtained can be used as a guide for priority locations/areas for soybean cultivation by farming communities, so as to produce maximum soybean productivity.

Predicting anti-trypanosome effect of carbazole-derived compounds by powerful SVM with novel kernel function and comprehensive learning PSO.

In order to predict the anti-trypanosome effect of carbazole-derived compounds by quantitative structure-activity relationship, five models were established by the linear method, random forest, radial basis kernel function support vector machine, linear combination mix-kernel function support vector machine, and nonlinear combination mix-kernel function support vector machine (NLMIX-SVM). The heuristic method and optimized CatBoost were used to select two different key descriptor sets for building linear and nonlinear models, respectively. Hyperparameters in all nonlinear models were optimized by comprehensive learning particle swarm optimization with low complexity and fast convergence. Furthermore, the models' robustness and reliability underwent rigorous assessment using fivefold and leave-one-out cross-validation, y-randomization, and statistics including concordance correlation coefficient (CCC), [Formula: see text] , [Formula: see text] , and [Formula: see text] . Among all the models, the NLMIX-SVM model, which was established by support vector regression using a nonlinear combination of radial basis kernel function, sigmoid kernel function, and linear kernel function as a new kernel function, demonstrated excellent learning and generalization abilities as well as robustness: [Formula: see text] = 0.9581, mean square error (MSE) = 0.0199 for the training set and [Formula: see text] = 0.9528, MSE = 0.0174 for the test set. [Formula: see text] , [Formula: see text] , CCC, [Formula: see text] , [Formula: see text], and [Formula: see text] are 0.9539, 0.8908, 0.9752, 0.9529, 0.9528, and 0.9633, respectively. The NLMIX-SVM method proved to be a promising way in quantitative structure-activity relationship research. In addition, molecular docking experiments were conducted to analyze the properties of new derivatives, and a new potential candidate drug molecule was ultimately found. In summary, this study will provide help for the design and screening of novel anti-trypanosome drugs.

Machine learning-based electricity theft detection using support vector machines

Electricity theft is a serious issue that many nations face, especially in developing areas where non-technical losses can make up a significant percentage of the overall losses sustained by utilities. Electricity theft detection (ETD) is a very challenging task because it frequently introduces irregularities in customer electricity consumption patterns. In recent times, machine learning (ML) techniques have been investigated as a potential solution for ETD. In this research, author propose electricity theft detection based on four kernel functions of support vector machines (SVM). The proposed method analyzes the electricity consumption patterns and then predicts the category of the user. The kernel functions utilized includes polynomial, sigmoid, radial basis function (RBF) and linear kernel function. For experimentation and model training, a dataset of Pakistani utility company is used, which contains the electricity consumption information. The results highlight SVM method works well for accurate ETD. The detection accuracy of the various kernel functions of SVM is 83%, 79%, 80%, and 76% for RBF, polynomial, sigmoid, and linear kernel functions, respectively, demonstrating the effectiveness of the proposed SVM-based method for theft detection. By leveraging these ML-based methods, utility companies can strengthen their ability to detect and prevent electricity theft, leading to improved revenue management and dependability of services.

SPATIAL PREDICTION OF RECEIVED SIGNAL STRENGTH FOR CELLULAR COMMUNICATION USING SUPPORT VECTOR MACHINE AND K-NEAREST NEIGHBOURS REGRESSION

