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Online maps applications have become an essential tool for modern society in finding the fastest and most efficient routes. However, these applications often fail to detect current road conditions such as flooding, demonstrations, accidents, or public events, causing users to get stuck in problematic routes. This study aims to develop a prototype of a community-based road condition reporting system, visualized through a web-based digital map. The system allows users to directly report road conditions by providing photo evidence, descriptions, and event categories. It is also equipped with features for designing event routes such as carnivals and suggesting alternative paths based on community reports. The development process was carried out using a simulation-based approach with scenario testing that reflects real field conditions, without involving direct user data. The implementation results show that all core features work properly. The technologies used include Leaflet.js, OpenStreetMap, and the Nominatim geolocation API. This research produces an adaptive community-based GIS model that can be further developed as an intelligent navigation solution at the city scale

Phishing is one of the most common and dangerous cyber attacks as it can trick users into providing sensitive information such as usernames, passwords, and personal data. This study aims to simulate Phishing techniques using the Zphisher tool as an educational medium to improve cybersecurity awareness. The method used is a quasi-experimental approach involving 30 participants exposed to Phishing scenarios in a controlled environment using Kali Linux. The results show that 83% of participants clicked the Phishing link and 60% entered their credentials, while 40% demonstrated awareness by avoiding further interaction. These findings indicate that cybersecurity awareness is still relatively low, but Phishing simulation is effective as an experiential learning tool.

The growth of digital banking services has inspired banks in Indonesia to provide a package of services not limited to financial transaction purposes but also lifestyle commerce and shopping. Yet, it is observed that the use of the Livin’ Sukha service in the Livin’ by Mandiri app is quite low compared to other digital banking services. Therefore, this study will examine the effect of perceived benefits, perceived risk, and perceived trust on customers' willingness to use Livin’ Sukha services for shopping activities at Alfamart. For this purpose, a quantitative research design was applied by utilizing a survey research methodology with a sample of 200 users of Livin’ by Mandiri, selected using purposive sampling technique. The data were gathered by distributing online surveys and analyzed using Structural Equation Modeling based on Partial Least Square. It was discovered that there is a positive relationship between perceived benefits and perceived trust and perceived benefits and willingness to use; meanwhile, perceived risk showed negative relationships with perceived trust and willingness to use. Furthermore, perceived trust has positive relationships with customers' willingness to use Livin’ Sukha services. Besides, it has been revealed that household shopping activities have good potential to increase customers' engagement and transactions  

The Jambi Class II Climatological Station is the agency responsible for providing climate data and information to the public and relevant agencies. In practice, the process of handling data requests is still carried out conventionally, resulting in relatively long processing times for document management, archiving, and user service. This situation has led to several challenges, such as inefficient administrative processes, a high risk of document loss, and limitations in monitoring the status of data requests. This study aims to design a web-based Data Service Information System that can support the digitalisation of climate data services at the Class II Jambi Climatology Station. The system development method used is the prototype method, as it allows the development process to be carried out in stages based on user needs. The system design was carried out using the Unified Modelling Language (UML) as a modelling tool to describe system requirements, process flows, and user interactions with the system. The design results show that the developed system is capable of facilitating the online submission of data requests, tracking the status of requests, and managing the service database in a more structured manner. The system also provides a digital archiving feature that simplifies the management and retrieval of data request documents. The implementation of this system is expected to improve the efficiency of service processes, speed up data request processing times, enhance service transparency, and support improvements in the quality of public services at the Class IV Jambi Climatological Station.

Transportation plays an important role in supporting community mobility and enhancing socio-economic activities in urban areas. The increasing population growth and community activities in Sorong City have led to a higher demand for transportation modes. This study aims to identify the characteristics of transportation users in Kota Sorong and to analyze the probability of selecting different transportation modes using the Multinomial Logit (MNL) method. A quantitative approach was employed in this research. Questionnaires were distributed to 225 transportation users in Sorong City. The variables analyzed included gender, age, occupation, income, trip purpose, travel frequency, and reasons for choosing a transportation mode. The data were analyzed using multinomial logistic regression with SPSS software. The results indicate that transportation mode choice is influenced not only by socio-economic factors, but also by showed that income, trip purpose, travel frequency, and reasons for choosing the mode significantly influenced transportation mode choice, while gender, age, and occupation did not have significant effects. The probability analysis indicated that private vehicles had the highest probability of selection at 89.63%, followed by online transportation at 9.96%, and conventional motorcycle taxis at 0.41%. These findings imply that socio-economic and travel behavior factors strongly influence transportation mode preferences in Sorong City and may serve as a reference for future urban transportation planning and policy development.

