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A crucial issue currently facing the Church is its stance on building good and harmonious relationships with all other belief systems within its own community. Although dogmatically the Church explicitly expresses its openness to all religious practices and piety lived out by the faithful according to their traditional beliefs, the Church remains cautious so that in building relationships through dialogue, the traditions of the Church are not lost. This paper aims to highlight the relationship between the Catholic faith and the traditional belief system of the Manggarai people in light of the dogmatic constitution Nostra Aetate. Nostra Aetate is one of the key documents resulting from the Second Vatican Council that addresses the current reality of religious diversity. Through this document, the Church seeks to declare its commitment to reflecting on the fact that all humanity shares a single origin and a single ultimate goal: God Himself. The beliefs or religious system of the Manggarai people is implicitly monotheistic, namely Mori Jari dedek Ema pu’un kuasa. On this basis, the Church sees that the same concept exists in Catholic doctrine. In this study, the author uses qualitative research with a text analysis approach. Text analysis is a methodology in qualitative research that aims to interpret and analyze texts based on their context.

The study critically examines the Gospel of John as a theological text integrating the incarnate Logos and sacramental symbolism. Using a qualitative, library-based approach with historical-critical and hermeneutical analysis, it shows that John’s Gospel not only narrates Jesus’ life but constructs a symbolic, existential, and transformative theological reflection. The Logos shifts from a metaphysical principle to a personal reality in history through the incarnation, enabling a concrete encounter between God and humanity. Within this framework, symbols such as water and bread function as media of revelation guiding individuals toward a living, relational faith experience. Furthermore, sacramental symbolism in the Gospel of John is ontologically grounded in the incarnation of the Logos. Faith is thus understood not merely as intellectual assent but as participation in divine life that transforms human existence. The integration of Christological and sacramental dimensions indicates that faith is holistic, encompassing spiritual, material, and relational aspects. Thus, the Gospel of John presents a theological vision of faith as a concrete and dynamic existential encounter, relevant for contemporary theological reflection.

Document-level sentiment analysis assigns a single polarity label to an entire review, often obscuring opinion diversity within multi-sentence submissions. This limitation is particularly evident in reviews of multi-service platforms, where users frequently express heterogeneous opinions toward different aspects of the platform in the same review. To address this challenge, this study proposes a sentence-level sentiment analysis framework for Indonesian Gojek app reviews collected from the Google Play Store. The proposed framework introduces a two-stage segmentation strategy that combines punctuation-aware rules with conjunction-aware splitting based on coordinating and adversative conjunctions (e.g., tapi [but], padahal [even though]) to identify opinion boundaries and decompose mixed-sentiment reviews into independently classifiable sentence units. A total of 14,730 raw reviews collected between May and July 2025 were subjected to data cleaning and quality filtering, resulting in 7,187 valid reviews that were further segmented into 14,187 sentence-level instances. Each instance was manually annotated by three annotators using a four-class labeling scheme consisting of app-positive, app-negative, app-neutral, and service categories. Sentiment-level inter-annotator agreement, computed on the subset of instances unanimously categorized as app-related by all three annotators (n = 4,384), achieved substantial agreement (Fleiss'  = 0.636). Hyperparameter optimization was conducted using Optuna with the Tree-structured Parzen Estimator (TPE) sampler across four experimental scenarios. The best performance was achieved by IndoBERTweet under Stratified K-Fold evaluation, attaining an accuracy of 0.751 and a macro F1-score of 0.729, outperforming all IndoBERT configurations. The results demonstrate the effectiveness of domain-adaptive pre-training on informal Indonesian text and highlight the value of conjunction-aware segmentation for preserving fine-grained opinion structures in mixed-sentiment reviews. These findings suggest that domain-aligned language representations provide a practical and effective solution for sentence-level sentiment analysis of Indonesian app reviews.

