Practical Benefits Research Articles

Investigating the impact of discretization techniques on real-time digital control of DC-DC boost converters: A comprehensive analysis

This paper provides a thorough comparative analysis of discretization techniques commonly utilized in digital control applications for DC-DC boost converters (DBCs). Given the crucial role of DC-DC converters in various industries such as renewable energy systems, electric vehicles, and portable electronic devices, it is imperative to optimize their performance through efficient digital control. This study aims to improve the understanding of the effects of discretization methods on control system behaviour. It investigates their impact on the performance, stability and accuracy of control algorithms in the context of DBC, using dSPACE real-time interface (RTI) and hardware-in-the-loop (HIL) simulation for validation. This approach replicates real-world conditions, allowing performance to be evaluated in a controlled environment. The analysis examines behaviour in the time and frequency domains, providing insight into the strengths and weaknesses of each method. Experimental validation using MATLAB/Simulink/Stateflow on dSPACE RTI 1007 processor, DS2004 high-speed A/D and CP4002 timing and digital I/O boards ensures that the comparative analysis provides practical benefits for DBC digital control applications.

A robust LightGBM model for concrete tensile strength forecast to aid in resilience-based structure strategies

The tensile strength (TS) of concrete is a factor in the design of civil engineering structures. Traditional methods for assessing concrete TS have notable limitations, leading to the emergence of non-destructive testing (NDT) as an innovative alternative. This study introduces a novel, optimized approach for accurate, non-destructive TS prediction by integrating Gradio, Bayesian optimization (BO), and Shapley additive explanations (SHAP). The input parameters for the model were ultrasonic pulse velocity (UPV) and electrical resistivity (ER). Using a comprehensive dataset of 3460 datapoints, a light gradient-boosting machine (LightGBM) model was developed. Through extensive hyperparameter tuning facilitated by BO, the model achieved a robust prediction accuracy of approximately 98 %. The novel application of SHAP in this study provided deep insights into model behavior, confirming UPV as the most critical variable for TS prediction. The deployment of the model using Gradio further enhances its accessibility and usability. This combined methodology not only advances the application of machine learning (ML) in civil engineering but also offers a powerful, globally applicable tool for accurately forecasting concrete TS. This supports resilience-based management strategies, essential for the sustainable development of infrastructure. The study's novel integration of BO for optimization and SHAP for interpretability marks a significant step forward in the precise prediction of TS, offering practical benefits for both researchers and industry professionals.

Increasing access to biomarker-driven treatment for lung cancer in Alabama: The Alabama Access initiative.

159 Background: Lung cancer is the leading cause of cancer death in Alabama, with an estimated 2,550 deaths in 2024. Non-small cell lung cancer (NSCLC) represents 85% of these cases. Despite national guidelines recommending comprehensive biomarker testing (CBT) for metastatic NSCLC, significant disparities persist in the utilization of CBT and targeted therapies among Alabama residents. Methods: In March 2024, we launched the "Alabama Access" initiative – a partnership between the LUNGevity Foundation and the O’Neal Comprehensive Cancer Center at the University of Alabama at Birmingham – to foster academic and community oncology partnerships to increase access to biomarker-driven treatments. We used a population health science and geospatial mapping approach to visualize oncology practices serving counties with Alabama's highest lung cancer burden. Practices in Walker, Dothan, Marengo, Morgan, Jackson, Marshall, and Montgomery counties were identified as the highest priority based on annual mortality rates. Key interventions will include virtual molecular tumor boards and oncology navigation to facilitate the integration of resources and expertise and ensure that patients in nonacademic practices benefit from the latest advances in precision oncology. After roll-out, we will collect feasibility and acceptability data around program integration. Results: Our analysis revealed that 62.0% of the state’s population resides in Primary Care Health Professional Shortage Areas (HPSAs), and 58 out of 67 counties are classified as Medically Underserved Areas. The poverty rate in 89.6% of counties surpasses the national average, contributing to higher lung cancer incidence and mortality rates, with 62.0% of cases diagnosed at advanced stages. The state also shows a higher smoking prevalence, affecting 97.0% of counties compared to national figures. We will present practice-level testing data, including barriers to CBT. Conclusions: This pilot aims to mitigate disparities in CBT through enhanced collaboration between academic and community oncology settings. Integrating molecular navigation into community practices will be pivotal in disseminating program information and collecting data on current testing practices to improve overall lung cancer care quality in Alabama.