Abstract. Signal strength maps are of great importance for cellular system providers in network planning and operation. Accurate prediction of signal strength is important for solving problems such as link quality. In this study, Received Signal Strength (RSS) prediction model is proposed for the 900 MHz band in the Van Yüzüncü Yıl University campus environment by using machine learning regression methods such as K- Nearest Neıghbours (KNN) and Support Vector Regression (SVR) together with Geographic Information Systems. For the training of this model, signal strength values taken from the RF Spectrum Analyser at different locations and distances were used. In addition, spatial data sets such as the digital elevation model, location of base stations and measurement stations, building heights and location, and land use/cover were used in the model. The effect of these data sets on RSS power is included in the model. The model aims to predict RSS accurately, visualize the estimated signal strength, and analyze the signal field strength coverage. Different kernels from the SVR model such as Polynomial, , and Sigmoid were tested. To increase the success of the model, appropriate parameter values were selected and configured according to SVR and KNN methods. For 900 MHz, the performances of SVR and KNN models were compared and the results of the models were verified using root mean squares (RMSE). Among the measured data, the lowest prediction is found in KNN Manhattan. According to the results of the simulation was observed that the SVR model created with spatial data performs better for Signal Strength. Finally, the lowest RMSE value (1.71 dB) was obtained from the Sigmoid kernel in the best signal strength estimation SVR model. The SVR model is recommended for Campus Area signal strength estimation.

Construction and validation of m6A-related diagnostic model for psoriasis.

Psoriasis is a chronic immune-mediated inflammatory disease. N6-methyladenosine (m6A) is involved in numerous biological processes in both normal and diseased states. Herein, we aimed to explore the potential role of m6A regulators in the diagnosis of psoriasis and predict molecular mechanisms by which m6A regulators impact psoriasis. GSE30999 (170 human skin tissue samples) and GSE13355 (180 human skin tissue samples) were downloaded as the training analysis dataset and validation dataset respectively. M6A-related genes were obtained from the literature and their expression levels in GSE30999 samples were measured to identify M6A-related DEGs between psoriasis lesions (LS) and non-lesional lesions (NL). We identified m6A-related DEGs using differential expression analysis and assessed their interactions through correlation analysis and network construction. A logistic regression analysis followed by LASSO optimization was employed to select m6A-related DEGs for the construction of a diagnostic model. The performance of the model was validated using support vector machine (SVM) methodology with sigmoid kernel function and extensive cross-validation. Additionally, the correlation between m6A-related DEGs and immune cell infiltration was analyzed, as well as the association of these DEGs with psoriasis subtypes. Functional analysis of the m6A-related DEGs included the construction of regulatory networks involving miRNAs, transcription factors (TFs), and small-molecule drugs. The m6A modification patterns were also explored by examining the gene expression differences between psoriasis subtypes and their enriched biological pathways. Finally, the expression of significant m6A regulators involved in the diagnostic model was examined by RT-qPCR. In this study, ten optimal m6A-related DEGs were identified, including FTO, IGF2BP2, METTL3, YTHDC1, ZC3H13, HNRNPC, IGF2BP3, LRPPRC, YTHDC2, and HNRNPA2B1. A diagnostic model based on these m6A-related DEGs was constructed, demonstrating high diagnostic accuracy with an area under the curve (AUC) in GSE30999 and GSE13355 of 0.974 and 0.730, respectively. Meanwhile, the expression level of m6A regulators verified by RT-qPCR was consistent with the results in GSE30999. The infiltration of activated mast cells and NK cells was significantly associated with all ten m6A-related DEGs in psoriasis. Among them, YTHDC1, HNRNPC, and FTO were targeted by most miRNAs and were regulated by nine related TFs. Therefore, patients may benefit from dorsomorphin and cyclosporine therapy. Between the two subgroups, 1,592 DEGs were identified, including LRPPRC and METTL3. These DEGs were predicted to be involved in neutrophil activation, cytokine-cytokine receptor interactions, and chemokine signaling pathways. A diagnostic model based on ten m6A-related DEGs in patients with psoriasis was constructed, which may provide early diagnostic biomarkers and therapeutic targets for psoriasis.

