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This research is motivated by the use of social media Twitter which attracts its users with informative and popular content. Twitter has succeeded in reaching users from various groups, from officials to the general public. The features in it allow for unlimited communication. This research focuses on analyzing the principles of jokes in tweets from the Twitter account @kaesangp. The principle of joking is a method that is intended to offend feelings by being friendly or in the form of a basis for making conclusions that are true and false; an intentional violation of the maxims of politeness; and disclosure of taboo things in a speech. In accordance with the focus of the research objectives, pragmatic theory is used and is related to the principle of jokes. This research use desciptive qualitative approach. The listening method and note-taking technique were used as data collection methods. As the end of the research objective, results were obtained in the form of utterances in tweets from the Twitter account @kaesangp which contained utterances with the principle of joking. The research results were obtained from the classification and decomposition based on the type of joking speech, including satire, banter and jokes. The benefit of this research is to find out the principles of jokes contained in the tweets of related Twitter accounts.

Computer vision technology is used to improve work safety in the construction industry. The key in this project is the utilization of the YOLO method on the Roboflow platform. In addition to the Convolutional Neural Networks (CNN) algorithm, YOLO efficiently divides the image into a grid and classifies the objects in the grid by bounding box and confidence score. With the integration of YOLO, this project can achieve accurate and fast PPE detection. This project uses the YOLO method to detect head and body parts from input images. The detected body parts are then cropped and processed using the CNN method for classification. This project will also implement computer vision algorithms, including Deep Learning methods that currently have the most significant results in image recognition is CNN method, to automatically detect and monitor the use of PPE. This model achieves mAP 64.1%, Precision 73.2%, and Recall 60.2%. The Streamlit framework was used for deployment, creating a web application for PPE compliance tracking. This project, ''Health and Safety PPE Compliance Tracking'', aims to improve work safety in the construction industry. This project uses Computer Vision technology to detect, monitor, and ensure worker compliance with the use of appropriate PPE. The suggestion is to conduct further trials using other datasets in the form of photos or videos that can be done in real-time by ensuring that the colors of hats and vests do not vary too much to detect the conformity of labeling with PPE use.

Background: Public opinion analysis has become increasingly important in the digital era, where social media platforms generate large-scale textual data reflecting public perceptions toward environmental policies. Advances in Natural language processing (NLP) and machine learning enable systematic sentiment classification to support data-driven decision-making. Objective: This study aims to evaluate the effectiveness of several sentiment classification models in analyzing Indonesian-language social media data related to environmental policies. Method: The research employed a text mining pipeline including data crawling, preprocessing (case folding, tokenization, stopword removal, and stemming), and vectorization using TF-IDF. Three classification models Logistic Regression, Support Vector Machine (SVM), and Long Short-Term Memory (LSTM) were trained and evaluated using accuracy and F1-score metrics. Results: Experimental findings indicate that LSTM achieved the highest performance with 91.7% accuracy and 91.2% F1-score, outperforming SVM (88.5%) and Logistic Regression (84.2%). Sentiment distribution analysis shows that public opinion is dominated by positive sentiment (47.5%), followed by neutral (32.0%) and negative (20.5%). Overall: The results demonstrate that deep learning-based models provide more robust contextual understanding and more reliable sentiment mapping for environmental policy analysis.

This research is based on an analysis of an American short story entitled The Lottery by Shirley Jackson. The purpose of this analysis is to examine and categorize the characterization of the character in the short story. The methods applied to analyze the data in this study is a qualitative method to describe in words the examination of the short story based on the characterization. The data source is collected through documentation and note-taking processes. For the results of this analysis the character in the short story classified into several types and detailed classifications of each character's traits, actions, and roles within the context of the narrative.

This research aims to improve the effectiveness of lung cancer classification performance using Support Vector Machines (SVM) with hyperparameter tuning. Using Radial Basis Function (RBF) kernels in SVM helps deal with non-linear problems. At the same time, hyperparameter tuning is done through Random Grid Search to find the best combination of parameters. Where the best parameter settings are C = 10, Gamma = 10, Probability = True. Test results show that the tuned SVM improves accuracy, precision, specificity, and F1 score significantly. However, there was a slight decrease in recall, namely 0.02. Even though recall is one of the most important measuring tools in disease classification, especially in imbalanced datasets, specificity also plays a vital role in avoiding misidentifying negative cases. Without hyperparameter tuning, the specificity results are so poor that considering both becomes very important. Overall, the best performance obtained by the proposed method is 0.99 for accuracy, 1.00 for precision, 0.98 for recall, 0.99 for f1-score, and 1.00 for specificity. This research confirms the potential of tuned SVMs in addressing complex data classification challenges and offers important insights for medical diagnostic applications.