Customer Acceptance for Self-Service Technologies in Malaysian Fast-Food Restaurants: An Empirical Study

Recently, the fast-food industry has witnessed an increase in adopting self-service technologies (SSTs) intended to offer customers increased convenience and efficiency. This study seeks to find out the factors influencing customer acceptance of SST in fast food restaurants in Melaka, Malaysia, using the Technology Acceptance Model. For that purpose, a structured questionnaire was presented to 350 customers of fast food and this data was analyzed by descriptive and inferential statistics. A test of the hypotheses revealed that perceived ease of use (PEOU) and perceived usefulness (PU) are highly significant predictors of customers' attitudes toward using SSTs (ATU). More specifically, PU indicates stronger effects on acceptance such that customers are more willing to use SSTs when obvious practical benefits would be achieved from them regarding faster services and greater accuracy in placing orders. The findings also indicate that younger, highly educated customers with previous experience with SSTs are more likely to accept this innovation. These insights go a long way in helping the operator of fast food to design a better customer experience using technology-driven solutions. This covers the improvement of user interfaces, the emphasis on the practical benefits of SSTs, and tailoring marketing strategies to diverse customer segments.

End-to-end [18F]PSMA-1007 PET/CT radiomics-based pipeline for predicting ISUP grade group in prostate cancer.

To develop an end-to-end radiomics-based pipeline for the prediction of International Society of Urological Pathology grade group (ISUP GG) in prostate cancer (PCa). This retrospective study includes 356 patients (241 in training set and 115 in independent test set) with histopathologically confirmed PCa who underwent [18F]PSMA-1007 PET/CT scan. Patients were classified into two groups according to their ISUP GG (1-3 vs. 4-5). Radiomics features were extracted from the whole, automatically segmented prostate on PET/CT images, 30 models were constructed by combining 6 feature selection algorithms and 5 machine learning classifiers. The clinical model incorporated age, total prostate-specific antigen (tPSA), maximum standardized uptake value (SUVmax), and prostate volume. The predictive performance of the models was evaluated using the area under the receiver operating characteristic curve (AUC), balanced accuracy (bAcc), and decision curve analysis (DCA). The best-performing radiomics model significantly outperformed clinical model (AUC 0.879 ± 0.041 vs. 0.799 ± 0.051, bAcc 0.745 ± 0.074 vs. 0.629 ± 0.045). On an external independent test set, best-performing radiomics model perform better than clinical model, with an AUC of 0.861 vs. 0.750, p = 0.002 (Delong), and bAcc of 0.764 vs. 0.582, p = 0.043 (McNemar). The learning curve, calibration curve and DCA demonstrated goodness-of-fit and improved benefits in clinical practice. The end-to-end radiomics-based pipeline is an effective non-invasive tool to predict ISUP GG in PCa.

Solusi Al-Qur’an dalam Mengatasi Pengaruh Media Sosial TikTok Terhadap Keagamaan Generasi Muda

This research aims to determine the influence of TikTok social media on the religion of the younger generation from the perspective of the Koran. This research was conducted using the library research method, which includes analysis of literature such as the Al-Qur'an, hadith, journals, and related books. In the digital era, social media has become an important platform for the younger generation to express themselves and seek information. TikTok, as a popular application, allows users to create short videos with various interesting effects. However, its inappropriate use can have negative impacts, such as reducing valuable time, reducing a culture of shame, and spreading hoax news. This research found that TikTok has a significant effect on the religion of the younger generation, including the potential for antisocial behavior, wasting time, and decreasing Islamic values. To overcome these negative influences, the Qur'an provides important guidance, including commands to socialize well, make the best use of time, and maintain a culture of shame. This article offers a unique perspective on how the younger generation can use social media wisely and still retain their religious values ​​through the teachings of the Koran. Thus, it is hoped that this research can provide practical and theoretical benefits for the younger generation and the general public in overcoming the challenges of using social media in the digital era.

