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Judicial digital transformation through the implementation of e-Court and e-Litigation represents a strategic initiative of the Supreme Court of Indonesia to establish a modern, effective, and efficient judiciary. These innovations have successfully transformed various stages of civil case administration and litigation into faster and more transparent processes. However, the success of digitalization in case examination has not been accompanied by similar reforms in the execution of court judgments. This study aims to analyze the regulation of civil judgment execution following the implementation of e-Court and e-Litigation, identify challenges encountered in practice, and formulate a concept of execution digitalization as part of judicial reform. This research employs a normative legal method using statutory, conceptual, and case approaches. The legal materials consist of primary legal sources in the form of legislation and Supreme Court regulations, as well as secondary legal sources including scholarly literature and previous studies. The findings reveal that although e-Court and e-Litigation have accelerated dispute resolution processes, the execution of civil judgments remains largely conventional. Consequently, several challenges persist, including delays in execution, lack of transparency, and limited supervision by litigating parties. Therefore, the development of an integrated Digital Execution System linked to existing electronic judicial platforms is necessary to enhance the effectiveness of judgment enforcement and ensure greater legal certainty for justice seekers.

This study explores how Generation Z in Indonesia produces and spreads narratives of micro-interfaith harmony through the TikTok platform. Amid growing social polarization in digital spaces, casual and personal tolerance content created by Gen Z offers a new approach to building social cohesion. Using a qualitative netnography method, this research observes 20 viral videos with over 10,000 views during the 2024-2025 Ramadan period, including the War Takjil trend and the #LoginLintasIman campaign, as forms of affective digital citizenship. The findings reveal that TikTok’s algorithmic logic, driven by emotional engagement, allows grassroots narratives of tolerance to reach broad audiences organically. Within this ecosystem, values of pluralism and solidarity are not shaped by formal institutions but emerge from the participatory dynamics and digital habitus of Gen Z. This study concludes that a new form of digital interfaith citizenship is emerging, termed algorithmic harmony, where tolerance is fostered through affective interactions, viral distribution, and the everyday media practices of youth. The findings provide new insights for media studies, diversity education, and digital tolerance discourse.

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.

The development of Artificial Intelligence (AI) technology has created various digital innovations, but it has also generated new forms of crime through the misuse of deepfake technology. This study aims to analyze the legal liability of social media platforms for the dissemination of AI-based deepfake content and the forms of legal protection for victims of digital fraud, particularly elderly groups, in the case of the “magical money ritual” scam using the identity of Ujang Busthomi. This research employs normative legal research methods using statutory and case approaches. The results show that perpetrators of deepfake fraud can be held criminally liable under Article 28 paragraph (1) in conjunction with Article 45A paragraph (1) of the Electronic Information and Transactions Law and Article 378 of the Indonesian Criminal Code concerning fraud. In addition, social media platforms as Electronic System Providers also bear preventive and repressive responsibilities under the ITE Law, Government Regulation on Electronic Systems and Transactions, and the Personal Data Protection Law to prevent the spread of illegal content. Legal protection for victims is carried out through criminal law enforcement, personal data protection, restitution mechanisms, and the enhancement of digital literacy in society.

This study aims to develop and evaluate the feasibility of Teknoran, a website-based digital learning medium developed through the Canva application for the Office Technology subject in Grade X at SMKN 31 Jakarta. The study was motivated by learning activities that were still dominated by lecture methods and limited learning media. Teknoran was designed to facilitate learning through integrated features such as learning materials, assignments, and educational games within a single platform. This research employed the Research and Development (R&D) method using the ADDIE model, which consists of analysis, design, development, implementation, and evaluation stages. Data were collected through observation, needs analysis questionnaires, validation sheets from material and media experts, and student response questionnaires during one-to-one and small group trials. Data were analyzed using descriptive quantitative and qualitative techniques. The results showed that the Teknoran website achieved a feasibility score of 95% from material experts and 93.33% from media experts, both categorized as “Very Feasible.” Student responses also indicated a very high level of acceptance, with an average score of 95.68%. These findings demonstrate that Teknoran is highly feasible as a digital learning medium and can support more interactive, flexible, and independent learning in Office Technology subjects at SMKN 31 Jakarta.

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.

This study explores Indonesian EFL student teachers’ experiences of learning to write news articles through blogging in a Journalism course. The study was motivated by the need to provide meaningful and genre-based writing practice for EFL student teachers, who often face difficulties in generating ideas, organizing information, meeting genre expectations, and writing for authentic aences. Employing a qualitative case study design, the study involved twenty undergraduate student teachers from a private Islamic university in Tangerang, Indonesia; all participants completed an online quetaire, and five volunteers joined follow-up semi-structured interviews. The questionnaire data were alysed descriptively, while the interview data were examined thematically. The findings indicate that blogging supported regular writing practice, helped students understand news text structure, enouraged clearer organization of headlines, leads, and supporting details, and increased engagement by allowing students to publish and share their work. Blogging also promoted peer interaction and selfevaluation because students could read, compare, and comment on one another’s posts. However, some paticpants eprienced challenges related to technical blogging skills, uncertainty about writing standards, limited peer feedback, and discomfort with public online publication. The study concludes that blogging can serve as a useful platform for EFL news writing when it is supported by explicit istrucion, relevant tasks, clear assessment criteria, structured feedback, and ethical awareness in using digital and AI-driven tools.