This study aims to analyze in-depth cognitive learning theory and its implications for improving the effectiveness of Islamic Religious Education (PAI) teaching at the elementary school level. The adopted methodological approach is qualitative through library research, with the application of content analysis and theoretical synthesis techniques. Primary data sources were obtained from academic literature including textbooks, scientific journals, and the latest empirical research relevant within the last decade. The results of the analysis indicate that cognitive learning theory positions cognitive processes as the core of learning activities, allowing students to function as active agents in constructing knowledge through understanding, analysis, memory representation, and integration with pre-existing cognitive schemas. In the context of PAI, this approach enables meaningful learning through interactive pedagogical strategies such as collaborative discussions, problem-solving, case studies, and metacognitive reflection, which are empirically adapted to the stages of students' cognitive development. The implementation of cognitive theory significantly contributes to strengthening conceptual understanding, increasing learning engagement, developing critical thinking skills, transferring knowledge, and internalizing Islamic values. Thus, cognitive learning theory makes a substantial contribution to optimizing the effectiveness of PAI teaching in elementary schools through an interactive, systematic, and understanding-oriented approach.

This research examines the potential application of the Right to Be Forgotten (RTBF) in Indonesia's criminal justice system, focusing on individuals who have been acquitted or have completed their sentences. The study explores the legal, social, and ethical implications of RTBF in relation to criminal records, aiming to assess how it could support the rehabilitation and reintegration of acquitted individuals. In Indonesia, criminal records often remain publicly accessible long after a person has been legally exonerated, creating barriers to social reintegration due to the stigma associated with past accusations. The study investigates the gaps in the current legal framework, such as the lack of provisions for the removal or anonymization of criminal records for acquitted individuals, and explores how RTBF could promote justice and fairness. The research uses a literature review methodology, analyzing relevant legal texts including Law No. 11 of 2008 on Information and Electronic Transactions (ITE Law), Law No. 39 of 1999 on Human Rights, and the 1945 Indonesian Constitution. The review critically evaluates the challenges and opportunities of implementing RTBF, focusing on balancing privacy rights with public safety concerns. The findings suggest that RTBF could reduce the negative impact of criminal records on individuals who have been acquitted, facilitating their reintegration into society. However, the study also highlights the challenges in implementing RTBF due to societal and legal factors. Legal reforms recommendations allow individuals to request the removal of criminal records, aligning Indonesia's legal system with international human rights standards.

Batik motif classification has attracted growing attention in visual computing due to its role in cultural heritage preservation, textile informatics, museum documentation, and automated cataloging. Although many studies report high classification accuracy, robustness under real-world acquisition conditions remains insufficiently understood. Batik images are frequently affected by illumination variation, blur, folds, watermark overlays, wearable deformation, scale inconsistency, and background clutter, creating challenges that extend beyond conventional image-noise assumptions. Existing studies largely focus on improving classification performance, while the interactions among acquisition variability, feature representation, evaluation practice, and deployment constraints remain fragmented. This systematic literature review addresses this gap by synthesizing batik classification research through a robustness-aware perspective. Using query expansion, backward and forward citation chaining, relevance screening, and thematic coding, 116 candidate records were identified, resulting in 50 highly relevant studies for detailed analysis. The review reveals that robustness is shaped less by denoising alone than by the combined effects of acquisition conditions, representation design, evaluation realism, and deployment context. Handcrafted descriptors remain competitive for small datasets and structured motifs due to their data efficiency and interpretability, whereas deep learning models achieve the highest reported accuracy when supported by sufficient data diversity and realistic augmentation. Hybrid representations emerge as the most consistently balanced approach, combining local texture stability with higher-level abstraction across heterogeneous acquisition settings. The review further identifies recurring robustness failure patterns, including background dependency, illumination instability, motif-scale inconsistency, wearable deformation, and source-shift vulnerability. Based on these findings, a robustness-oriented research agenda is proposed, emphasizing cross-acquisition evaluation, representation-stability analysis, batik-specific robustness benchmarks, acquisition-aware augmentation, and deployable lightweight or hybrid architectures. The study contributes a domain-specific synthesis that reframes batik motif classification from an accuracy-centric task toward a robustness-aware visual recognition problem.