PROJECT MANAGEMENT IN THE FIELD OF INTERNATIONAL COOPERATION IN HIGHER EDUCATION INSTITUTIONS

The article evaluates the potential benefits of implementing project management in higher education and its influence on maintaining high-quality education. The study includes an analysis of a successful dual degree educational project involving several universities and surveys its participants, providing practical insights. The example of successful international cooperation, highlighted in the article, underscores the practical benefits of these methodologies. Key trends and challenges in applying project management within higher education management are identified, and various project management methodologies are analyzed to determine those most suitable for the educational context. The article provides recommendations to facilitate the implementation of project management in higher education institutions. Project management is a crucial tool in higher education, enhancing institutional goals and the quality of student training. It ensures efficient resource management, timely task completion, and alignment with labor market demands. Additionally, project management develops essential skills in students, such as teamwork and decision-making, and leads to adaptable curricula, cost reductions, and increased efficiency. Keywords: project management, international cooperation, higher education, management, education projects, higher education institutions, modern education.

Women's Language in Bargaining: A Sociolinguistic Study of Seller-Buyer Interactions in Dinoyo Market

This study investigates the characteristics of women’s language in market bargaining, revealing how specific communicative features affect buying and selling interactions. These features include the use of polite and non-confrontational language forms, such as hedges and tag questions, which reflect a desire to maintain harmonious social relations. Drawing on Robin Lakoff’s (1975) theory, the study examines how features like hedges, tag questions, and super polite forms are employed by female sellers and buyers during market interactions. The research methods include direct observation and conversation analysis to identify the frequency and context of these language features. The findings indicate that the use of female language traits not only influences the nature of bargaining but also contributes to establishing and maintaining positive social relationships between sellers and buyers. These insights enhance our understanding of communication strategies in economic contexts and underscore the importance of recognizing gender-specific linguistic features in everyday social interactions. Furthermore, the research suggests that a deeper understanding of women’s language traits can provide practical benefits for market participants, aiding in the improvement of their negotiation and marketing skills

Few-Shot Symbol Detection in Engineering Drawings

ABSTRACT Recently, there has been significant interest in digitizing engineering drawings due to their complexity and practical benefits. Symbol digitization, a critical aspect in this field, is challenging as utilizing Deep Learning-based methods to recognize symbols of interest requires a large number of training instances for each class of symbols. Acquiring and annotating sufficient diagrams is difficult due to concerns about confidentiality and availability. The conventional manual annotation process is time-consuming, costly, and prone to human error. Additionally, obtaining an adequate number of samples for rare classes proves to be exceptionally challenging. This paper introduces a few-shot framework to address these challenges. Several experiments with fewer than ten, and sometimes just one, training instance per class using complex engineering drawings from industry sources were carried out. The results suggest that our method not only significantly improves symbol detection performance compared to other state-of-the-art methods but also decreases the necessary number of training instances.

Factors Influencing the Adoption of Green Practices: A Case of Five Selected Shopping Malls in Lusaka District

There is growing recognition for the need for eco-friendly commercial buildings. However, there exists a significant gap between the potential for developing them and the actual implementation. Zambia faces the tangible impacts of climate change, such as droughts and floods, while the uptake of green initiatives remains sluggish. Environmental degradation, worsened by the absence of green initiatives, threatens ecosystems, biodiversity, and the overall health of the planet. Recognizing the urgent need for change, there is a growing consensus that economic growth should no longer be pursued at the expense of environmental degradation. Yet, despite the abundance of opportunities to develop and implement green practices, various stakeholders have not fully embraced these options. This study delved into the factors influencing green practice adoption, focusing on five prominent shopping malls in Lusaka district, Zambia. A mixed methods approach was adopted, and involved 25 purposively selected respondents that attended a questionnaire uploaded via google forms and semi-structured in-depth interviews. Quantitative data was analyzed using SPSS while content analysis was adopted for qualitative data. The study found a positive correlation between perceived benefits of green practices and willingness to adopt green practices. However, the study showed a disconnect between knowledge of climate change effects and adoption of green practices, which meant that the choice to implement green practices was not influenced by the knowledge of the consequential effects of climate change. This study findings underscore the importance of the educational knowledge on the benefits green implementation and government incentives had in fostering green practice adoption within commercial establishments. While the study provides valuable insights into the dynamics of green practice adoption in the context of Lusaka district, limitations and potential biases underscore the need for further research in this area.