This study analyzes netizen sentiment concerning the 17+8 public aspirations circulating the digital platform X spanning the period from August 18 through October 31, 2025. 1,837 comments obtained through scraping method. Classification Research stages include data preprocessing, sentiment weighting based on lexicon, and feature extraction using TF-IDF. Data 80% used for learning purposes and the remaining 20% utilized for validation. The findings reveal that the majority of comments, amounting to 81.14%, contained negative sentiment, while the remaining 18.86% were positive. The outcomes demonstrate that community reactions toward the 17+8 People's Demands were dominated by unsupportive views. From a theoretical standpoint this scholarly work offers to enriching knowledge concerning public opinion classification on political issues through a computational approach, while also serving as a reference for future research focused on improving the accuracy of sentiment analysis related to political dynamics and the behavior of state institutions.

The presence of Generation Z, who grew up entirely in the digital era, has triggered a fundamental transformation in Muslim religious practices, where social media has replaced conventional religious institutions as the primary source of religious information. This condition creates a paradox: Gen Z has become a generation that is highly religious online, yet is often disconnected from physical communities and traditional religious authorities. This study aims to analyze the character of Muslim Gen Z religiosity, identify its forming factors, and examine the impact of the digital era on their religiosity. This study employed a descriptive qualitative approach using a library research method. The findings indicate that Muslim Gen Z religiosity is characterized by personalization, flexibility, and digital spirituality, strongly influenced by social media. These characteristics are shaped by massive digital technology accessibility, the effectiveness of contextual Islamic preaching on platforms such as TikTok, as well as spiritual needs and social pressure from the digital environment. The digital era brings positive impacts in the form of increased accessibility and religious literacy, but also negative impacts including shallow religious understanding, vulnerability to information bias, and potential exposure to extreme ideologies. This study implies the importance of an integrated digital religious literacy strategy through critical thinking-based Islamic Religious Education curriculum reform, enhancement of educators' digital capacity, and cross-sector collaboration to strengthen Gen Z's moderate and reflective religious understanding.

Game collaboration branding has emerged as a significant marketing strategy influencing player purchasing behavior. This study aims to analyze the influence of collaboration branding, player satisfaction, and emotional attachment on purchase intention among Honkai: Star Rail players. 280 active members of the Pompomfess community on Platform X (Twitter) participated in a quantitative survey that was analyzed using Structural Equation Modeling–Partial Least Squares (SEMPLS). The results extend the application of the Theory of Planned Behavior to digital game consumption behavior by showing that collaboration branding, player satisfaction, and emotional attachment significantly increases purchase intention. In addition to offering useful advice for game developers and marketers in creating player-oriented brand collaboration strategies, this study conceptually advances the body of knowledge on digital consumer behavior.

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.

Social media X (Twitter) has become the main platform for the Indonesian public to express opinions, including on the trend of 'kabur aja dulu' (let's just run away for a bit). This research aims to classify the sentiments of the public using the Naïve Bayes and Support Vector Machine (SVM) methods, and to compare the accuracy of both in sentiment analysis. Data was collected via the Twitter API with the hashtag #kaburajadulu, resulting in 2,067 tweets, which, after the cleansing process and manual labeling, left 385 data points. The analysis process followed the CRISP-DM stages, which include business understanding, data understanding, data preparation, modeling, evaluation, and deployment. Model evaluation was conducted using a confusion matrix with accuracy, precision, and recall metrics. The classification results show that 82% of tweets have a positive sentiment and 18% negative. The Naïve Bayes algorithm achieved an accuracy of 86.49%, slightly lower than SVM, which reached 88.05%. In conclusion, Support Vector Machine is more effective in sentiment classification on public opinion data. This research contributes to the digital mapping of public opinion and recommends the development of automatic labeling methods as well as the exploration of advanced algorithms in the future.

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.

Technological developments have led to a shift from traditional sales promotion to digital sales promotion. A new phenomenon has emerged, namely the integration of social media platforms and e-commerce. The purpose of this study is to analyze the effect of sales promotion and positive emotions on impulsive buying, as well as the mediating role of positive emotions on TikTok Shop users in Denpasar. The results of this study were obtained using the SEM-PLS method. The results of this study indicate that positive emotions and sales promotions have a significant influence on impulsive purchasing of products on TikTok Shop, sales promotions have a significant influence on positive emotions when purchasing products on TikTok Shop, and positive emotions can mediate the influence of sales promotions on impulsive purchasing of products on TikTok Shop.  