This study examines ideological polarization in online news comment sections through a Critical Discourse Analysis (CDA) approach. In recent years, digital platforms have increasingly become sites of discursive contestation where ideological divisions are intensified rather than mitigated. Using a qualitative interpretive design, this study analyzes approximately 300 user comments on political news articles collected through purposive sampling. The analysis applies van Dijk’s socio-cognitive model, focusing on textual structures, social cognition, and social context. The findings reveal three dominant patterns: (1) the construction of “us versus them” dichotomies through evaluative and exclusionary language, (2) the use of emotional and stigmatizing discourse as a mechanism of ideological legitimation, and (3) the recursive reproduction of polarization through interactive comment chains. Drawing on recent studies in digital communication and polarization, the findings demonstrate that online language operates as a form of symbolic power that reinforces group boundaries and social fragmentation. This study contributes to contemporary digital discourse research by emphasizing the importance of critical digital literacy and micro-level discursive analysis in understanding ideological polarization.

The advancement of information technology has driven higher education institutions to manage their digital presence professionally, particularly through quality website management as a representation of institutional identity. Preliminary observations of the Universitas Graha Karya (UGK) website (ugk.ac.id) identified several issues, including inconsistent page layouts, inactive hyperlinks, and irregularly updated content. This study aims to analyze the quality of the UGK website based on three WebQual 4.0 dimensions—usability, information quality, and service interaction quality—and to determine improvement priorities using Importance Performance Analysis (IPA). A descriptive quantitative approach was employed, with data collected through questionnaires distributed to 308 active students selected via purposive sampling using the Slovin formula at a 5% margin of error. Data analysis included validity testing, reliability testing, WebQual 4.0 analysis, gap analysis, and IPA Cartesian diagram mapping. Results indicate negative grand mean gap values across all dimensions, with service interaction quality recording the largest gap (-0.16), followed by information quality and usability (both -0.01). IPA mapping identified five indicators in Quadrant I as the main improvement priorities: ease of menu navigation (U2), relevance of academic information (IQ2), clarity of information language (IQ4), website access security (SI1), and ease of contacting administrators (SI4). The study concludes that UGK's website quality has not fully met user expectations and requires structured, user-oriented improvements.

The readiness of officers to carry out Electronic Medical Records (RME) needs to be measured using the Technology Readiness Index (TRI) method, which is an index used to measure the readiness of new technology users to achieve goals in daily life and work. This research aims to determine the readiness of Electronic Medical Record (RME) users using the Technology Readiness Index (TRI) case study at Daha Husada Regional Hospital. This is a descriptive research with an evaluation study where the research subjects are the head of medical records and 3 medical records officers in the inpatient registration section at Daha Husada Regional Hospital using a total saturated sampling technique. Data collection uses interview techniques. Data presentation is presented using narrative. The results of the research show that of the 4 variables used to measure the readiness of medical record users, medical record officers at Daha Husada Regional Hospital are ready to use medical records, even though they are ready there are still difficulties experienced and feelings of insecurity when using Electronic Medical Records (RME), namely Medical records can be accessed anywhere and are not yet integrated according to the user's wishes. Therefore, Daha Husada Regional Hospital should further tighten its electronic medical record data security system.

The reliability of an e-commerce application is essential to ensure smooth and secure user transactions. This study discusses the implementation of Software Quality Assurance (SQA) on a web-based e-commerce application to test the functionality of the developed system. The testing methodology used in this research combines manual testing and automated testing using the Katalon Studio tool, following the Software Testing Life Cycle (STLC) framework. Manual testing was focused on validating the user interface and user experience across core workflows, including user registration, authentication, cart management, and the checkout process. Meanwhile, Katalon Studio was utilized to execute automated test scenarios on these crucial features to accelerate regression testing and ensure system stability. The results indicate that from 42 test cases executed, the SQA approach successfully identified five functional defects across three modules, categorized by severity into two High-level defects (server error on registration and missing error handling on checkout), two Medium-level defects (client-side-only address validation and cart quantity logic), and one Low-level defect (insufficient logout notification display time). Four of the five defects were resolved, raising the test pass rate from 90.5% to 97.6% during regression testing. Automated testing demonstrated a significant time efficiency of approximately 95% compared to manual execution for regression scenarios. In conclusion, the combination of manual and automated testing proved capable of thoroughly verifying software quality, ensuring that the SQA Shop application functions according to the required specifications before its release to end-users.