Factors Influencing Farmers’ Adoption of Climate Smart Agriculture Practices in the Case of Ilu Aba Bora Zone, South West Ethiopia

To overcome the challenges of climate change, this study was intended to identify factors that affect the farmers’ adoption of CSA in the Ilu Aba Bora Zone of Southwest Ethiopia across varying agro-ecological zones. A mixed research design was used for this study. During this study, representative districts (Bure, Hurumu, and Nono Sale) were selected purposely based on their differences in agro-ecology, while rural kebeles and respondents were selected randomly from the districts to minimize the biases of the data. Data collection tools employed were a questionnaire, KKI, FGD, and field observations. A binomial logistic regression model was used to identify the interaction of response and explanatory variables and to draw a conclusion. The findings of this study revealed the majority (63.1%) of farmers did not have an understanding of the meaning, function, and goals of CSA practices. Existing CSA options in the study area were conservation agriculture, crop diversification, agro-forestry, early warning system, livelihood diversification, and improved livestock feed, which were practiced at different levels and by a few households. In particular, the early warning system and improved livestock feed were the least implemented CSA options, whereas livelihood diversification, conservation agriculture, and agro-forestry were the highly adopted practices in the area. Independent variables including on-farm income, farming experience, weather information, agro-ecology, farm land size, age, extension service, marital status, and off-farm income were the factors significantly affecting the adoption of CSA practices in the study area. Experts, governments, and nongovernmental organizations should improve farmers’ awareness of the benefits of CSA practices in ensuring food security, climate mitigation and adaptation through training and capacity building. The result of this study will show the direction for farmers, policy makers, planners, and other stakeholders to set up a solution in order to promote CSA practices and technologies based on agro-ecology.

How Family Group Conferencing Can Support a Contextual Safeguarding Response to Community-based Youth Harm: Lessons for Practice from a Participatory Study

Abstract This article reports on a study that brought Family Group Conferencing (FGC) and Contextual Safeguarding together to create an innovative practice response to extra-familial harm. It took place as part of a wider research project into the implementation of Contextual Safeguarding—an ecological approach to creating safety for young people harmed outside the home. Whilst there is growing understanding in social care of the contextual dynamics of young people’s safeguarding needs beyond their homes, many remain unsure about what it means, in practice, for them to create safety in a context. Using data collected via participatory and embedded research methods, this article considers how FGCs were adapted to create responses to youth harm that shifted the focus from ‘family’ to ‘community’; addressed the ‘social conditions of harm’; and drew on practitioners’ existing FGC skills. It demonstrates conceptual alignment and practice benefits for bringing the two approaches together and argues that drawing on the value-base and skills of FGC coordinators has considerable potential. The article highlights three key elements that are needed to recreate similar responses, namely: organisational mandate/support; conceptual framework aligned to ecological theory; and skills and attributes that include facilitation, working restoratively and with authority in challenging situations.

Assessing clustering methods using Shannon's entropy

Unsupervised clustering techniques are crucial for effectively partitioning datasets into meaningful subgroups. In this paper, we introduce a novel clustering fuzziness metric based on Shannon's entropy, which quantifies the level of uncertainty in clustering assignments. This metric is employed to develop a statistical test for evaluating clustering algorithms and to propose parametric corrections to enhance their performance. We provide empirical evidence showing that our approach significantly improves clustering quality compared to well-known algorithms such as Fuzzy K-Means (FKM) and Fanny. For instance, applying our correction method led to a notable decrease in Jensen-Shannon Divergence (JSD) values, indicating enhanced clustering accuracy. Our methodology is demonstrated through comprehensive experiments on both simulated and real-world datasets. Additionally, our approach proves effective in determining the optimal number of clusters, showing superiority over traditional methods. These results highlight the practical benefits of our metric and correction techniques for improving clustering performance.

Unmanned vehicles in health monitoring and medicine delivery with swarm algorithm innovations

The article delves into modern approaches and innovative technologies designed to monitor the health of military personnel using unmanned vehicles, while also assessing their potential for delivering medicines in combat environments. It thoroughly examines the wide range of sensors and technologies that enable remote measurement of vital signs, including but not limited to thermal imaging cameras, photoplethysmography sensors, and radio frequency sensors, highlighting their effectiveness in various operational contexts. The article also reviews numerous real-world applications of drones in both medical and humanitarian projects, showcasing the transformative impact these technologies can have in providing timely healthcare and support in areas where traditional medical infrastructure may be lacking. In addition to discussing the practical benefits of using unmanned vehicles for healthcare delivery, the article also addresses the challenges posed by their integration into military operations, such as logistical, technical, and regulatory hurdles. Furthermore, it presents a detailed example of how these innovative systems can be effectively combined to enhance the medical care provided to military personnel, especially during combat, where traditional access to medical services is often limited or compromised. The article proposes several key areas for future research and development, emphasizing the importance of continued technological advancements to fully harness the potential of unmanned vehicles for medical applications in the military sector. These areas include improving sensor accuracy, developing better communication systems, and creating more autonomous and efficient delivery mechanisms. Here are examples that prove, the article suggests that unmanned vehicles could play a critical role in revolutionizing both battlefield medicine and military logistics, offering new solutions for healthcare delivery in challenging environments.