The development of social media has driven changes in digital marketing communication strategies, including within educational institutions such as Juara Academy. This study aims to analyze digital marketing communication strategies through Instagram in increasing student enrollment, identify gaps between social media performance and conversion rates, and formulate efforts to optimize these strategies. This research employs a descriptive qualitative approach using a case study method. Data were collected through interviews, observations, and documentation, and then analyzed using the interactive model of Miles and Huberman, as well as the AIDA framework (attention, interest, desire, action). The results indicate that digital marketing communication strategies have been systematically implemented through the utilization of Instagram features such as feed, story, reels, and live to capture attention, build interest, generate desire, and ultimately encourage enrollment actions. However, a gap was found between high engagement levels and suboptimal enrollment conversions, influenced by technical factors such as inconsistent content posting and a lack of integration across communication stages. Strategic optimization is therefore necessary through strengthening content consistency, enhancing more personalized interactions, and leveraging persuasive approaches based on audience experience. This study provides practical contributions to the management of digital marketing communication as well as theoretical contributions to the development of social media–based marketing communication studies.  

This community service program aims to implement digital marketing management strategies to optimize community based digital businesses among young generations in Jayapura City. The rapid development of digital technology has created significant opportunities for youth to develop digital businesses. however, limitations in marketing knowledge, content creation skills, and the use of digital platforms remain major challenges. This program was conducted through training, mentoring, and practical workshops focusing on digital marketing management, including market segmentation, branding strategy, social media marketing, content planning, digital advertising, and evaluation using digital analytics. The participants consisted of young entrepreneurs and youth communities who are actively involved in small scale digital business activities. The results of the program indicate an improvement in participant’s understanding and skills in managing digital marketing strategies, particularly in building brand identity, optimizing social media engagement, and designing digital promotional content. In addition, participants were able to develop structured digital marketing plans and apply them to their business activities. This program contributes to strengthening youth capacity in Jayapura City to compete in the digital economy through sustainable community based business development.

Reproductive health is an aspect of health that still requires serious attention, particularly for women of reproductive age. Polycystic Ovary Syndrome (PCOS) is a hormonal disorder with a prevalence of 3.4% among women worldwide, yet it often goes undetected due to a lack of awareness. This community service activity aims to increase the knowledge of women of reproductive age regarding menstruation, PCOS, and the importance of early detection through an educational webinar titled “Hormone Talk: Exploring Menstruation, PCOS, and the Importance of Early Detection.” The event was held online via the Zoom Meeting platform on December 13, 2025, and was attended by over 100 participants, the majority of whom were female students. The evaluation method utilized pre-tests and post-tests analyzed using a Dependent T-test. The results showed a significant increase in the average knowledge score from 64.78 (pre-test) to 79.67 (post-test), with a difference of 14.89 points and a p-value of 0.000. This demonstrates that reproductive health educational webinars are effective in enhancing participants’ knowledge. It is hoped that this activity can serve as a model for sustainable digital-based health promotion interventions in efforts to improve reproductive health literacy among Indonesian women.

The escalation of negative sentiment in the digital space towards Rohingya refugees in Indonesia throughout 2023-2026 has reflected a shift in public perspectives, from humanitarian principles to restictive rejection. This study aims to analyze how digital discourse on TikTok dan Instagram platforms frames the Rohingyan refugee issue as a national security threat through the lens of Barry Buzan`s Securitization Theory and Ruth Wodak`s Critical Discourse Analysis (AWK). This study uses qualitative methods with note-taking techniques and filtering hastag-based viral data related to refugee rejection. The results show that the securitization process was successfully driven by three main typologies of netizen narratives: domestic socio-economic jealousy, delegetimization of Internasional authorities (UNHCR) by referring to popular legal discourse on the 1945 Constitution, and demands for an active role for the military (TNI AL) and Polair at maritime borders. The accumulation of speech acts that have gone viral on social media is evidence of the creation of strong horizontal pressure, thus urging the Indonesian goverment to review its policies towards a more restrictive direction (viral-based policy) to prioritize national soverignity and security over global humanitarian commitments.

Usability evaluation plays a crucial role in ensuring the quality of digital systems, particularly in terms of comfort, effectiveness, and ease of use. Instruments such as the System Usability Scale (SUS), User Experience Questionnaire (UEQ), and Heuristic Evaluation (HE) are widely used in modern usability studies. This research conducts a Systematic Literature Review (SLR) to identify patterns and trends in the use of these instruments. A total of 27 initial studies were collected, and 16 were selected through the PRISMA screening procedure. The findings show that UEQ is the most frequently used instrument, especially in Learning Management Systems (LMS) and academic platforms, while SUS is commonly applied to mobile applications and digital libraries for rapid usability assessment. HE is effective in revealing fundamental interface issues such as non-intuitive navigation and layout inconsistencies. Overall, digital systems perform well in Efficiency and Perspicuity, but consistently show low scores in Novelty. This study provides an integrative knowledge map that highlights cross-instrument insights and supports the development of more intuitive, innovative, and user-centered digital systems