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Film serves not only as entertainment but also as a medium for conveying messages through verbal interactions between characters. In pragmatics, illocutionary acts are central to understanding a speaker’s intention within a given context. This study aims to describe and analyze the types and functions of illocutionary speech acts in the Indonesian film Komang (2025) by Naya Anindita, which portrays a love story across religious and cultural boundaries. Using a descriptive qualitative approach, data were collected through observation and transcription of dialogue from the film. The analysis is based on Searle’s classification of illocutionary acts: representatives, directives, expressives, commissives, and declaratives. The findings reveal that all five types appear in the film, each playing a role in shaping interpersonal dynamics, emotional tension, and cultural values. Directive and expressive acts are the most dominant, reflecting intense emotional exchanges and social interactions. Commissive and declarative acts highlight commitment and authority, contributing to the narrative progression and cultural identity negotiation. The study concludes that illocutionary acts in films can represent social conflicts and cultural expressions, and recommends further research on other films with varied genres and contexts. The integration of multimodal elements is also suggested to enrich the analysis of meaning in audiovisual texts.  

Learning strategies are very important to be applied in preparation for the implementation of learning in its implementation can be attributed to local wisdom which is a concept that refers to the culture of the surrounding community. This study aims to analyze learning strategies based on local wisdom and its integration with the improvement of narrative writing skills in Indonesian language learning over the last 10 years which have been published in SINTA-indexed journals. This research is a qualitative research using a literature review of 20 articles. The writing of this article is assisted by several applications such as publish or perish and mendeley. Learning strategies can be associated with local wisdom that is adapted to the culture in the local environment. The results of the literature review are in the form of a classification of learning strategies in improving the ability to write narratives based on local wisdom. The results of the analysis show that local wisdom can be integrated into learning models, learning media, and the development of teaching materials. Based on the research, the application of learning strategies based on local wisdom can improve the ability to write narrative texts and can train critical thinking, creativity and cultural literacy.

Learning strategies are very important to be applied in preparation for the implementation of learning in its implementation can be attributed to local wisdom which is a concept that refers to the culture of the surrounding community. This study aims to analyze learning strategies based on local wisdom and its integration with the improvement of narrative writing skills in Indonesian language learning over the last 10 years which have been published in SINTA-indexed journals. This research is a qualitative research using a literature review of 20 articles. The writing of this article is assisted by several applications such as publish or perish and mendeley. Learning strategies can be associated with local wisdom that is adapted to the culture in the local environment. The results of the literature review are in the form of a classification of learning strategies in improving the ability to write narratives based on local wisdom. The results of the analysis show that local wisdom can be integrated into learning models, learning media, and the development of teaching materials. Based on the research, the application of learning strategies based on local wisdom can improve the ability to write narrative texts and can train critical thinking, creativity and cultural literacy.

This study examines the intersection of sentiment discourse in news texts and user comments on Facebook Detikcom, focusing on government policies during the Covid-19 pandemic. The research explores institutional discourse in news articles that align with government narratives, while user comments reflect a spectrum of responses, from support to opposition. Using qualitative content analysis, this study applies critical discourse analysis (CDA) and sentiment analysis to examine linguistic strategies, ideological framing, and sentiment polarity. The data were taken from news texts and the comment section on Facebook Detikcom, collected through documentation and following several stages of observation, careful reading, selection, and classification. The findings show that social media transforms news consumption into a participatory discourse. This indicates that traditional narratives are challenged and reinterpreted by users. Sentiment clustering and engagement metrics further shape the visibility and influence of competing ideologies. This study contributes to digital discourse research by demonstrating that sentiment functions as an ideological tool in crisis communication. The analysis also highlights the evolving role of social media in public discourse and emphasizes the need for critical engagement with online news narratives and user-generated content.

