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This study aims to develop a financial saving application to improve the saving habits of students, particularly in Islamic boarding schools, through an adaptive challenge approach. The system integrates a mobile iOS application with a backend service and Large Language Model (LLM) processing via Ollama. Transaction data entered by users is processed by the backend to generate contextual and personalized saving challenges, applying Reinforcement Learning concepts in an adaptive and data-driven manner. The research adopts a descriptive quantitative method using surveys and system testing with 50 respondents. Results indicate that the application functions as designed, with no significant bugs detected. User evaluation shows high satisfaction, with an average score of 4.3 out of 5, covering ease of use, interface design, and increased awareness of saving. The combination of gamification, reward systems, and adaptive personalization successfully motivates users to save regularly. This system demonstrates the potential of integrating AI-driven personalization to strengthen financial literacy and healthy financial habits among students in a fun and interactive way.methods, and a summary of the results. The abstract should end with a comment about the significance of the results or conclusions brief.

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.

Early detection of a potential heart attack is a crucial step in preventing sudden death from heart disease. This research aims to develop an Internet of Things (IoT)-based health monitoring system capable of measuring vital body data in real time and predicting the likelihood of a heart attack from CSV data obtained from sensors, integrated through RapidMiner as learning data using a machine learning algorithm, the Support Vector Machine (SVM). The system was built using an ESP32 microcontroller connected to a MAX30102 sensor to measure heart rate and finger oxygen levels (SpO₂), as well as a DHT22 sensor to measure temperature and humidity. The resulting data is sent to the Blynk application to display real-time data according to its parameters. The initial prediction logic was developed using a rule-based method based on medical thresholds for four vital parameters. The data was then used to train an SVM model as a classification system to detect potential heart attacks. Test results showed that the system can identify abnormal conditions with a good level of accuracy and provide early warnings based on changes in vital parameters in real time. This system is expected to be an initial solution for personal health monitoring, especially for individuals at risk of heart disease. It can be further developed with cloud integration and automatic notifications to users' devices.

This study discusses a student concentration detection system using Convolutional Neural Network (CNN) with the MobileNetV2 architecture. The dataset was adapted from Classroom Student Behaviors and mapped into four concentration categories: highly focused, focused, less focused, and unfocused. The system was tested with a 720p webcam and produced real-time detection data. The evaluation results show an overall accuracy of 75.85%, with the highest precision achieved in the focused class (0.9859) and the highest recall in the highly focused (0.9739) and unfocused (0.9811) classes. The confusion matrix indicates that the focused class was detected most consistently, while highly focused and unfocused classes were often misclassified as focused, resulting in lower precision. In real-time testing, the system operated at an average of 7 FPS and worked optimally when students faced the camera directly with sufficient lighting, but its performance decreased significantly at face angles greater than 45°. User evaluation shows that 75% of students rated the detection results as accurate/very accurate with an average satisfaction score of 3.6 out of 5, and 75% felt assisted in recognizing their concentration level. From the teachers’ perspective, most stated that the results were consistent with classroom observations, and all expressed willingness to reuse the system.

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

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 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.

Public service institutions currently utilize digital technology to improve information accessibility for society. Satres PPA and PPO Polresta Banyumas has an important role in providing services, protection, and education related to violence cases against women and children. However, information regarding services, reporting procedures, and educational materials is still not fully accessible to the public. This study aims to design and develop a service and educational website for Satres PPA and PPO Polresta Banyumas using the Laravel framework. The research method used in this study is the Waterfall method, which consists of requirement analysis, system design, implementation, testing, and maintenance stages. The website was developed using Laravel, MySQL, and Bootstrap to support responsive interface design. The results show that the developed website is able to provide service information, educational articles, reporting procedures, and contact information effectively. System testing results indicate that all website features function properly according to user requirements. The website is expected to improve public access to information and increase awareness regarding the protection of women and children.

Public Wi-Fi services (Wifi_STAKat) at the State Catholic College of Pontianak continue to face technical issues, such as network speed and connection stability, as well as non-technical challenges, including the responsiveness of administrators to user complaints. This study aims to analyze user satisfaction based on the five Servqual dimensions and to map improvement priorities using the Importance–Performance Analysis (IPA) method. The relationship between Servqual and IPA is explained by mapping GAP values (perception–expectation) into the four IPA quadrants to determine the urgency level of service improvements. A 4-point Likert scale was used to avoid neutral responses and strengthen the clarity of respondents’ perceptions. The results show that all dimensions have negative GAP scores, particularly responsiveness and reliability, which are directly related to technical indicators (speed, stability, coverage) and non-technical factors (responsiveness to complaints, ease of access. The study recommends integrating an IT-based monitoring system and increasing network capacity to improve service quality.

