Publication Search

This community service program addressed the limited capacity of educators to use artificial intelligence in a structured and pedagogically responsible way for textbook development. The program focused on empowering educators through NotebookLM in the Ngabuburit AI 2026 activity. Its objective was to improve participants’ understanding and practical skills in organizing sources, designing textbook structures, developing chapter content, and generating interactive learning media. The program used a participatory training approach consisting of presentation, demonstration, guided practice, discussion, and feedback-based evaluation. The results showed that participants gained a clearer understanding of AI-assisted academic writing workflows and recognized NotebookLM as a useful tool for integrating source analysis, textbook writing, and learning media preparation. The activity also fostered new awareness that artificial intelligence can strengthen, rather than replace, educators’ academic roles in producing more systematic and interactive teaching materials.

This study aims to analyze public sentiment toward the hashtag #KaburAjaDulu, which has circulated widely on the social media platform X (formerly Twitter). The hashtag reflects the growing anxiety among the public, especially younger generations, regarding socio-political issues in Indonesia. The data were collected using web scraping techniques, focusing on user-generated tweets that contain the hashtag. A comprehensive text preprocessing phase was conducted to clean the raw data by removing irrelevant elements such as URLs, emojis, numbers, and punctuation. The research applies a hybrid classification approach using a combination of Support Vector Machine (SVM) and Random Forest algorithms to categorize sentiment into three classes: positive, negative, and neutral. The performance of the model was evaluated using metrics such as accuracy, precision, recall, and F1-score to determine the effectiveness of the classification. The study aims to demonstrate that combining algorithms can improve classification performance compared to using a single algorithm. This research contributes to the field of sentiment analysis and provides valuable insights for researchers, policymakers, and social observers in understanding public opinion trends in digital media.

QRIS (Quick Response Code Indonesian Standard) has become a widely used national digital payment standard. User satisfaction with this service needs to be monitored continuously to ensure its sustainability. This study aims to predict the level of QRIS user satisfaction based on their experiences and perceptions expressed organically on the Twitter social media platform. The method used is sentiment analysis with the Naive Bayes classification algorithm implemented using RapidMiner software. The research data was obtained from Twitter user comments collected through web scraping techniques. The text data then went through a preprocessing stage that included cleansing, stopword filtering, stemming, and tokenizing to be prepared as features ready to be processed by the model. The data was divided into training (80%) and testing (20%) subsets for model training and validation. The results showed that the Naive Bayes model was able to predict user satisfaction sentiment with an accuracy of 80.99%. These findings indicate that the model is highly accurate in identifying satisfied comments and sufficiently sensitive in detecting dissatisfaction. This study concludes that sentiment analysis of Twitter UGC data using Naive Bayes is an effective and efficient approach for predicting QRIS user satisfaction in real time. The practical implication of this study is to provide an automatic feedback system for service providers to monitor public sentiment and take targeted corrective actions.

The growth of information technology has driven changes in consumer behavior, one of which is through e-commerce platforms such as Shopee. This phenomenon has generated a large number of customer reviews, including those for local cosmetic products such as Wardah. These reviews serve as an important source of information for understanding customer perceptions and satisfaction levels. However, manual analysis of large and linguistically diverse datasets is inefficient and potentially subjective. This study aims to implement the multi-category Naive Bayes algorithm to classify the sentiment of Wardah product reviews on Shopee into three categories: positive, negative, and neutral. The data were collected using a web scraping technique and processed through a series of preprocessing stages including case folding, tokenization, stopword removal, stemming, and text cleaning. Subsequently, term weighting was performed using the TF-IDF method prior to classification. Model performance was evaluated using a confusion matrix as well as accuracy, precision, and recall metrics. The results indicate that the multi-category Naive Bayes algorithm achieved an accuracy of 86.00%, a precision of 86.63%, and a recall of 98.24%. This approach can assist business practitioners in objectively understanding customer opinions and support decision-making in business strategy and product development.