This article has been retracted at the request of the Editor-in-Chief. The journal was alerted to issues within this article, including significant overlap in content, methodology, and visual materials with another previously published article: "Social Engineering Attack Classifications on Social Media Using Deep Learning" (DOI: 10.32604/cmc.2023.032373) published in Computers, Materials & Continua in 2023. Upon thorough investigation, it was found that the article substantially reproduces ideas, methodologies, and figures from the original work without proper attribution, violating the ethical standards of the journal and academic publishing. The authors were contacted and asked to provide an explanation for these concerns. The corresponding author acknowledged the oversight and accepted responsibility for the duplication. Consequently, the authors formally requested the withdrawal of the paper. As per journal policy, the Editor-in-Chief has decided to retract the article due to a breach of publication ethics. The journal sincerely regrets that these issues were not detected during the manuscript screening and review process and apologizes to the authors of the original article, as well as to the readers of the journal. For more information on the journal’s ethical policies, please visit: Retraction Policy.

The proliferation of imported used clothing trade is due to clothes sold having foreign brands at cheap prices so that people who want to stay fashionable prefer imported used clothes because they are considered more affordable. The Clinical Pathology Laboratory of Muhammadiyah University Surabaya stated that used clothing samples contained mold or yeast fungi, Staphylococcus aureus bacteria, Escherichia coli bacteria and HPV (Human Papilloma Virus). This study aims to investigate the regulations regarding the trade of secondhand clothing in Indonesia and explore the legal protection provided for consumers of secondhand clothing in the country. The study employed a normative juridical legal research method, utilizing both the statute approach and conceptual approach. It drew upon legal materials such as Law Number 8 of 1999, Law Number 36 of 2009, Law Number 7 of 2014, and Minister of Trade Regulation No. 18 of 2021. The initial finding of the research was that the importation of secondhand clothing in Indonesia has been prohibited under Minister of Trade Regulation No. 18 of 2021, Article 2, paragraph (3), while local secondhand clothing businesses are permitted based on the Indonesian Business Field Standard Classification (KBLI) with code 47742. Secondly, legal protection for secondhand clothing consumers in Indonesia has been regulated in Law Number 8 of 1999. One of the consumer rights that needs to be fulfilled in consumer protection is the right to compensation for losses suffered by consumers based on Article 4 number 8, Article 7 letter f, and Article 19 paragraph (1).

This study explores the utilization of LightGBM, a gradient-boosting framework, to classify the inhibitory activity of beta-secretase 1 inhibitors, addressing the challenges of Alzheimer's disease drug discovery. The study aims to enhance classification performance by focusing on overcoming the limitations of traditional statistical models and conventional machine-learning techniques in handling complex molecular datasets. By sourcing a dataset of 7298 compounds from the ChEMBL database and calculating molecular descriptors for each compound as features, we employed LightGBM in conjunction with a set of carefully selected molecular descriptors to achieve a nuanced analysis of compound activities. The model's efficiency was benchmarked against traditional machine-learning algorithms, revealing LightGBM's superior accuracy (84.93%), precision (87.14%), sensitivity (89.93%), specificity (77.63%), and F1-score (88.17%) in classifying beta-secretase 1 inhibitor activity. The study underscores the critical role of molecular descriptors in understanding drug efficacy, highlighting LightGBM's potential in streamlining the virtual screening process. Conclusively, the findings advocate for LightGBM's adoption in computational drug discovery, offering a promising avenue for advancing Alzheimer's disease therapeutic development by facilitating the identification of potential drug candidates with enhanced precision and reliability.