Experimental investigation and application of soft computing models for predicting flow energy loss in arc-shaped constrictions

Abstract This investigation focuses on flow energy, a crucial parameter in the design of water structures such as channels. The research endeavors to explore the relative energy loss (ΔEAB/EA) in a constricted flow path of varying widths, employing Support Vector Machine (SVM), Artificial Neural Network (ANN), Gene Expression Programming (GEP), Multiple Adaptive Regression Splines (MARS), M5 and Random Forest (RF) models. Experiments span a Froude number range from 2.85 to 8.85. The experimental findings indicate that the ΔEAB/EA exceeds that observed in a classical hydraulic jump with constriction section. Within the SVM model, the linear kernel emerges as the best predictor of ΔEAB/EA, outperforming polynomial, radial basis function (RBF), and sigmoid kernels. In addition, in the ANN model, the MLP network was more accurate compared to the RBF network. The results indicate that the relationship proposed by the MARS model can play a significant role resulting in high accuracy compared to the non-linear regression relationship in predicting the target parameter. Upon comprehensive evaluation, the ANN method emerges as the most promising among the candidates, yielding superior performance compared to the other models. The testing phase results for the ANN-MLP are noteworthy, with R = 0.997, average RE% = 0.63%, RMSE = 0.0069, BIAS = −0.0004, DR = 0.999, SI = 0.0098 and KGE = 0.995.

Detection of oral mucosal lesions by the fluorescence spectroscopy and classification of cancerous stages by support vector machine.

Detection of oral mucosal lesions has been performed by an in-house developed fluorescence-based portable device in the present study. A laser diode of 405 nm wavelength and a UV-visible spectrometer are utilized in the portable device as excitation and detection sources. At the 405 nm excitation wavelength, the flavin adenine dinucleotide (FAD) band at 500 nm and three porphyrin bands at 634, 676, and 703 nm are observed in the fluorescence spectrum of the oral cavity tissue. We have conducted this clinical study on a total of 189 tissue sites of 36 oral squamous cell carcinoma (OSCC) patients, 18 dysplastic (precancerous) patients, and 34 volunteers. Analysis of the fluorescence data has been performed by using the principal component analysis (PCA) method and support vector machine (SVM) classifier. PCA is applied first in the spectral data to reduce the dimension, and then classification among the three groups has been executed by employing the SVM. The SVM classifier includes linear, radial basis function (RBF), polynomial, and sigmoid kernels, and their classification efficacies are computed. Linear and RBF kernels on the testing data sets differentiated OSCC and dysplasia to normal with an accuracy of 100% and OSCC to dysplasia with an accuracy of 95% and 97%, respectively. Polynomial and sigmoid kernels showed less accuracy values among the groups ranging from 48 to 88% and 51 to 100%, respectively. The result indicates that fluorescence spectroscopy and the SVM classifier can help to identify early oral mucosal lesions with significant high accuracy.

Advanced EEG-Based Classification of Periodic Leg Movements and Bruxism Using Complexity and Entropy Features

Periodic leg movements (PLM) and bruxism are prevalent sleep disorders that significantly impact sleep quality and overall health. Accurate and timely detection of these disorders is pivotal for effective treatment and improved patient well-being. In this research paper, we present a novel approach for the identification of Periodic Leg Movements (PLM) and bruxism. Our methodology leverages sophisticated complexity and entropy features extracted from electroencephalogram (EEG) signals recorded across various sleep stages. The implementation involves a diverse set of artificial intelligence (AI) classifiers, encompassing K-nearest neighbours (KNN), artificial neural networks, support vector machines (SVM) with radial basis function, SVM with polynomial kernel, and SVM with sigmoid kernel. These classifiers are employed to categorize EEG signals into three distinct classes: PLM, bruxism, or normal sleep patterns. To underscore the efficacy and superiority of our proposed approach, we conduct a thorough comparison with existing publications in the field. Our approach consistently outperforms previous methods and attains remarkable accuracies of 98.74% for PLM and 94.74% for bruxism, particularly when utilizing the SVM Sigmoid classifier. Furthermore, our study highlights the potential of EEG signals in elucidating alterations in brain activity associated with PLM and bruxism, underscoring the reliability of complexity and entropy features as robust biomarkers for these sleep disorders. Our research significantly advances the contemporary pinnacle in EEG-based detection of Periodic Leg Movements (PLM) and bruxism signifies a significant advancement, holding potential for impactful clinical applications in the realm of sleep disorder diagnosis and treatment.

