Rapid advances in information technology have transformed multiple sectors, particularly education, where digital platforms play an increasingly vital role in academic operations. Study program websites now serve as primary channels for information dissemination and communication between institutions and their stakeholders. Yet the Informatics Engineering Study Program website at Universitas Bina Darma faces notable usability problems affecting both interface design and user experience quality. A heuristic evaluation survey of 100 students identified several critical issues: absent active menu indicators that hinder navigation tracking, unattractive visual layouts that reduce user engagement, and ambiguous icons and terminology that create confusion during interaction. To address these challenges, researchers employed the Design Thinking framework—a user-centered methodology that emphasizes empathy, ideation, and iterative testing. The framework guided the development of a redesigned prototype, which underwent rigorous evaluation through the Maze platform across four distinct user task scenarios covering key website functions. Test outcomes demonstrated 100% task completion rates among participants, with an average usability score of 89, placing the design in the excellent category. The redesigned interface delivers substantially improved user experience through enhanced navigation clarity, better visual hierarchy, and more intuitive interaction patterns. These improvements establish a solid groundwork for continued development and eventual implementation of systems, offering practical solutions that can be adapted for similar academic website redesign projects.

Social assistance distribution to elderly populations in urban areas, particularly Jakarta, frequently encounters challenges in accurately identifying eligible recipients. The present study develops a classification model for elderly social assistance recipients using the Decision Tree algorithm to enhance objectivity and precision in beneficiary selection. Field research was conducted in RW 13, Malaka Jaya Sub-District, East Jakarta, utilizing primary data gathered through systematic observation. Key variables including age, income level, residential ownership status, health conditions, and family dependents were incorporated into the classification framework. The CRISP-DM methodology structured the analytical process, spanning from initial data exploration through model validation. Model testing employed a 70:30 data partition strategy, achieving 95.84% classification accuracy. Findings demonstrate the model's capability in determining eligibility for Jakarta's elderly social assistance program. Implementation of the proposed classification system promises to strengthen transparency, improve targeting precision, and establish evidence-based decision-making in social welfare distribution.

Digital transformation is still an ongoing process in health service delivery to improve operational performance and service quality. However, BPJS Kesehatan patient referrals are still trapped in administrative bottlenecks. At Mulia Medika Clinic, staff used to handle BPJS patient referrals manually, so that records were prone to errors and delays in obtaining information. We designed, implemented, and tested a desktop-based BPJS referral information system using Python and Tkinter for clinic operations. The development process followed the Waterfall methodology, which consisted of requirements analysis, system architecture design using Context Diagrams, Data Flow Diagrams, Entity-Relationship Diagrams as well as Flowcharts followed by implementation and black-box testing validation. The system will manage patient records, referral processing as well as user administration. Automated features include generating referral letters and producing reports. Testing has proven that this system is accurate and efficient—the manual workload has reduced, data traceability has improved, and continuity in the referral service has been maintained. Results prove operational readiness for clinic deployment to enhance administrative efficiency and precision of the referral data. Currently, it runs standalone without real-time database synchronization; hence workflow integration cannot be achieved. Future versions should have direct connections with both clinic management and BPJS databases to allow seamless data exchange without manual synchronization

- Employee burnout threatens organizational sustainability through reduced productivity, compromised mental health, and elevated turnover rates. Early detection remains critical for maintaining workforce stability. We address burnout prediction optimization at PT. XYZ through three advanced machine learning models: Explainable Boosting Machine (EBM), Long Short-Term Memory (LSTM), and Extreme Gradient Boosting (XGBoost). Our methodology incorporates structured data preprocessing, model construction, training protocols, and rigorous performance evaluation. We assessed models using MAE, RMSE, and R² for regression tasks, alongside Accuracy, Precision, Recall, F1-score, Confusion Matrix, Feature Importance, and ROC curves for classification. Cross-validation ensured robust evaluation, with burnout labels derived from established psychosocial factor assessments. Results reveal LSTM's superior performance at 0.99 accuracy, followed by EBM (0.96) and XGBoost (0.95). LSTM demonstrates exceptional capability in identifying subtle burnout patterns, while EBM delivers high interpretability regarding causal factors. These findings offer a data-driven framework for human resource management, enabling precise, proactive intervention through evidence-based decision-making.