Indonesia's golden opportunity to take part in a world-class soccer competition at the U-20 World Cup competition was wiped out, as FIFA gave the decision to revoke Indonesia's status as host of the U-20 World Cup. Indonesian netizens who felt disappointed expressed their opinions and trended on social media Twitter. This research focuses on sentiment analysis of tweets using a combination of FastText embeddings method for word vectorization and using LSTM type RNN algorithm for sentiment classification. The dataset used totals 9,645 data consisting of 4,141 positive data and 5,504 negative data taken from March 29, 2023 to April 05, 2023. The test results on the LSTM model provide the best performance with an accuracy value of 74.92%, precision 74.74%, recall 74.92%, and f1-score 74.78%. The conclusion of this research is that the majority of datasets have negative sentiments, which means that people are more likely to give negative opinions than to provide support to Indonesian football which is experiencing problems. It is hoped that with this conclusion in the future people will better control their opinions and provide positive opinions when Indonesia is experiencing problems.

This research aims to determine the ability to organize compositions, process colors, adjust colors, and determine the level of suitability of shapes in the results of paintings using umbrella media at the Qalam Jihad Painting Studio Pematangsiantar. This research uses a qualitative descriptive method, by researching a topic in a systematic, factual and accurate manner obtained from data collection in the field and carried out based on existing facts and characteristics. This research is to review the composition, color and shape of paintings using umbrella media by studio students aged 13-15 years. The composition aspect gets the lowest score and the color aspect gets the highest score. This can be seen from the good coloring and color combinations with dark and light on the objects and colors that are seen clearly and appropriately by the studio students. Based on this, it produces a higher color aspect than other aspects. The classification of the results of reviewing the composition, color and shape of paintings using umbrella media at the Qalam Jihad Pematangsiantar painting studio is that 7 students are categorized as good with a score of 241.4 to 266 and an average score of 80.5 to 88.6. 8 students were quite good with scores of 211.2 to 234.9 with an average score of 70.4 to 78.3.  

Big data is a collection of data that has a large volume, so traditional data processing technology is unable to handle it well. Marketplace is a platform that most people often use to shop online. On this platform there is a comments column for writing reviews of products that have been purchased. Consumer reviews have an important role in understanding customer perceptions and sentiments towards the products being sold. Classification uses the Support Vector Machine method. The goal is to classify consumer reviews into positive or negative sentiment categories. The test uses data from more than 300 data samples with the assumption of independence between the features in the data. The results of big data analysis of consumer sentiment reviews of health masks on the marketplace used the support vector machine method with an accuracy value of 88%. The results of the analysis can be concluded that the dominant results of scraping reviews on health mask products lead more to positive reviews. The results on wordcloud of negative reviews provide insight to improve the quality of masks which are still lacking in terms of thinness, straps breaking easily, tears, holes, rubber quality and product packaging.    

The aim of this research is to identify and analyze the economic potential of the base sector so that economic growth in East Kutai Regency can increase. Classification of economic sectors that have advantages and potential and can be developed to boost economic growth in East Kutai Regency. An overview of the pattern and structure of economic growth in East Kutai Regency. This research uses quantitative secondary data. The approach used is the Location Quotient (LQ) analysis tool to identify economic sectors that are the base sectors, Shift Share analysis to identify economic sectors that have the potential to become leading sectors and Klassen Typology analysis to identify a picture of the pattern and structure of economic growth to support economic development and regional development in East Kutai Regency. The research results show that during the research period, namely from 2013 to 2022, the mining and quarrying sector still dominates the economic structure of East Kutai Regency with the contribution of this sector to the total GRDP of 83.37%. The Mining and Quarrying sector in East Kutai Regency is a basic sector, so changes in GRDP growth or policy changes in the mining and quarrying sector are believed to have an impact on the formation of GRDP as a whole and besides that, this sector also has competitive advantages and is included in the fast-growing sector. and grow fast. East Kutai Regency in 2013-2022 shows that there are eight economic sectors that have a negative sign (PPij < 0) or slow growth, namely the Agriculture, Forestry and Fisheries sectors amounting to Rp. – 53,630.94,-, Processing Industry sector Rp. – 117,477.84,-, Transportation and Warehousing sector Rp. – 24,119.22,-, Financial Services sector Rp. – 2,233.09, Real Estate sector Rp. – 10,176.60, Corporate Services sector Rp. – 1,288.94, Government Administration, Land and Mandatory Social Security sectors amounting to Rp. – 4,012.91, Education Services sector Rp. – 7,759.64,-. This happens because at the provincial level the growth of these sectors has slowed down, resulting in a slow growth impact on the same sectors in East Kutai Regency.