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

The rapid digitalization in Indonesia, reaching 180 million active social media users, has not been accompanied by adequate security system resilience, thereby triggering massive data breach risks. This study aims to analyze the privacy navigation mechanisms of the digital society as an instrument for mitigating information leaks. The method used is descriptive qualitative with a literature study (library research) approach, which examines primary and secondary literature related to regulations, digital behavior, and user psychological factors. The research findings indicate that privacy navigation in the digital era has not operated optimally due to the dominance of social existence needs, which triggers the privacy paradox phenomenon. Although users possess knowledge regarding cyber risks, the desire for social validation through self-disclosure often overrides technical protection logic. The practice of using secondary accounts (second accounts) was found to be a form of manual navigation, yet its effectiveness remains dependent on individual digital literacy. The implications of this research emphasize that mitigating information leaks requires the integration of critical user awareness, platform governance transparency, and consistent law enforcement through the PDP Law. Digital awareness must transform into reflexive protective behavior to maintain informational sovereignty in cyberspace.  

The increasing complexity of urban transportation systems demands intelligent and measurable navigation methods. Medan City, the capital of North Sumatra Province, has a dense road network with multiple route options that often confuse road users. Dijkstra's Algorithm, developed by Edsger Wybe Dijkstra in 1959, is a greedy-based computational approach proven effective for solving the shortest path problem on non-negative weighted graphs. This study applies Dijkstra's Algorithm to determine the shortest route from Medan Railway Station to Universitas Negeri Medan (UNIMED). The road network was modeled as an undirected weighted graph with 15 nodes and 16 edges, where edge weights represent actual road distances measured via Google Maps. The graph has a density of 0.152, confirming its sparse graph characteristic. Three alternative routes were identified and analyzed. The algorithm was implemented in Python 3 using the heapq module as a priority queue. Results show that the optimal route is A → B → C → E → F → M → N → O via Jl. M.T. Haryono, Jl. Aipda KS Tubun, Jl. Madong Lubis, and Jl. Prof. H.M. Yamin, with a total distance of 6.64 km. This achieves 99.1% accuracy compared to Google Maps, with a deviation of only 0.06 km. The optimal route is 6.25% more efficient than Alternative Route 1 (7.30 km) and 11.9% more efficient than Alternative Route 2 (7.54 km). The algorithm executes in under 1 millisecond with time complexity O((V+E) log V). These findings confirm Dijkstra's Algorithm as highly effective for medium-scale urban road network optimization.

This study is motivated by the dynamic changes in digital consumer behavior in e-commerce, particularly in the purchase of electronic products through the Tokopedia platform. The study aims to analyze and synthesize the influence of online reviews and product ratings on purchase decisions using a systematic literature review approach. The method involves a comprehensive examination of global and local scientific literature, with a focus on peer-reviewed journals and empirical studies published between 2021 and 2026. The findings indicate that online reviews and product ratings are capable of reducing consumer uncertainty as well as the functional risks associated with electronic products. Empirical evidence over the past decade suggests that high ratings can build initial confidence in brand quality, while detailed reviews provide crucial technical validation for potential buyers. Overall, these two indicators work synergistically to strengthen consumer trust and serve as key determinants in the final stage of the purchasing decision. The implications suggest that e-commerce platforms and sellers need to prioritize the management of user-generated content and maintain transparency in reputation to remain competitive in a market increasingly reliant on the credibility of online information.

This study aims to analyze the implementation of the Bravo PUPR Online attendance system in improving the work discipline of Civil Servants (ASN) at the National Road Implementation Center of East Nusa Tenggara. The research focuses on punctuality of attendance, consistency in fulfilling working hours, task completion, compliance with attendance procedures, alignment between recorded data and actual conditions, and acceptance of consequences for violations. The study employs a qualitative approach with data collection techniques including observation, documentation, and interviews with leaders, system operators, and ASN as direct users. The results indicate that the implementation of the Bravo PUPR Online attendance system has improved administrative order in attendance and increased ASN awareness of time discipline. However, work discipline has not been fully optimal, as several issues remain, such as inconsistent attendance, suboptimal fulfillment of working hours, and discrepancies between attendance data and actual field conditions. In addition, technical constraints such as internet connectivity and GPS accuracy, as well as behavioral factors like negligence and lack of supervision, also affect system effectiveness. In terms of compliance, the Bravo system integrated with e-HRM is considered effective in detecting violations and enforcing strict consequences, ranging from warnings to administrative sanctions. This demonstrates that the attendance system functions not only as a recording tool but also as an instrument for monitoring discipline.

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.

The swift proliferation of short-video-centric social media, notably TikTok, has revolutionized the educational landscape by facilitating novel methods of knowledge production, dissemination, and interpretation. This phenomenon denotes a transition in media and signifies an epistemological transformation in educational practices within the digital age. This study seeks to analyze the representation and interpretation of knowledge in TikTok educational content using a qualitative methodology grounded in an interpretive case study framework. Data were gathered via digital participant observation, comprehensive interviews, and document analysis involving 12 participants, comprising educational content creators and active TikTok users in higher education settings. Thematic data analysis was performed utilizing a Multimodal Critical Discourse Analysis framework to elucidate the interplay among visual, verbal, and auditory components in the construction of meaning. The results show three main patterns: the conflict between quick understanding and deep knowledge, the importance of emotional multimodal experiences in learning, and the negotiation of knowledge authority in changing digital spaces. These results indicate that learning via TikTok encompasses not only cognitive aspects but also intricate emotional, aesthetic, and social dimensions. This study theoretically enhances multimodal discourse analysis by integrating users' subjective experiences, while practically informing the advancement of critical digital literacy and the design of social media-based learning. Moreover, this study facilitates additional investigation into algorithmic dynamics, digital identity, and the evolution of learning methodologies within platform-centric contexts.