Pox disease is an infectious disease that attacks the immune system. This disease is often considered mild because it is usually harmless and does not cause death. With the lack of counseling and treatment of this disease, lacks an understanding of pox disease, the types and the treatment . Some of the types of pox disease are Monkeypox, Chickenpox, and Cowpox. Cowpox can cause complications such as keratitis and corneal melting due to persistent erosion. To avoid complications of pox disease arising, CNN algorithm is considered capable of classifying the type of pox disease. Therefore, this study was conducted using 2 CNN methods used, namely: ResNet50 and VGG16. Because the distinguishing features of pox disease classification involve a mixture of color, shape and texture, CNN algorithm approaches including ResNet50 and VGG16 were tried. The ResNet50 model showed quite good results with an average accuracy of 76% but VGG16 had better accuracy with a value of 93%. The ResNet50 model showed good results with an average accuracy of 76% but VGG16 had better accuracy with a value of 93%. Using further evaluation, VGG16 has superior values with good precision, recall, and F1-score  for each class. This proves that VGG16 is a superior model for classifying pox disease types. The ResNet50 model is good at identifying three classes, namely, Chickenpox, Cowpox, and Healthy compared to the Monkeypox class because that class has 25% recall value and 40% F1-score  value. Similarly, the VGG16 model where the monkeypox class still has a recall value of 76%. This shows the potential of using artificial intelligence technology to classify smallpox disease.  

A paragraph is the smallest unit in written discourse which has an important role in building text cohesion and coherence. However, a lack of understandiag of the structure and function of paragraphs often becomes an obstacle in writing, especially in the context of learning Indonesian at the high school level. This research aims to analyze the structure and function of paragraphs in various types of text, such as narrative, descriptive and expository, and identify patterns that support communication effectiveness. This research uses a qualitative method with a content analysis approach. Data were obtained from five Indonesian high school level textbooks chosen randomly. The analysis focuses on paragraph structure (subject, predicate, object) and characteristics of text types. The research instrument includes guidelines for paragraph structure analysis, classification of text types, and criteria for effective paragraphs. The research results show that each type of text has a unique paragraph structure and function pattern, in accordance with its communication objectives. Narrative paragraphs support the storyline, descriptive create images, and expository convey factual information logically. In conclusion, understanding the structure and function of paragraphs can improve students' writing skills. This research recommends strengthening learning strategies that focus on analyzing paragraph structure and text variety to improve students' writing skills effectively.

This study enhances Jiang et al.'s compression-based classification algorithm by addressing its limitations in detecting semantic similarities between text documents. The proposed improvements focus on unigram extraction and optimized concatenation, eliminating reliance on entire document compression. By compressing extracted unigrams, the algorithm mitigates sliding window limitations inherent to gzip, improving compression efficiency and similarity detection. The optimized concatenation strategy replaces direct concatenation with the union of unigrams, reducing redundancy and enhancing the accuracy of Normalized Compression Distance (NCD) calculations. Experimental results across datasets of varying sizes and complexities demonstrate an average accuracy improvement of 5.73%, with gains of up to 11% on datasets containing longer documents. Notably, these improvements are more pronounced in datasets with high-label diversity and complex text structures. The methodology achieves these results while maintaining computational efficiency, making it suitable for resource-constrained environments. This study provides a robust, scalable solution for text classification, emphasizing lightweight preprocessing techniques to achieve efficient compression, which in turn enables more accurate classification.

The Sundanese language, once spoken by 48 million individuals, has experienced a significant decline in speakers, losing 2 million in the past decade. This decline is attributed to weakened intergenerational transmission and the dominance of more widely used languages. The challenges in developing Natural Language Processing (NLP) tools for Sundanese stem from the lack of annotated corpora, trained language models, and adequate processing tools, complicating efforts to preserve and enhance the language's usability. This research aims to address these challenges by implementing emotion classification in Sundanese text using Long Short-Term Memory (LSTM) and Bidirectional Encoder Representations from Transformers (BERT) models. The study utilizes a dataset of annotated Sundanese tweets, applying preprocessing techniques such as cleansing, stopword removal, stemming, and tokenization to prepare the data for analysis. The results indicate that the BERT model significantly outperforms the LSTM model, achieving an accuracy of approximately 80% compared to LSTM's 70%. These findings highlight the potential of advanced NLP techniques in enhancing the understanding of emotional nuances in Sundanese communication and contribute to the revitalization of the language in the digital age.