Within language, there are several categories, such as language politeness, which have objectives and intents that vary depending on the context and situation of human communication. In this language civility study, attention is drawn to the goals and intentions generally contained in our language usage. This study aims to identify forms of compliance and violations of the principle of language politeness on the Satu Persen playlist of Taman Edukasi: Career Preparation. This research uses two approaches, namely the qualitative descriptive approach as a methodological approach and the pragmatic approach as a theoretical approach. The technique used in collecting data is the listen-and-record technique. Data analysis of this research is presented through a data triangulation process, which includes three stages: data verification, data presentation, and data classification. The author conducted this research hoping that public knowledge about the principles of language politeness will be wider. This study aims to identify forms of compliance and violations of the principle of language politeness on the Satu Persen playlist of Taman Edukasi: Career Preparation. The results showed that in the playlist there were thimbles of generosity, wisdom, consensus, opinion, and sympathy thimbles. However, there is no thimble of apology, forgiveness, appreciation, simplicity, and feeling. Furthermore, the study's findings demonstrate the necessity for an increased awareness of the utilization of courteous and deferential language in communication, particularly concerning the realm of education and career readiness.

Education and human life cannot be separated, because education as a determinant of the direction of human life, the higher human education will show the higher quality of human life and vice versa. In line with the development of education, however, there are still people who reject the progress of education because they believe that the development of the times is not a guarantee for the realization of independence and peace of mind. This research aims to raise the issue of education development in Baduy community of Leuwidamar District, Lebak Regency, Banten Province. The Baduy community consists of groups, Inner Baduy, Outer Baduy and Muallaf Baduy, the three of them have their own uniqueness, for the Inner Baduy community they reject various forms of modernization, while the Outer Baduy community they accept although there is still a classification between what is accepted and what is rejected, while the Muallaf Baduy community totally accepts the development even their lifestyle is not like the lifestyle of urban communities, in terms of their children's education some of them have enjoyed higher education.

This study explores the role of sentiment analysis as a predictive tool for understanding and forecasting product launch success in the digital market. Sentiment analysis involves the classification of consumer sentiment expressed on social media platforms such as Twitter and Instagram, and it can significantly impact businesses by predicting consumer behavior and product performance. The research highlights the relationship between social media sentiment and product success, demonstrating that positive sentiment is strongly correlated with higher sales and consumer engagement, while negative sentiment can lead to declines. Machine learning models, including Support Vector Machines (SVM) and Random Forest, were employed to classify sentiment from large volumes of social media data and correlate it with product performance indicators such as sales volume and consumer interaction. The study found that sentiment analysis models were highly effective in predicting product success, with positive sentiment generally driving product profitability and negative sentiment posing a potential threat to brand reputation. Moreover, the analysis showed that social media sentiment provides real-time insights into consumer perceptions, enabling businesses to quickly adjust marketing strategies and product development plans. These findings underscore the importance of integrating sentiment analysis into product launch evaluations and strategic decision-making. Future research should explore the integration of sentiment analysis with other predictive market models and investigate the effects of fake reviews and post-purchase consumer behaviors on product success.

In the agricultural sector, the automatic identification of chili pepper varieties is crucial for improving production efficiency and quality. This study developed a chili pepper variety detection system based on characteristics using the Principal Component Analysis (PCA) method. The PCA method was used to reduce the dimensionality of chili pepper image data, thereby facilitating the classification process while retaining the key features necessary for chili pepper variety identification. The recognition system for chili pepper identification involves inputting chili pepper image data into a computer. The computer then interprets and identifies the chili pepper variety, and the test data utilizes a dataset of chili pepper images from various varieties. The research results indicate that the proposed system achieves a high level of accuracy in detecting and classifying chili pepper varieties. Consequently, this system can assist farmers and agricultural industry stakeholders in the chili pepper sorting and selection process, thereby improving operational efficiency and the quality of the harvest.