Accelerated pump tube concept for hypersonic ground testing

This paper examines a new hypersonic wind tunnel concept which is referred to here as an ‘accelerated pump tube’ (APT). The facility can be considered a hybrid between a gun tunnel and an expansion tube. Like a gun tunnel, the test gas is isentropically compressed with a piston to raise its pressure and temperature; like an expansion tube, the compressed test gas is subject to an unsteady expansion to further increase its enthalpy and stagnation pressure. State-to-state analytical calculations incorporating high temperature equilibrium chemistry are presented which demonstrate that the APT is theoretically able to simulate a wide hypersonic terrestrial flight envelope from Mach 5–14, with significant capacity for pressure-length scaling. Potential practical benefits of this concept include relatively simple internal flow processes, removal of the need for a film secondary diaphragm and helium as a driver gas, and a reduction in experimental uncertainty through better defined internal boundary conditions and relatively low mechanical complexity. To assess the feasibility of the APT concept in a practical context, the paper considers how an APT based around a 25 m long, 600 mm diameter pump tube should be configured for a Mach 10 test flow. One-dimensional Lagrangian CFD analysis is used to compute piston dynamics for three different pump tube compression ratios and the associated test flows are characterised. The CFD results suggest that the concept should be viable in practice, and also validate the Mach 10 state-to-state calculations.

Children’s Retrieval of Science Facts: The Role of Hints and Confidence

ABSTRACT The effortful process of retrieving information from memory has been established as an effective strategy for improving student learning. However, we have a limited understanding of the development of retrieval practice in children, including contexts that may scaffold its benefit. In the current pre-registered study, we examined whether the use of hints during retrieval practice improved free recall in an online science learning task in 8- to 13-years-olds (N = 77, N females = 36). We found partial evidence supporting the provision of hints as boosting the benefit of retrieval practice. Children’s long-term retention of science facts was higher when they received hints during an earlier practice test compared to restudying information, but not compared to a test only condition without hints. Furthermore, we found similar effects across both age and levels of confidence, suggesting that retrieval practice remains stable across these factors.

Identifikasi Kematangan Buah Pisang Berdasarkan Variasi Jarak Menggunakan Metode K-Nearest Neighbor

This research aims to identify the level of ripeness of kepok bananas based on the color of their skin using the K-Nearest Neighbor (K-NN) method. Bananas are an important commodity in Indonesia, and various ripeness levels need to be identified. The current process of identifying banana ripeness is still done manually, which requires a lot of labor and tends to be subjective. The K-NN method is used to classify bananas based on their skin color. This research involves the collection of banana images with three ripeness levels (raw, ripe, and overripe) and the extraction of RGB color features from these images. Three distance methods, namely Euclidean, Minkowski, and Manhattan, are also employed to compare accuracy results. The evaluation results of this research show that the accuracy value for the Euclidean distance method is 84%, the Minkowski distance method is 82%, and the Manhattan distance method is 80%. Thus, the findings indicate that the K-NN method and the Euclidean distance method provide good results in identifying the ripeness level of bananas. By implementing the K-NN algorithm, this research attempts to address the weaknesses of the time-consuming and subjective manual identification process, with the hope of providing a more accurate and efficient solution for the banana industry. The results of this research can be used to automate the identification process of banana ripeness levels and improve efficiency in banana sorting. It is expected that this research can provide practical benefits to the community and serve as a basis for further research in this field.

The Influence of Environmental Sustainability Initiatives on Employee Morale: The Mediating Role of Organizational Identification

This study investigates the impact of environmental sustainability initiatives on employee morale, with a specific focus on the mediating role of organizational identification. Utilizing a mixed-methods approach, the research encompasses quantitative data from 500 employees across various industries in Germany and qualitative insights from semi-structured interviews. The findings reveal that environmental sustainability initiatives positively influence employee morale both directly and indirectly through enhanced organizational identification. Employees who perceive a strong alignment between their values and their organization's sustainability efforts report higher job satisfaction and commitment. The study also highlights the critical role of leadership and communication in successfully implementing sustainability initiatives. These insights extend social identity theory and contribute to the literature on corporate social responsibility (CSR) by elucidating the internal benefits of sustainability practices. Practical implications for organizational leaders and policymakers include integrating sustainability into core business strategies, fostering organizational identification, and promoting transparent communication to enhance employee morale and organizational effectiveness.