Instagram social media addiction has become an increasingly common phenomenon among university students. Excessive use of Instagram can negatively affect mental, social, and academic well-being, and may increase the risk of social anxiety. Students who experience Instagram addiction tend to have reduced direct social interaction, and fear of negative evaluation from others, which are key factors contributing to social anxiety. This study aims to determine the relationship between Instagram social media addiction and social anxiety among students. This study used a quantitative approach with a cross-sectional design. The sample consisted of 105 respondents selected using purposive sampling. The instruments used were the Instagram Addiction Scale (TIAS) and the Social Anxiety Scale for Social Media Users (SAS-SMU). Data analysis was conducted using the Chi-Square test. The results showed that most respondents had a moderate level of Instagram addiction (56.2%) and a moderate level of social anxiety (68.6%). The Chi-Square test indicated a significant relationship between Instagram addiction and social anxiety, with a p-value < 0.001 (p < 0.05). There is a significant relationship between Instagram addiction and social anxiety among students. However, the relationship is not always linear, as higher levels of addiction do not necessarily correspond to higher levels of social anxiety.

This study aims to analyze the relationship between self-compassion and self-objectification among female TikTok users in Jambi City. The study used a quantitative correlational approach with a cross-sectional design. A total of 218 female students in Jambi City aged 18–25 years who actively use TikTok were selected using a purposive sampling technique. The instruments used were the Self-Compassion Scale (SCS) adapted into Indonesian (Skala Welas Diri) and the Objectified Body Consciousness Scale (OBCS). Data analysis used the Pearson Product Moment correlation test. The results showed a significant negative relationship between self-compassion and self-objectification (r = -0.216; p = 0.001). The higher the self-compassion, the lower the tendency for self-objectification. The majority of respondents had moderate to high levels of self-compassion, while self-objectification was in the moderate category. The duration of TikTok use of more than 2 hours per day was associated with a higher tendency for self-objectification. These findings confirm the role of self-compassion as a protective factor against self-objectification in the context of visual-based social media use.

The rapid development of artificial intelligence (AI) technology and personalization algorithms has fundamentally transformed the landscape of e-commerce and digital retail, particularly influencing the shopping behavior of female consumers. This study examines the utilization of AI-driven personalization algorithms and their impact on the research shopping phenomenon among female consumers in the digital era. Research shopping, defined as the behavior of searching for product information online while making the final purchase decision offline or across multiple platforms, has become increasingly prevalent as digital touchpoints multiply. This research employs a quantitative approach with a survey method involving 250 female respondents aged 18–45 who actively use e-commerce platforms in Indonesia. Data were collected using structured questionnaires and analyzed using Structural Equation Modeling (SEM). The findings reveal that AI personalization algorithms significantly influence female consumer decision-making processes, fostering research shopping tendencies through targeted recommendations, dynamic pricing, and adaptive content delivery. Furthermore, the study identifies trust, perceived usefulness, and digital literacy as key mediating variables between AI technology utilization and research shopping behavior. These results contribute to the growing body of literature on AI-driven consumer behavior and offer practical implications for digital marketers and e-commerce platform developers seeking to optimize user experience for female consumers.

Instagram social media addiction has become an increasingly common phenomenon among university students. Excessive use of Instagram can negatively affect mental, social, and academic well-being, and may increase the risk of social anxiety. Students who experience Instagram addiction tend to have reduced direct social interaction, and fear of negative evaluation from others, which are key factors contributing to social anxiety. This study aims to determine the relationship between Instagram social media addiction and social anxiety among students. This study used a quantitative approach with a cross-sectional design. The sample consisted of 105 respondents selected using purposive sampling. The instruments used were the Instagram Addiction Scale (TIAS) and the Social Anxiety Scale for Social Media Users (SAS-SMU). Data analysis was conducted using the Chi-Square test. The results showed that most respondents had a moderate level of Instagram addiction (56.2%) and a moderate level of social anxiety (68.6%). The Chi-Square test indicated a significant relationship between Instagram addiction and social anxiety, with a p-value < 0.001 (p < 0.05). There is a significant relationship between Instagram addiction and social anxiety among students. However, the relationship is not always linear, as higher levels of addiction do not necessarily correspond to higher levels of social anxiety.