This study aims to understand the role of E-Commerce in building customer loyalty, explain the process of loyalty formation, and assess how customers perceive service quality in relation to loyalty, using the GoFood service in Jombang as a case study. The rapid growth of online food delivery services has intensified competition, making customer loyalty a critical factor for sustainability. Employing a qualitative case study approach, data were collected through in-depth interviews with 30 GoFood customers and several drivers in the Jombang area. The findings reveal that E-Commerce platforms significantly enhance loyalty through user-friendly application interfaces, supporting features (such as live tracking, history, and digital payments), and beneficial promotions. The loyalty formation process occurs gradually through consistent positive experiences, moving from customer satisfaction to trust, and ultimately to habitual use. Crucially, the quality of driver services—including politeness, effective communication, punctuality, and order accuracy—emerged as a key determinant of customer comfort and repeat orders. This study implies that for E-Commerce platforms to maintain a competitive edge, strategies must integrate digital convenience with consistently reliable human interactions. The research contributes empirical insights from a semi-urban Indonesian context, highlighting that customer loyalty is not merely transactional but is built on a combination of technological ease, economic value, and positive interpersonal service experiences.

This study aims to analyze the level of student satisfaction in paying tuition fees (UKT) through the BRImo application at Universitas Negeri Gorontalo. The research applies the service quality (SERVQUAL) framework, which consists of five dimensions: tangibles, reliability, responsiveness, assurance, and empathy. A qualitative descriptive approach was employed, with data collected through in-depth interviews, direct observations, and documentation. Participants included students who actively use the BRImo application for UKT payments as well as staff responsible for tuition management. Data analysis followed a systematic process involving data reduction, data display, and drawing conclusions to ensure accuracy and clarity of findings. The results indicate that using the BRImo application for tuition payment offers significant convenience and efficiency, allowing students to complete transactions without physically visiting the university payment office. Despite these benefits, several challenges were identified, including failed transactions, delays in payment confirmation, and suboptimal responses from customer service staff. Overall, the study concludes that student satisfaction with tuition payments via BRImo is considered fairly good. Nevertheless, improvements in reliability and responsiveness are recommended to further enhance the user experience and optimize the quality of service provided.  

Advances in information and communication technology have encouraged governments to integrate digital systems into public service delivery through e-government initiatives. In Riau Province, the Riau application at the Soeman H.S. Library exemplifies this implementation. This study examines how the Riau application enhances the effectiveness and efficiency of public services within the library. Using a descriptive qualitative method, data were collected through interviews, observations, and documentation. Findings show that the application provides convenient online access to library information and services. However, challenges remain, including limited financial resources, dependence on external parties, and insufficient feature development to meet user needs fully. These findings underscore the importance of strengthening institutional capacity, improving infrastructure, and formulating strategic plans to sustain digital library services. In line with Indonesia’s 1945 Constitution, regional governments possess autonomy to manage local affairs, aiming to improve public welfare through enhanced services, community empowerment, and civic participation while promoting regional competitiveness with attention to equity and democratic values. Following Presidential Instruction No. 3 of 2003, digital governance transformation is necessary to reduce bureaucratic barriers, integrate workflows, and support inter-institutional collaboration.

This study examines the role of QRIS as a non-cash payment system in supporting the digitalization of micro, small, and medium enterprises (MSMEs) in Indonesia. The rapid growth of digital transactions has encouraged MSMEs to adopt more efficient, practical, and secure payment systems. This study aims to integrate the benefits and barriers of QRIS, map its position in the MSME digitalization process, and develop a conceptual framework for future research. A descriptive qualitative approach with a literature study design was employed, utilizing relevant academic sources related to QRIS, MSMEs, digital payments, and user behavior. Data were analyzed using content analysis by comparing, interpreting, and synthesizing findings from selected literature. The results indicate that QRIS contributes to simplifying transactions, improving operational efficiency, reducing cash dependency, and supporting MSMEs’ adaptation to digital payment trends. However, its implementation remains constrained by factors such as digital literacy, user readiness, trust, and infrastructural limitations. The study also highlights that QRIS adoption is influenced not only by technological advantages but also by perceived value and user trust. Therefore, QRIS can be positioned as a strategic instrument in accelerating the digital transformation of MSME payment systems. The findings imply the need for further empirical research to examine the direct impact of QRIS adoption on MSME performance and sustainability.