Early Addition of Selexipag to Double Therapy for Pulmonary Arterial Hypertension

A subgroup analysis of a randomized clinical trial established the efficacy of selexipag plus background therapy (monotherapy or double oral therapy [DOT]) vs placebo plus background therapy and found that the addition of selexipag within 6 months had an added benefit. However, the timing of selexipag addition to DOT and the incremental benefit in clinical practice is not well studied. To compare triple oral therapy (TOT) consisting of selexipag, endothelin receptor antagonist (ERA), and phosphodiesterase type 5 inhibitor (PDE5i) vs DOT consisting of ERA and PDE5i. This comparative effectiveness study was conducted using data from the US Komodo claims database to emulate a randomized trial. Patients aged 18 years or older with pulmonary arterial hypertension (PAH) treated with ERA plus PDE5i with records from July 2015 through June 2022 were duplicated to TOT and DOT and artificially censored when observed treatment deviated from assigned treatment. Hypothetical randomization was emulated using inverse probability of treatment weighting, and the study accounted for censoring-induced selection bias using inverse probability of censoring weighting. A pooled logistic model estimated the per-protocol difference between treatment groups. Data were analyzed from November 2022 through July 2023. TOT (addition of selexipag within 3, 6, and 12 months of initiating DOT) vs DOT. Adjusted risk of all-cause hospitalization, PAH-related hospitalization, and PAH-related disease progression over a 2-year follow-up. A total of 2966 patients with PAH (mean [SD] age, 54.3 [14.0] years; 2125 female [71.6%]) met eligibility criteria. Adding selexipag within 6 months of ongoing DOT was associated with a reduction in risk for all-cause hospitalization (adjusted hazard ratio [aHR], 0.82; 95% CI, 0.72-0.94), PAH-related hospitalization (aHR, 0.81; 95% CI, 0.70-0.95), and PAH-related progression (aHR, 0.82; 95% CI, 0.70-0.95) vs DOT alone. There were no associations if selexipag was initiated within 12 months for all-cause hospitalization, PAH-related hospitalization, or PAH-related disease progression. The association remained with a greater decrease in risk for disease progression vs DOT for selexipag initiation within 3 months (aHR, 0.74; 95% CI, 0.61-0.90). This study found that early selexipag addition to ERA plus PDE5i was associated with a reduction in risk of hospitalization and disease progression. These findings suggest that delays in selexipag initiation likely contribute to suboptimal patient and health system outcomes.

Analysis of Public Sentiment Towards Celebrity Endorsment On Social Media Using Support Vector Machine

Analysis of public sentiment towards celebrity endorsements on social media is very important to understand the public's response to promotional campaigns involving celebrities. In this study, we combine the VADER labeling method with the Support Vector Machine (SVM) method to analyze public sentiment toward celebrity endorsements on social media. Data is taken from various social media sources such as Twitter, Instagram, and Facebook. The data is pre-processed to ensure data accuracy and relevance and then labeled with the VADER method to determine the positive, negative, or neutral sentiment of the text. The labeled data is then extracted for features and used to train the SVM model. The trained SVM model is then validated using test data to measure its accuracy and performance. The results of the analysis provide useful insight into public sentiment towards celebrity endorsements on social media and can provide recommendations for stakeholders regarding this matter. Overall, combining the VADER labeling method with SVM in analyzing public sentiment towards celebrity endorsements on social media shows more accurate results and can provide practical benefits in marketing and promotional strategies. The results shown using the Support Vector Machine method with a ratio of 80:20 can provide average precision results of 77%, recall of 100%, f1-score of 87%, and accuracy of 76.92%. Twitter application user sentiment shows that 77% (338 data) of Twitter user reviews provide positive sentiment and 23% (119 data) provide negative sentiment reviews from a total of 517 data. Suggestions from researchers are that in future research they can add more data to make modeling easier to provide higher accuracy values. Using other classification and performance evaluation methods, such as Naive Bayes, Decision Tree, Fuzzy, or Deep Learning. Use other data processing tools, such as RapidMiner, Jupyter Notebook, RStudio, or others.