In this research the author took a study in the field of syntax. The syntactic survey in this research focuses on the types of transitive verb clauses in popular articles by Muh. Syahrul Padli in the April 2024 edition of the medium. This research aims to classify and analyze transitive verb clauses. This research is conducted with a synchronic approach that seeks to describe and provide the types and structures of clauses presented descriptively and qualitatively. The purpose of this study is to provide insight into the classification of verb clauses and provide examples of their classification in the description text in “Popular Articles by Muh. Syahrul Padli on Medium Masa Media April 2024 Edition”. Data collection in this research uses note-taking and listening/reading techniques. Data analysis techniques include data reduction, analysis, interpretation, and triangulation after which a conclusion is drawn. The data presentation technique is informal data presentation. From the data analysis, it can be concluded that some form of verb clauses in description texts, namely: transitive verb clauses. This article can be useful for students because it gives insight into the structured division of verb clauses and also the use of verb clauses in the article. 

Named Entity Recognition (NER) is a crucial natural language processing task that extracts and classifies named entities from unstructured text into predefined categories. While existing NER methods have shown success in general domains, they often face significant challenges when applied to specialized contexts like Filipino cultural and historical texts. These challenges stem from the unique linguistic features, and diverse naming conventions. This research introduces an enhanced rule-based NER approach that specifically addresses these challenges. At its core, the system utilizes curated Corpus of Historical Filipino and Philippine English (COHFIE), which serves as both training and evaluation data. This research presents an enhanced rule-based approach for NER using a Corpus of Historical Filipino and Philippine English (COHFIE) building on pattern-learning methods, incorporating character and token features, and by using positive and negative example sets. To enrich the classification process, we used the International Committee for Documentation – Conceptual Reference Model (CIDOC-CRM), a cultural heritage framework, to provide a more nuanced categorization of entities based on their historical and cultural significance. Tested across existing Filipino based models (calamanCy and RoBERTa Tagalog), the enhanced model shows improvement on identifying entities related to Filipino culture (CUL) and history terms (PER, ORG, LOC).

Education is the main foundation for the progress of society and individuals, Basic education is very important to build students' knowledge and skills, especially in the field of science. Conventional methods, such as the use of illustrations and textbooks, are often not interesting and interactive. Digital technology presents new opportunities in learning innovation, including by utilizing augmented reality (AR). This study aims to analyze and evaluate the use of interactive media based on augmented reality (AR) in learning the topic of animal classification based on their type of food. The method used is a literature study by reviewing various scientific sources, including journals, articles, and relevant research reports in the last five years. AR-based interactive media offers an innovative and immersive learning experience, allowing students to interact directly with virtual objects in the form of three-dimensional (3D) visualizations. The results of the study show that the use of AR can improve students' understanding of the material, increase engagement in learning, and provide a more interesting and contextual learning experience. In addition, AR media is considered effective in helping students identify animal characteristics based on eating habits, such as herbivores, carnivores, and omnivores. This study concludes that the integration of AR in science learning at the elementary school level has great potential to improve learning effectiveness.

Education is the main foundation for the progress of society and individuals, Basic education is very important to build students' knowledge and skills, especially in the field of science. Conventional methods, such as the use of illustrations and textbooks, are often not interesting and interactive. Digital technology presents new opportunities in learning innovation, including by utilizing augmented reality (AR). This study aims to analyze and evaluate the use of interactive media based on augmented reality (AR) in learning the topic of animal classification based on their type of food. The method used is a literature study by reviewing various scientific sources, including journals, articles, and relevant research reports in the last five years. AR-based interactive media offers an innovative and immersive learning experience, allowing students to interact directly with virtual objects in the form of three-dimensional (3D) visualizations. The results of the study show that the use of AR can improve students' understanding of the material, increase engagement in learning, and provide a more interesting and contextual learning experience. In addition, AR media is considered effective in helping students identify animal characteristics based on eating habits, such as herbivores, carnivores, and omnivores. This study concludes that the integration of AR in science learning at the elementary school level has great potential to improve learning effectiveness.