The Free Nutritional Meal Program (MBG) is a government policy that is widely discussed by the public through social media, especially TikTok. Various comments that have emerged indicate differences in public opinion towards the program, so an analysis is needed to determine the tendency of public sentiment. This study aims to analyze TikTok user sentiment towards the Free Nutritional Meal Program using the Naive Bayes method. The research method is carried out through several steps, namely collecting TikTok comment data, preprocessing text, labeling sentiment data into positive, negative, and neutral, feature transformation using TF-IDF, and classification using the Naive Bayes algorithm. Based on the analysis of 500 comment data, the results show that positive sentiment dominates public opinion by 42% (210 data), followed by negative sentiment by 36% (180 data), and neutral sentiment by 22% (110 data). Testing the classification model using Naive Bayes produces excellent performance with an accuracy rate of 86%, precision of 84%, recall of 85%, and F1-score of 84%. The conclusion of this study shows that the Naive Bayes method is effective as an approach in social media sentiment analysis to map public responses to government policies.

Nature tourism is one of the sectors that plays an important role in supporting the development of regional tourism, including in Lahat Regency, which has significant waterfall tourism potential. Currently, many visitors share their reviews and experiences through digital platforms such as Google Maps. This review can be used as a source of information to understand the public's evaluation of the quality of tourist attractions. This study aims to examine public perception of tourist attractions in Lahat Regency using the Support Vector Machine (SVM) method. Research data were collected through scraping from Google Maps, totaling 500 reviews from five tourist attractions, namely Curup Maung, Curup Buluh, Senyawe Waterfall, Panjang Waterfall, and Green Canyon. The research stages include data preprocessing, consisting of cleaning, case folding, normalization, tokenization, stopword removal, and stemming. After that, feature extraction was carried out using the TF-IDF method and the classification process using the SVM algorithm. Based on the research results, the Support Vector Machine (SVM) method is able to perform sentiment classification quite well, although the accuracy level varies for each tourist attraction. Curup Maung and Panjang Waterfall achieved the highest accuracy level of 90%. Nevertheless, most visitor reviews were dominated by negative sentiments. This indicates that there are still several aspects that need to be improved, particularly related to tourist facilities and services. This research is expected to serve as a consideration for tourism managers and local governments in efforts to improve management quality as well as the development of tourism in Lahat Regency.

Education is the main driver of a nation's progress, which is not only related to intellectual intelligence, but also the formation of student character. Unfortunately, currently the world of education is facing serious problems, such as declining interest in learning and increasingly complex external factors in the digital era. This study aims to explore in depth the impact of learning motivation on student academic achievement, identifying obstacles that hinder this achievement. The method used in the current study is a literature study with a qualitative approach. Data were collected through theoretical studies and analysis of various relevant scientific sources, including research journals and textbooks, which were then combined to develop a comprehensive argument. The findings of this study indicate that learning motivation is the main factor that encourages student persistence and consistency in achieving the best learning. Academic success is defined as the result of a complex interaction between internal and external factors, where the existence of a supporting ecosystem such as good learning facilities, a supportive environment, and effective communication between lecturers and students plays a very important role. On the other hand, phenomena such as digital fatigue, low independence, and excessive workload are identified as significant barriers that can reduce academic performance. This analysis concludes that there is synergy in strengthening psychological aspects and creating an adaptable learning environment to maintain the stability of academic achievement amidst global demands.

This study aims to determine the production process and the level of consumer acceptance of Bandung nagasari cake utilizing stevia as a natural sweetener and butterfly pea flower extract (Clitoria ternatea) as a natural coloring agent. The research employed a quantitative approach with an experimental method through organoleptic testing involving 20 panelists. Data collection techniques consisted of literature review, questionnaires, and organoleptic evaluation covering taste, texture, aroma, and appearance. The obtained data were analyzed using descriptive quantitative analysis with percentage calculations. The findings indicated that the Bandung nagasari formulation containing stevia and butterfly pea flower extract was more preferred than the formulation using granulated sugar. Approximately 90% of panelists preferred the taste attribute due to its healthier perception, while 80% of panelists favored the texture, aroma, and appearance attributes because of the softer texture and the attractive natural coloration produced by the butterfly pea flower extract. Furthermore, the product demonstrated a shelf life of 12 hours at room temperature and up to 3 days under refrigerated storage conditions. The study concludes that the incorporation of stevia and butterfly pea flower extract may serve as an innovative development of traditional Bandung nagasari cake without eliminating its traditional characteristics and shows favorable consumer acceptance.

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.

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.

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

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.

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.

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 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 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 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.