This study aims to develop and evaluate a Medical Record Education System designed to enhance students’ digital competence in managing electronic medical records (EMRs). Addressing the gap between the- oretical learning and practical application in health informatics educa- tion, the system was developed using the Waterfall model and evaluated through the ISO 9126 software quality framework. A Research and De- velopment (RD) approach was applied at Politeknik Indonusa Surakarta involving 30 final-year students of the Health Information Management program. Data collection included observation, ISO 9126-based ques- tionnaires, and in-depth interviews, analyzed using descriptive and the- matic techniques. The results showed significant improvement in student performance, with pretest–posttest scores increasing from 64.5 to 86.2 after a two-week system trial. The system comprises interactive modules such as patient data entry, diagnosis coding, and auto-report generation. Expert evaluation produced high scores for usability (4.6), functionality (4.4), reliability (4.2), and efficiency (4.3), confirming both educational value and technical feasibility. These findings highlight the role of struc- tured development and quality assessment models in instructional in- novation. The system not only improves learning outcomes but also provides a replicable model for embedding digital transformation into vocational healthcare education.

This study examines the challenges and opportunities encountered by vocational high school teachers in implementing online assessments and how these experiences facilitate their pedagogical development. The research employed a qualitative phenomenological design, involving six vocational teachers from several disciplines who had administered online exams for a minimum of one academic year. Data were gathered via semi-structured interviews, document analysis, and a focus group discussion. Thematic analysis was employed to identify emerging patterns and meanings within the participants' stories. The results indicate that teachers initially encountered several obstacles, including insufficient technological proficiency, inadequate institutional support, and unequal access to digital resources among students.Notwithstanding these challenges, teachers have shown flexibility and ingenuity by formulating innovative assessment methods, including digital portfolios, performance-based activities, and interactive rubrics. These improvements promoted increased student autonomy and involvement, converting assessment into a learning-focused process rather than a simple evaluation task. Teacher collaboration developed as a vital element in surmounting challenges and maintaining professional development. The study indicates that online assessment functions as both a challenge and a driver of educational transformation. It underscores the necessity for ongoing professional development and institutional backing to improve teachers' digital literacy and reflective practices. The study implies that well-designed and adequately supported online assessments can foster genuine learning and professional rejuvenation in vocational education.

IT productivity paradox phenomenon shows that increasing investment in information technology is not always followed by increasing productivity or user satisfaction. In the context of smart governance, citizen science becomes a strategic approach to evaluate the effectiveness of geospatial technology. This study explores the relationship between self-efficacy, spatial abilities, and user knowledge of system quality and information quality and their impact on user satisfaction. The case study was conducted on the Sistem Informasi Penataan Ruang (Si-Petarung) of Malang City. The results showed that self-efficacy has the most dominant influence on system quality with a dominance value of 40.5%, and user knowledge has the greatest impact on information quality with a dominance value of 32.29%. Information quality is also proven to be the most significant factor in increasing user satisfaction with a dominance value of 47.93%. These findings emphasize the importance of individual abilities, system quality, and information quality in supporting the successful implementation of geospatial technology and provide a basis for developing a system that is more inclusive and responsive to user needs.

Mental health is a fundamental aspect of human well being, yet it often faces persistent barriers such as stigma, high treatment costs, and limited access to professional psychological services. This study addresses these challenges by designing the Bright for Mental Health application, developed through the Design Thinking framework encompassing empathy, problem definition, ideation, prototyping, and evaluation. The usability of the application was assessed using the System Usability Scale–Positive (SUS-P) method. The evaluation involved user testing, which resulted in an average score of 79.7, placing the application in the “Good” usability category. This outcome suggests that the system demonstrates strong levels of effectiveness, efficiency, and user satisfaction, although certain aspects still require further refinement. By integrating AI-driven features, the application expands the potential for inclusive and accessible digital mental health support, reducing barriers to care and offering an alternative for individuals with limited access to professional services. The findings highlight the significance of combining user-centered design principles with technological innovation to create impactful digital health solutions. Future studies are recommended to engage a more diverse range of respondents and enhance collaboration with mental health professionals to strengthen the application’s effectiveness and adoption in broader communities.

This study presents a max-margin–based approach for sentence boundary segmentation in Indonesian paragraphs, addressing a persistent challenge in Natural Language Processing applications. Conventional rule-based or sequential methods often struggle to distinguish ambiguous punctuation marks, particularly in contexts involving abbreviations, numerical expressions, hierarchical sentence structures, and direct quotations. To overcome these limitations, this research formulates sentence segmentation as a paragraph parsing task, enabling the model to capture both local boundary cues and global structural patterns within a paragraph. A manually annotated corpus of 12,000 paragraphs from news articles, public documents, and academic texts was developed to provide diverse linguistic structures and punctuation variations. The proposed model integrates local punctuation features, structural constraints from the Indonesian EYD standard, and global paragraph coherence through a max-margin discriminative parsing framework. Experimental results show that the model achieves strong performance on the test set, with a precision of 0.93, recall of 0.91, and F1-score of 0.92, significantly outperforming a rule-based baseline. Error analysis further highlights improvements in handling ambiguous cases such as abbreviations, numerical formatting, and direct quotations with nested punctuation. The findings demonstrate that a structured max-margin approach delivers more reliable sentence boundary segmentation and can enhance downstream NLP tasks requiring accurate sentence-level text processing.

The objective of the research was to implement the method of secure data storage through the use of steganography techniques using the Discrete Cosine Transform (DCT) method. The impetus for the research is the increasing threat to the security and confidentiality of student data, such as unauthorized file deletion, data theft, unauthorized data use, and data utilization for unintended purposes. The research applied the ADDIE Research and Development (RD) model, which consists of the following five stages: Analysis, Design, Development, Implementation, and Evaluation. The system uses a modified DCT coefficient at position (2,1) with thresholds of 12.0 for embedding and 6.0 for extraction on 8×8 pixel blocks, following IEEE standards. The dataset comprised 14 images corresponding to various personal data files of Computer Engineering students, such as family cards, ID cards, and birth certificates. The system was implemented using the Laravel framework integrated with Python scripts for DCT algorithm processing. Experimental and questionnaire-based testing with 14 respondents demonstrated that the system achieves high imperceptibility, with an average MSE of 2.847 and PSNR of 46.89 dB. The system achieved an average embedding time of 67.8 ± 54.2 seconds and extraction time of 34.2 ± 22.8 seconds, with throughput rates of 1.068 kbit/s for embedding and 1.908 kbit/s for extraction. The overall extraction success rate reached 50%, with 100% success for "Excellent" images and 22.2% for "Good" images. Usability testing yielded an average user satisfaction score of 4.54/5.0, with the highest rating for visual design (4.79).