The 2024 Indonesian Presidential Election marked the fifth general election in the country, aimed at electing a new President and Vice President for the 2024–2029 term. Candidates competed to succeed the outgoing president, who had served two constitutional terms. A key aspect of this election was the candidate debates, where each candidate presented their vision, allowing the public to assess their policies. These debates were broadcast on platforms like YouTube, giving the public a space to comment. However, analyzing YouTube comments presents challenges due to the volume of data, language diversity, and informal expressions. Sentiment analysis, crucial for understanding public opinion, uses algorithms such as Naïve Bayes, which is based on Bayes' Theorem and assumes feature independence. Naïve Bayes is widely used in text analysis for its speed and simplicity. When applied to YouTube comments from the 2024 debates, the algorithm demonstrated its effectiveness, especially with a balanced dataset through random oversampling. It achieved 85.155% accuracy, high precision, recall, and an AUC of 96.8% on an 80:20 data split. Its fast classification time (0.000998 seconds) makes it suitable for real-time sentiment analysis, validating its use for political events. Future applications may incorporate advanced techniques like BERT for more sophisticated analysis.

This study aims to analyze the sentiment of iPhone product reviews fromAmazon using the BERT (Bidirectional Encoder Representations from Transformers) model to classify reviews as either positive or negative. The dataset, sourced from Kaggle, includes text reviews and star ratings, where high ratings indicate positive sentiment and low ratings indicate negative sentiment. After text preprocessing steps, including data cleaning, tokenization, and sentiment labeling, the BERT model was fine-tuned for sentiment classification, with the data split into training, validation, and test sets. Evaluation results demonstrate that the BERT model achieves a high classification accuracy, with an accuracy rate of 93.9% and a balanced F1 score between precision and recall. Confusion matrix evaluation also indicates that the model consistently identifies both positive and negative sentiments. This study shows that Transformer-based models like BERT are highly effective in understanding customer opinions in e-commerce, with broad application potential for data-driven decision-making in marketing strategies and product development.

Sentiment Analysis is a technique for extracting text data to obtain information about positive, neutral or negative sentiments. The purpose of sentiment analysis is given by internet users on social media to provide a personal assessment or opinion. Paga Lewu Shop that often gets user sentiment through social media is Paga Lewu Shop. The existence of consumer opinion sentiments about Paga Lewu Shop can be analyzed and utilized to obtain useful information for other customers and the Paga Lewu Shop. By using the Text Mining technique classification method, a sentiment will be known as positive, neutral or negative. One of the algorithms widely used in sentiment analysis is the Naïve Bayes classification method. This study uses the Naïve Bayes Classifier (NBC) method with tf-idf weighting accompanied by the addition of an emotion icon conversion feature (emoticon) to determine the existing sentiment class from tweets about the Paga Lewu Shop. The results of the study show that the Naïve Bayes method without additional features is able to classify sentiment with an accuracy value of 96.44%, while if the tf-idf weighting feature is added along with the conversion of emotion icons, the accuracy value can be increased to 98%.

The background of this research is students’ mistakes in writing review texts based on linguistic level studies. This research analyzes the fields of phonology, morphology, semantics and syntax in the review text of the film entitled Alive. This research aims to obtain results of language analysis errors in grade 8 students at Shuffah Al-Jamaah Tasikmalaya Middle School. The research method uses analytical descriptive research. The sample selection method in this study used a simple random sampling method. The data collection method used is (1) accumulating the results of students’ review texts at school as a data source, (2) reading carefully all data sources, (3) marking and assigning classification codes to the data, (4) classifying data based on the form of error. Language use, (5) presenting and describing data based on forms of language use errors. The instrument used is data analysis. Based on the results of data analysis, the most dominant errors that emerged were errors in the field of phonology, namely the omission of phonemes. Apart from that, it is also often found in the field of morphology regarding reduplication, namely the use of the word repeat with a square sign. The deletion of phonemes and incorrect reduplication of writing occurs, which is done by students to shorten the writing of a word so that the writing process can be completed more quickly without paying attention to good and correct linguistic rules. Students need to concentrate and be more careful in the process of writing activities. Apart from that, teachers also need to provide direction in the form of knowledge and insight regarding the basics of good and correct linguistics to minimize the occurrence of language mistakes and mistakes.