Predicting Nitrate Levels in the Saïss Water Table: A Comparative Study of Machine Learning Methods

The main goal of this study is to predict nitrate (NO3-) levels in the Saiss basin water table as a function of various physicochemical parameters. To accomplish this, three machine learning approaches were utilized: multiple linear regression (MLR), super vector regression (SVR), and artificial neural networks (ANN). The independent variables were composed of six water quality parameters, including Ca2+, Na2+, EC, Cl-, HCO3-, and SO42-. The study utilized a dataset of 389 water samples collected between 1991 and 2017. The artificial neural network (ANN) was trained using the Levenberg-Marquardt (LM) algorithm, which was selected from various optimization algorithms. Additionally, during the training of the SVR model, it was observed that the RBF kernel outperformed the other kernels (linear, polynomial, and sigmoid kernel). The results were analyzed by the coefficient of determination (R2) and the mean square error (MSE). The results of the MLR method revealed R2 (0.523) and MSE (757.34). The ANN model with architecture [6-20-1] performed better than RLM with R2 = 0.836, MSE= 0.023 The SVR model result confirms what has been proved by ANN concerning the performance, with R2=0.902 and MSE= 4,364.

Securing Cyberspace: Exploring the Efficacy of SVM (Poly, Sigmoid) and ANN in Malware Analysis

This study presents a comprehensive exploration and comparative analysis of three prominent classification algorithms—Support Vector Machine (SVM) with polynomial and sigmoid kernels, and Artificial Neural Network (ANN)—in the context of malware classification. Leveraging a dataset comprising 5184 samples, including both malware and benign instances, the research systematically evaluates the performance of these algorithms using key metrics such as accuracy, precision, recall, F1 score, and AUC-ROC. The SVM classifier with a polynomial kernel emerges as the top performer, achieving remarkable accuracy (98.08%), precision (98.56%), and recall (97.85%). Its capacity to minimize false positives while maintaining a high true positive rate positions it as a robust tool for accurate malware identification. The sigmoid kernel SVM demonstrates a well-balanced performance, suitable for scenarios requiring a nuanced trade-off between false positives and false negatives. The ANN model, while exhibiting a lower overall accuracy (89.00%), excels in recall (92.61%), showcasing its proficiency in capturing instances of malware. The study underscores the significance of selecting an algorithm aligned with specific application requirements, whether prioritizing precision, recall, or a balanced approach. Furthermore, the research acknowledges the dataset's limitations and calls for future exploration with diverse datasets and additional preprocessing techniques. As cybersecurity threats evolve, the insights provided by this study contribute to the ongoing discourse on developing robust tools for effective malware detection. The findings empower cybersecurity professionals and researchers with valuable considerations for selecting the most suitable classification algorithm in the dynamic landscape of digital security.

ScLSTM: single-cell type detection by siamese recurrent network and hierarchical clustering

MotivationCategorizing cells into distinct types can shed light on biological tissue functions and interactions, and uncover specific mechanisms under pathological conditions. Since gene expression throughout a population of cells is averaged out by conventional sequencing techniques, it is challenging to distinguish between different cell types. The accumulation of single-cell RNA sequencing (scRNA-seq) data provides the foundation for a more precise classification of cell types. It is crucial building a high-accuracy clustering approach to categorize cell types since the imbalance of cell types and differences in the distribution of scRNA-seq data affect single-cell clustering and visualization outcomes.ResultTo achieve single-cell type detection, we propose a meta-learning-based single-cell clustering model called ScLSTM. Specifically, ScLSTM transforms the single-cell type detection problem into a hierarchical classification problem based on feature extraction by the siamese long-short term memory (LSTM) network. The similarity matrix derived from the improved sigmoid kernel is mapped to the siamese LSTM feature space to analyze the differences between cells. ScLSTM demonstrated superior classification performance on 8 scRNA-seq data sets of different platforms, species, and tissues. Further quantitative analysis and visualization of the human breast cancer data set validated the superiority and capability of ScLSTM in recognizing cell types.