Biometric features that can be used for identification include iris, voice, DNA and fingerprints. Fingerprints are the most widely used biometric feature because of their uniqueness, universality and stability. Fingerprint recognition can be grouped into two different forms of problems, namely verification and identification. Verification is comparing one fingerprint with another fingerprint. Meanwhile, identification is matching an input fingerprint with fingerprint data in the database. Thus, identification can be interpreted as an extension of verification carried out by comparing one fingerprint to many fingerprints. Identification is inherently more complex than verification. The problem increases as the number of fingerprint datasets increases, resulting in an increase in the time required for the identification process. However, there is a way to overcome this complexity, namely classification. Apart from that, evolutionary algorithm optimization can also be carried out. The Chromosome Algorithm is an improvement on the evolutionary algorithm with a separate local search process. The memetic or chromosomal algorithm is a simple algorithm with reliable performance that can provide accurate solutions to problems in the real world. The current challenge is with the increasing growth of datasets (more than 106) which include the process of clustering text analysis, molecular DNA simulation, feature selection, and forecasting, handling large-scale optimization such as complex simulations, data mining, quantum chemistry, spectroscopic analysis, geophysical analysis, drug discovery, and fingerprint recognition studies. Chromosome algorithms have proven to be very competitive in large-scale optimization because they are based on stochastic algorithms that do not require gradient information.  

The results of data classification and field surveys by Unsuri students in Wilayut Village, Sukodono District. Has potential MSMEs in the form of businesses that produce a product of goods or services. However, in marketing activities, business actors are still doing conventionally and the use of branding strategies is also not optimal. So that the scope of marketing is still limited, making the business being run unable to develop properly. KKN activities as a form of community service in Wilayut Village aim to provide social contributions from the campus to the community as well as for the community to get help with thoughts and energy to carry out village development and solve community problems in the village. The method used is the MSME data classification method and partner participation by providing direction on the benefits of social media. Participating in contributing to the classification of MSME data in Wilayut Village and helping business actors in increasing the existence of cilok kuah seblak bu tin as one of the MSMEs engaged in the food sector.

Abstract: Indonesia, which consists of thousands of islands, reaching 17,001 islands, is known as a maritime country because the ocean area is larger than the land area, around 2/3 of the total area of ​​Indonesia. Sea transportation as part of the National Transportation System needs to unite all regions of Indonesia. Maritime transportation accidents occur due to 3 (three) important factors, namely human error, technical factors and weather (natural) factors. The purpose of this research is to analyze the influence between the independent variables, namely the Role of Harbor Master, Communication Ability and ISM Code, for the dependent variable, namely Shipping Safety. The population in this study was 130 people consisting of ship crew and employees of the PT. ASDP Indonesia Ferry (Persero) Padang Bai Branch who had knowledge classification about shipping safety and the sample in this study was 100 respondents with the technique used was non-probability sampling technique namely by using quota sampling. The analytical method used is descriptive analysis and quantitative analysis, data is analyzed using multiple linear regression analysis with the help of the Statistical Package For Social Science (SPSS) software. The results of this research by testing the t-test hypothesis show that the variables Role of Harbormaster, Communication Ability and ISM Code have a positive and significant effect on shipping safety at the Padang Bai Bali Ferry Port. Based on the results of the research, it can be seen that the research model of the multiple linear regression equation is Y = 2.898 + 0.436X1 + 0.214X2 + 0.189X3 + µ. From the regression results, it can be seen that the variable that has the most dominant influence on shipping safety is the role of the harbor master variable with a regression coefficient of 0.436. With the r square test of 0.526 or 52.6%, which means that the increase in shipping safety at the Padang Bai Bali Ferry Port is influenced by the Role of the Harbor Master, Communication Ability and ISM Code, namely 52.6% and other factors that influence the increase in shipping safety at the Padang Ferry Port Bai Bali was 47.4%.

The research aims to find out what the emotional state of teenagers in novels is by using the theory of literary psychology according to David krech's book of emotional classification. It identifies the underlying emotions, the emotions associated with sensory stimulation, emotions related to self-assessment and emotions connected with others. This research method employs qualitative methods using descriptions, dialogues and characters that relate to the emotional reactions of characters. The results of analysis show that the novel's emotional main character has such basic emotions as happiness, anger, fear and sadness in various situations. This study provides insight into how the emotional condition of the novel can be perceived by the theory of David krech. This study may be a reference to further research on emotional states in literature.