The background of this research is students’ mistakes in writing review texts based on linguistic level studies. This research analyzes the fields of phonology, morphology, semantics and syntax in the review text of the film entitled Alive. This research aims to obtain results of language analysis errors in grade 8 students at Shuffah Al-Jamaah Tasikmalaya Middle School. The research method uses analytical descriptive research. The sample selection method in this study used a simple random sampling method. The data collection method used is (1) accumulating the results of students’ review texts at school as a data source, (2) reading carefully all data sources, (3) marking and assigning classification codes to the data, (4) classifying data based on the form of error. Language use, (5) presenting and describing data based on forms of language use errors. The instrument used is data analysis. Based on the results of data analysis, the most dominant errors that emerged were errors in the field of phonology, namely the omission of phonemes. Apart from that, it is also often found in the field of morphology regarding reduplication, namely the use of the word repeat with a square sign. The deletion of phonemes and incorrect reduplication of writing occurs, which is done by students to shorten the writing of a word so that the writing process can be completed more quickly without paying attention to good and correct linguistic rules. Students need to concentrate and be more careful in the process of writing activities. Apart from that, teachers also need to provide direction in the form of knowledge and insight regarding the basics of good and correct linguistics to minimize the occurrence of language mistakes and mistakes.

This study is motivated by the status of Surah Yusuf as ahsanul qashash, which has special characteristics in terms of balagah, particularly the use of kinayah to convey sensitive narratives in a polite manner. This study aims to identify the classification of types of kinayah and reveal the pragmatic functions behind their use in the text. The research method used is descriptive qualitative with heuristic-semiotic analysis techniques, where the primary data is sourced directly from the verses of the Qur'an. Surah Yusuf predominantly uses kinayah 'an al-maushuf to refer to certain entities or positions in order to maintain the dignity of the narrative, in addition to the use of kinayah shifah and nisbah to describe the emotional turmoil of the characters. Functionally, this style of language plays the role of ihtiraz (preventive), mubalaghah (emphasis of meaning), and ta'dzim (respect). The implications of this study confirm that the use of kinayah is not merely linguistic aesthetics, but a medium of moral education that promotes polite interpersonal communication and is relevant to contemporary Islamic ethics.

Abstract Databases stored on storage media are rarely used by most of their users and even within a certain period of time the data is deleted because it is considered trash and only fills up the storage media. This assumption is not entirely true, because in fact a large database can provide the information needed for various interests, both for business interests in making decisions and for science and research. The development of information and communication technology in this era often called the millennial, information and communication technology is also increasingly advanced and developing and cannot be avoided. Where the development and progress of information and communication technology is growing very rapidly, such as the need for data processing which is increasing every day and if left alone, the data will be useless. By using the Text Mining technique, the classification method, a sentiment will be known to be positive, neutral or negative. One of the algorithms widely used in sentiment analysis is the Naïve Bayes classification method. This study uses the Naïve Bayes Classifier (NBC) method with tf-idf weighting accompanied by the addition of an emotion icon conversion feature (emoticon) to determine the existing sentiment class from tweets about Agu Ate Store. The results of the study show that the Naïve Bayes method without additional features is able to classify sentiment with an accuracy value of 96.44%, while if the tf-idf weighting feature is added along with the emotion icon conversion, the accuracy value can be increased to 98%.

The policy for using the MyPertamina application issued does not rule out the possibility of differences of opinion due to changes in the policy. There are many positive, neutral, and negative responses to the MyPertamina application implementation policy. To see the public's reaction to the MyPertamina application implementation policy, it can be seen through various media, including social media. Twitter is a social network that is widely used by people in Indonesia. The number of Twitter users in Indonesia reached 18.45 million in 2022, making Indonesia the fifth largest Twitter user country in the world. Researchers conducted a sentiment analysis of the search results for tweets containing the keyword "MyPertamina" using the support vector machine algorithm. 382 tweet data were obtained and classified using the support vector machine algorithm. Support vector machine is a supervised learning algorithm for data classification. SVM is very fast and effective in solving text data problems. Text data is suitable for classification with the SVM algorithm because the basic nature of text tends to be high-dimensional. Of the 382 data analyzed, the support vector machine classification using the RBF kernel with parameter C=2 gave the highest accuracy value of 80.51%, precision value of 81%, recall value of 81%, and F1 score value of 80%.