Gorontalo Province possesses significant mineral resource potential, particularly gold, silver, and copper, positioning the mining sector as a key driver of regional economic growth. However, a shortage of skilled local labor and the scarcity of vocational educational institutions in the mining field severely hamper human resource development in this sector. This study aims to design a Mining Polytechnic Campus in Gorontalo by applying sustainable architecture principles, encompassing energy efficiency, environmentally friendly materials, sound wastewater management, and user comfort. The research approach involves literature studies, field observations, interviews with relevant stakeholders, and quantitative data analysis regarding resource potential, the number of senior high school students, and educational space requirements. The design results emphasize site arrangement, building mass configuration, utility systems, and interior and exterior spaces that support academic, social, and community activities. The application of sustainable architecture principles is expected to create a campus that not only meets the needs of mining vocational education but also contributes to environmental conservation and sustainable regional development.

This study examines the foundational framework and evolving dynamics of Indonesian civil procedural law (Hukum Acara Perdata) amid its transformation from colonial legal heritage toward a modern, digitalized justice system. Using normative-doctrinal legal research methodology combined with empirical court statistics from 2022–2024, the study analyzes four dimensions: (1) core procedural principles rooted in the HIR and RBg; (2) the mandatory mediation framework under PERMA No. 1 of 2016; (3) the evolution of evidence law toward electronic evidence under the amended UU ITE (Law No. 1 of 2024); and (4) mechanisms of judicial decisions and legal remedies. Findings show that e-court implementation has dramatically increased efficiency minutasi productivity rose from 64.35% (2022) to 96.50% (2024)—while 594,816 users registered through e-court by 2023. The study concludes that Indonesia’s civil procedural law is actively adapting to technological disruption, though structural challenges such as digital inequality, electronic evidence authentication gaps, and regulatory harmonization remain unresolved.

The integration of educational technology has become increasingly important in supporting effective learning processes. Google Drive is one of the widely used cloud platforms for online storage, access, and collaboration in educational activities. This study aims to examine the factors influencing vocational school students’ perceived ease of use (PEoU) of Google Drive as a learning support tool. The research model was adapted from the Technology Acceptance Model (TAM) by incorporating external variables, including Computer Self-Efficacy (CSE), Perceived Enjoyment (PE), Perceptions of External Control (PEC), Technological Complexity (TC), and Facilitating Conditions (FC). Data were collected from 274 students at a public vocational school in Central Java using an online questionnaire. The data were analyzed using Partial Least Squares–Structural Equation Modeling (PLS-SEM) with SmartPLS. The findings reveal that Computer Self-Efficacy, Perceived Enjoyment, and Perceptions of External Control significantly influence students’ perceived ease of use of Google Drive. In contrast, Technological Complexity and Facilitating Conditions do not significantly affect perceived ease of use. These findings highlight the importance of internal user perceptions in supporting educational technology adoption among vocational school students.

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.

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.

Digital transformation in the judicial system represents a strategic step to improve the quality of legal services in Indonesia. One of the innovations implemented by the Supreme Court of the Republic of Indonesia is the E-Court system, which enables case administration and court proceedings to be conducted electronically. This study aims to analyze the effectiveness of the implementation of E-Court in realizing the principles of simple, fast, and low-cost justice, as well as to identify the obstacles faced by parties who do not utilize the system. The research method employed is a normative legal approach, involving the analysis of statutory regulations and scholarly literature. The results indicate that, normatively, E-Court has been effective in supporting judicial efficiency; however, in practice, it still encounters various challenges, including technical, social, and institutional aspects. These obstacles vary among different legal subjects, namely advocates, individuals, private legal entities, and government legal entities. Therefore, comprehensive efforts are required to optimize the implementation of E-Court to ensure that the objectives of judicial reform can be fully achieved. Furthermore, the findings reveal that, from a normative perspective, E-Court provides a more efficient mechanism compared to conventional systems, particularly in terms of case administration and communication between parties. However, this effectiveness has not been evenly realized due to constraints such as the digital divide, limited technological infrastructure, and low user readiness. Consequently, the implementation of E-Court still requires reinforcement in both technical and cultural aspects to ensure optimal accessibility for all segments of society.

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