The development of social media, especially TWITTER, has become one of the main means for people to express opinions and criticism on various issues, including the performance of law in Indonesia. This study aims to analyze public sentiment towards the performance of law based on TWITTER user comments using the Naïve Bayes algorithm. The research data consists of 1004 comments collected from several videos related to legal topics. The analysis process includes the stages of data crawling, pre- processing (text cleaning, normalization, and tokenization), labeling sentiment into positive, negative, and neutral, and testing the Naïve Bayes model. The results show that the Naïve Bayes algorithm is able to classify sentiment with an accuracy level of 93.73%. The distribution of sentiment from 1004 comments shows that the majority of public opinion is (negative/positive/neutral), which indicates that public perception of the performance of law is still (critical/positive). These findings are expected to be input for related parties to understand public opinion and improve the quality of legal performance in

Based on the 2023 Indonesian Health Survey, the prevalence of overweight among adults reached 23.4% nationally and 24.5% in East Java Province. This condition highlights the need for food innovation, particularly high-fiber snacks suitable for overweight adults. This study aimed to develop cereal based on okara (soy pulp) flour and mung bean flour as an alternative high-fiber food product. Organoleptic testing was conducted on 30 semi-trained panelists evaluating color, aroma, texture, and taste using hedonic and hedonic quality tests. Data were analyzed using the Kruskal–Wallis test followed by the Mann–Whitney test, while nutrient content analysis was analyzed using One Way ANOVA followed by Duncan’s post hoc test. The results showed that in the hedonic test, color, texture, and taste parameters were significantly different (p<0.05), while aroma was not significantly different (p>0.05). In the hedonic quality test, color and taste showed significant differences (p<0.05), whereas aroma and texture did not (p>0.05). Overall, formulation 922 was the most preferred by panelists. The nutritional content of the cereal ranged from 407–410 kcal for energy, 76.44–77.53% carbohydrates, 8.91–9.66% protein, 6.80–7.30% fat, 5.20–5.51% moisture, 1.26–1.41% ash, and 9.72–10.90% total dietary fiber. The developed cereal is classified as a high-fiber food and has potential as a healthy snack for overweight adults.

This research aims to analyze how gender and culture are represented in the Grade VIII Indonesian language textbook of the Merdeka Curriculum. The focus is on various aspects of gender representation, such as the depiction of characters, their roles, activities, attributes, social status, gender equality, and stereotypes related to gender. Additionally, the study explores cultural representation, which encompasses cultural forms, diversity, local traditions, context, ways of presentation, and the cultural values expressed in the textbook's texts and illustrations. The methodology employed is descriptive qualitative research with a content analysis framework. Data were gathered through documenting and note-taking methods on the content of the textbook, followed by an analysis process that includes identification, classification, interpretation, and drawing conclusions. Findings indicate that the gender representation in the textbook predominantly portrays men as leading figures in public roles, leadership, and decision-making, whereas women are mainly shown in nurturing, domestic, and supportive capacities. On the other hand, the cultural representation illustrates the variety of Indonesian culture by showcasing regional customs, languages, art forms, traditional cuisine, practices, and societal norms. The study concludes that although the textbook presents cultural diversity adequately, there is a need for improvement in gender representation balance to better reflect equality values in the educational experience.

The article entitled “Gender Construction and Representation of Social Actors in the Drama Lutung Kasarung: A Critical Discourse Analysis by Theo van Leeuwen” aims to describe the representation of gender-based social actors through inclusion and exclusion strategies and to reveal the construction of gender ideology built in the drama text. This study uses a qualitative approach with a descriptive-analytical design. The research data source is the drama text Lutung Kasarung, while the analysis unit includes dialogue, narrative, and the depiction of characters who represent gender-based social actors. Data collection techniques are carried out through documentation by reading, identifying, and grouping data according to Theo van Leeuwen's analysis categories. The results of the study show that the inclusion strategy is more dominantly used to present male characters as strong, rational figures, and have authority in determining the course of the story. In contrast, female characters are represented in two patterns, namely the ideal passive woman and the dominant woman who is constructed negatively. In addition, the exclusion strategy is used to obscure the role of women in decision-making, thereby reinforcing gender marginalization. This study concludes that the drama Lutung Kasarung represents patriarchal ideology through discourse practices that shape power relations between men and women.