Klasifikasi Penyakit Tanaman Bawang Merah Menggunakan Metode SVM dan CNN

Shallots are one of the most widely produced crops in Enrekang Regency. The obstacle in cultivation is the presence of disease in the plant which can reduce production yields. We can recognize this disease from the spots on the leaves because these spots have unique color and texture characteristics. The aim of this research is to determine the results of the classification of shallot plant diseases which focuses on purple spot and moler disease. The classification algorithms used are CNN and SVM with RBF, linear, sigmoid and polynomial kernels. The feature extraction method used is Gray Level Co-occurance Matrix (GLCM). The analysis was carried out using 320 datasets with 2 classes, namely, purple spot disease and moler disease, each class has 160 datasets. The test results show that the CNN and SVM methods with RBF, linear and polynomial kernels get accuracy, precision, recall and F1 scores of 100% respectively. Meanwhile, the SVM method on the sigmoid kernel using texture feature extraction with the GLCM method states that the accuracy value is 75%, precision 75%, recall 73% and F1-Score 74%. So these results state that the Sigmoid method using GLCM feature extraction has the lowest value among other methods

Uji Kernel SVM dalam Analisis Sentimen Terhadap Layanan Telkomsel di Media Sosial Twitter

Telkomsel is an internet service provider in Indonesia which was launched in 1995. As an internet 
 service provider with the most users, Telkomsel has become the center of attention of internet users in Indonesia. This 
 invites user opinions and perspectives on Telkomsel, which is commonly referred to as sentiment. One of the media 
 commonly used to express an opinion and point of view is Twitter. Twitter is a social media platform that is often a 
 place for sharing and spreading the news, and discussing ideas, and opinions of Twitter users. In this study, the algorithm used 
 is the Support Vector Machine. In the Support Vector Machine, there is a kernel trick that will be used to determine 
 kernel performance and analyze sentiment. The sentiments analyzed amounted to 537 tweets collected by scraping. 
 The collected tweets will go through the preprocessing stage, namely cleaning, case folding, tokenizing, normalization, 
 stemming, stopword removal, and detokenizing. A sentiment is classified into 2 labels, namely positive and negative. 
 Based on the test results, the sigmoid kernel has the best performance with an accuracy value of 0.950, a precision of 
 0.945, a recall of 0.860, an f1-score of 0.896, and sentiment tend toward negative.

Economic Impact due Covid-19 Pandemic: Sentiment Analysis on Twitter Using Naïve Bayes Classifier and Support Vector Machine

Covid-19 is an outbreak caused by severe acute respiratory syndrome. Covid-19 first appeared in Indonesia on March 2, 2020, with two confirmed cases and increased to 1285 cases in 30 provinces. One of the impacts of the Covid-19 pandemic is on the economic aspect, which has experienced a drastic decline in income. This study aims to classify public opinion to determine the level of public sentiment on the economic impact of the Covid-19 pandemic and to identify parameters that influence the accuracy of the sentiment analysis classification model. The methods used in this current research are Lexicon, Support Vector Machine (SVM), and Naive Bayes Classifier (NBC). First, Lexicon is used for scoring and labeling the preprocessed data. Second, SVM is used to classify the sentiment, then find the best accuracy using linear, radial, polynomial, and sigmoid kernels. Third, NBC is used to classify sentiment as a comparison method. The results indicated that 255 tweet data consisted of 44 positive tweets (17.25%), 46 neutral tweets (18.04%), and 165 negative tweets (64.71%). Therefore, it can be inferred that the economic impact on the Indonesian people due to the Covid-19 pandemic has a high negative sentiment value. In the performance, SVM yielded a better accuracy of 100%, precision, recall, and F-measure are 1. This study proves that selecting the kernel type and applying underfitting can improve the accuracy of SVM. Also, SVM can perform well on a small amount of training data.