The rise of social media has created a digital public sphere that enables users to express their opinions on social and political issues openly and in real-time. One of the most discussed topics on social media platform X is the trending hashtag #IndonesiaGelap, which reflects public concern and criticism regarding various governmental and societal conditions. This study aims to conduct sentiment analysis on tweets containing the hashtag to determine the overall sentiment trend among users. The method employed in this research is the Naive Bayes classification algorithm, known for its simplicity and effectiveness in text classification. To enhance the model’s performance, Particle Swarm Optimization (PSO) is applied to optimize feature selection and parameter tuning. The dataset consists of public tweets collected via the Twitter API, followed by preprocessing, feature extraction using TF-IDF, and sentiment classification into three categories: positive, negative, and neutral. The results indicate that the integration of PSO significantly improves the classification accuracy of the Naive Bayes model compared to the baseline. The majority of tweets related to #IndonesiaGelap exhibit a negative sentiment, indicating widespread public dissatisfaction and criticism. This research is expected to contribute to a better understanding of public perception and serve as valuable input for stakeholders in addressing social issues in the digital age.

This research is motivated by the shortcomings in the Indonesian Language Textbook for Grade X Published by the Ministry of Education, Culture, Research, and Technology, Revised Edition 2023, in the form of inconsistent material presentation patterns, imbalance in linguistic and literary aspects, and unequal distribution of materials. This research study aims to assess the content suitability of the Indonesian Language Textbook for Grade X Published by the Ministry of Education, Culture, Research, and Technology, Revised Edition 2023. The method applied in this research is content analysis. The findings of this study indicate that chapters I to VI in the book have met or fulfilled the five indicators of content suitability according to Hartono (2016), although chapter IV is less suitable for the depth and breadth of material indicators because it does not include material on the structure of poetry. However, overall, this textbook is very suitable for use as a primary reference book in learning Indonesian. Thus, this study can be an evaluation material for textbook writers to complement the shortcomings in the next edition of new Indonesian language books.

This article examines how business entities construct eco-friendly narratives as strategic persuasive instruments in sustainable product advertising, operating through discursive and ideological mechanisms deliberately managed by corporations. Drawing on Norman Fairclough's Critical Discourse Analysis (CDA) framework, which dissects discourse across three analytical dimensions such as text, discursive practice, and social practice, as a lens to expose corporate ideology concealed beneath sustainability claims, this study analyses four eco-friendly product advertisements from international and domestic brands published between 2020 and 2026. Findings reveal that eco-friendly advertising narratives are constructed through three dominant discursive strategies: 1. Naturalization of consumer identity as a morally responsible environmental subject. 2. Reframing of consumption as a form of activism. 3. Linguistic greenwashing that conceals the contradictions of green capitalism behind vague sustainability lexicons. The study argues that business entities actively manage eco-friendly narratives not merely as informative messages, but as ideological apparatuses that strategically discipline consumer subjectivity, reproduce corporate hegemony, and legitimize green capitalism practices within ecological discourse. Implications for environmental communication studies, advertising regulation, and consumer critical literacy are discussed in the concluding section.

This research is based on the importance of studying Malay-Jawi religious manuscripts as a source of transmission of Islamic teachings in the archipelago, particularly in the field of monotheism. The study aims to examine the textual content of Jawi manuscripts containing the treatise of monotheism, especially the concept of the sentence of monotheism and the attributes of twenty, and to explain their position in the intellectual tradition of Malay Islam. The research employs This research is based on the importance of studying Malay-Jawi religious manuscripts as a source of transmission of Islamic teachings in the archipelago, particularly in the field of monotheism. The study aims to examine the textual content of Jawi manuscripts containing the treatise of monotheism, especially the concept of the sentence of monotheism and the attributes of twenty, and to explain their position in the intellectual tradition of Malay Islam. The research employs a qualitative method with a philological approach and content analysis. Primary data consist of Jawi manuscripts, while secondary data are obtained through library research. Data were collected through documentation and literature review and analyzed descriptively. The findings reveal that the manuscripts contain systematically arranged monotheistic teachings, including the meaning of lā ilāha illa Allāh through the principles of negation and affirmation, as well as the concept of faith involving the heart, speech, and actions. The manuscripts also explain the twenty attributes within the classifications of nafsiyah, salbiyah, ma‘ani, and ma‘nawiyah, reflecting the theological framework of Ahlussunnah wal Jama‘ah. These manuscripts function as both religious texts and pedagogical media, highlighting the importance of preserving Nusantara Islamic manuscripts as part of the region’s intellectual heritage.

This study aims to describe the representation of adolescent conflicts in the film Jumbo directed by Ryan Adriandhy and to analyze its relevance as teaching material for review text learning in vocational high schools. This research employed a descriptive qualitative approach using content analysis. The data consisted of dialogues, scenes, character expressions, plot, and cinematic elements representing adolescent conflicts in the film. Data sources included the film Jumbo, interviews with Indonesian language teachers and eleventh-grade students at SMK PGRI 1 Surakarta, and review text learning documents. Data were collected through observation, documentation, and semi-structured interviews, then analyzed using the Miles and Huberman model. Stuart Hall’s representation theory was used as the analytical framework. The findings show that the film represents identity, family, friendship, and social conflicts experienced by adolescents. These conflicts are constructed through dialogues, visuals, character relationships, and plot development. Furthermore, the film is relevant as teaching material for review text learning because it helps students understand story elements, increases learning engagement, and develops analytical, evaluative, and critical thinking skills. However, students still need guidance in distinguishing summaries from evaluations in review texts.

This study was motivated by the phenomenon of increasing anti-feminism and digital misogyny in Germany, where constitutionally guaranteed equality has suffered a practical setback in the form of a decline in female representation in the Bundestag to 32,4%. This situation has triggered an urgent need for persuasive communication through state addresses. This study aims to analyze the emotional rhetoric (pathos) and intonation strategies in the Ansprache zum Welt-Frauentag in order to raise audience awareness. The method used is a mixed method of qualitative analysis of rhetoric by Aristoteles (2007) and quantitative digital acoustic analysis using Praat software supported theory by Kohler (1995). The data for this analysis are transcripts and audio recordings of the Ansprache zum Welt-Frauentag delivered by Bundespräsident Steinmeier. The results of the study identified three typologies of relationships between diction and intonation, namely contradiction, in which emotional diction is delivered in a flat tone to maintain objectivity; compensation, in which neutral legal references are given emotional weight through a rise in tone (late peak); and parallelism, which is the harmony between emotional diction and the highest voice frequency to build strong authority. Thus, this study confirms that the effectiveness of persuasion in a speech does not only depend on the text, but also on the flexibility of manipulating voice frequency, which strategically functions as an instrument for navigating the audience’s emotions.

This study aims to describe the intrinsic structure and analyze the psychological aspects of the main characters in the three short stories based on Sigmund Freud's psychoanalytic theory. The research uses a qualitative approach with descriptive methods. Data sources are texts from three satua cutet in the Nimbang Rasa anthology: I Kucil by I Komang Tri Nanda Defhayana, Idup Lara by Ni Wayan Esa Juliantini, and Pajalan Keneh by Ni Putu Ayu Ari Astiti. Results show that the intrinsic structure of all three stories employs a linear plot, with themes of diligence, perseverance, and courage. Psychological analysis based on Freud's id, ego, and superego theory reveals that I Kucil is dominated by id impulses in the form of aggressive emotion due to injustice; Ani demonstrates a strong ego in withstanding social pressure and a firm superego in upholding moral values; while Gékyu shows a balance between ego and superego when facing abuse from a teacher. This research is expected to serve as a reference in Balinese literary studies, particularly in the field of literary psychology.

This study examines the contribution of Anselmus dari Canterbury in addressing the tendency of anti-intellectualism in certain theological practices that separate faith from reason. The background of this research lies in the growing assumption that faith does not require philosophical reflection, which often leads to subjective and uncritical interpretations of sacred texts. The objective of this study is to analyze Anselm’s concept of fides quaerens intellectum as a framework for integrating faith and reason. This research employs a qualitative method with a philosophical and hermeneutical approach, focusing on textual analysis of Anselm’s works and relevant scholarly literature. The findings show that Anselm does not oppose faith and reason but emphasizes that faith naturally seeks understanding. This integration prevents arbitrary interpretations and strengthens the rational foundation of belief. The study implies that Anselm’s thought remains relevant for contemporary theological discourse, especially in promoting a balanced and critical understanding of faith in modern religious contexts.

This research critically investigates the circulation of disinformation concerning the instability of fuel prices on the digital platform X and its subsequent implications for the polarization of modern society. In an era where unverified economic news frequently dictates public reaction, fake news often acts as a potent catalyst for mass anxiety. By implementing a quantitative framework driven by lexicon-based computational sentiment analysis, this study effectively processed a dataset of 500 public opinion samples extracted via Google Colab spanning from April 2024 to April 2026. To ensure computational accuracy and eliminate textual noise, the data underwent a rigorous preprocessing phase encompassing case folding, alongside the systematic removal of URLs, account mentions, numbers, hashtags, and punctuation marks. The statistical outcomes revealed a highly disproportionate emotional landscape, overwhelmingly dominated by 451 negative reviews. In stark contrast, neutral observations and positive affirmations were nearly absent, recording only 40 and 9 instances, respectively. The data compellingly illustrates that the relentless influx of pessimistic narratives regarding economic instability directly induces financial panic, undermines rational discourse, and severely fragments cyberspace into deeply polarized factions.

This study aims to develop a sentiment classification method for YouTube user comments related to the game Love and Deepspace using the Naïve Bayes algorithm, focusing on improving the text data processing and understanding user perceptions. Comment data were collected through scraping from YouTube videos, followed by preprocessing including text cleaning, normalization, stopword removal, stemming, and translation into English. Initial labeling was conducted using TextBlob, then the data were randomly sampled for training the Naïve Bayes model. Evaluation involved comparing sentiment distributions and visualization using Word Cloud and bar charts. The Naïve Bayes model achieved an accuracy of 77.36% in sentiment classification. The sentiment distribution shows differences between TextBlob (positive: 1,011, neutral: 1,312, negative: 575) and Naïve Bayes (positive: 901, neutral: 1,627, negative: 370), with Naïve Bayes being more conservative. The Word Cloud visualization identifies dominant words such as "bang," "game," and "main," while the bar chart shows the largest proportion of neutral sentiment. Naïve Bayes is effective for sentiment classification on informal comment data, with significant differences from rule-based methods like TextBlob. This research contributes to the development of text data processing techniques and user perception analysis, as well as opening up optimization opportunities with other algorithms like SVM for better accuracy.

Social media has evolved into a significant platform where consumers freely express their opinions, experiences, and levels of satisfaction regarding various products, including those offered by Micro, Small, and Medium Enterprises (MSMEs). The comments and reviews shared by customers on these platforms contain diverse sentiments that can serve as valuable indicators of how consumers perceive product quality. Understanding these sentiments is crucial for MSME owners, as it allows them to evaluate their products and adapt to market expectations more effectively. This study aims to analyze customer sentiment toward MSME products on social media by utilizing the Naïve Bayes algorithm, a widely used classification method in text mining. The data used in this research consist of customer comments collected from various social media platforms. The research process involves several stages, including data collection, manual labeling of sentiments, text preprocessing (such as tokenization, case folding, and stopword removal), and splitting the dataset into training and testing subsets. Subsequently, the classification process is carried out using the Naïve Bayes algorithm to categorize sentiments into positive, negative, and neutral classes. The results of this study demonstrate that the Naïve Bayes method is effective in classifying customer sentiments with a satisfactory level of accuracy. These findings provide a comprehensive overview of consumer perceptions regarding the quality of MSME products. Furthermore, this research is expected to assist MSME business owners in understanding customer feedback more systematically and using it as a basis for improving product quality and enhancing customer satisfaction in a competitive digital marketplace.

This research is motivated by the low skills of students in writing speech texts, especially in the aspects of structure, idea development, and language use. This study aims to describe the application of infographic media, analyze the differences in writing abilities between the experimental and control classes, and determine student responses. The study used a quantitative approach with an experimental design at SMP Negeri 55 Surabaya. The research sample consisted of class VIII B as the experiment and VIII A as the control. Data collection techniques included observation, pretest and posttest tests, and questionnaires. Data analysis used descriptive statistics and independent sample t-test. The results showed that the application of infographic media ran very well. There was a significant difference in the ability to write speech texts between the two classes, with an average posttest score of 78.2 for the experimental class and 69.5 for the control class (sig. 0.000 <0.05). Student responses were also in the very good category. Thus, infographic media is effective in improving students' speech writing skills.