Tesseract OCR ranks among the most widely adopted open-source tools for text extraction. Nevertheless, processing documents with degraded image quality—including blurry e-KTPs, low-contrast specimens, or those affected by uneven lighting—presents substantial challenges. We conducted experimental research to generate empirical data supporting the development of text detection systems for e-KTPs operating under non-ideal conditions. Our methodology involved testing 10 e-KTP images, each containing 15 text attributes, yielding 150 evaluated data points. Image preprocessing proceeded sequentially through grayscale conversion, denoising, contrast enhancement (CLAHE), and thresholding to improve image clarity prior to Tesseract OCR processing. We evaluated accuracy using confusion matrix analysis, emphasizing True Positive (TP), False Positive (FP), and False Negative (FN) metrics. Results demonstrate that preprocessing stages substantially improved text readability. Baseline OCR accuracy of 39.55% increased incrementally: +22.68% following grayscale conversion, +47.70% after denoising, +60.99% post-CLAHE application, and +19.62% after thresholding, culminating in 64.97% accuracy upon completing all preprocessing stages. Average TP values rose from 4 to 8 out of 15 attributes per image, while precision remained stable at 100% (FP = 0). Despite variable CLAHE performance across samples, preprocessing stages proved essential for OCR systems operating under degraded image conditions. Our work introduces a novel preprocessing pipeline tailored specifically to Indonesian e-KTP characteristics, providing quantitative benchmarks and systematic analysis that can inform the development of more adaptive digitalization and verification systems for population documents under real-world field conditions

Deaf people use sign language as their primary means of communication. Images of sign language gestures are usually low quality because visual impairments like noise and low contrast prevent an automatic recognition system from working well. This research tries to enhance the quality of images with sign language gestures using two preprocessing methods, namely Gaussian Filter and Contrast Stretching. The first one eliminates noise while keeping important details in the image, and the second increases pixel intensity distribution to make hand gestures more apparent and outlined. An experiment was done on a dataset that includes 54,049 static hand gesture images taken from videos that contain certain sign languages divided into 28 classes for hijaiyah letters. A quantitative evaluation indicated substantial enhancements in processed image quality. The preprocessing method resulted in an average PSNR of 20.13 dB, SSIM equal to 0.8875, and MSE equal to 976.39 for all samples tested confirming that this combination method improves sharpness, structural integrity, and contrast when compared with original unprocessed images significantly. This study recommends using Gaussian Filter along with Contrast Stretching as a practical option for improving the quality of sign language images which can eventually help automated recognition systems that need clear visual input to correctly classify gestures.

Mosquito-transmitted diseases like Dengue Hemorrhagic Fever and Filariasis pose serious health threats throughout tropical regions, particularly in Indonesia. Quick and accurate identification of mosquito larvae plays a crucial role in disease prevention, especially for Aedes albopictus and Culex quinquefasciatus species that act as main disease carriers. Manual identification methods using microscopes or visual guides often struggle with time constraints, accuracy issues, and dependence on trained specialists. Our research focuses on improving the classification of Aedes albopictus and Culex quinquefasciatus mosquito larvae using Vision Transformer (ViT) technology, a deep learning method that has shown strong results in image recognition tasks. We applied the Vision Transformer model to classify mosquito larvae from microscopic field images. The study also tested how different factors impact model performance, such as image clarity, lighting conditions, and image resolution. Our findings show that using Vision Transformer in classification systems produced excellent results, achieving 98.00% accuracy in recall, precision, and F1-score measurements. The research reveals that Vision Transformer methods deliver better accuracy than traditional approaches like Convolutional Neural Networks and can be adapted into working systems for technology and healthcare sectors.

Agile methodologies are widely adopted in software development for their effectiveness in team coordination, iterative progress, and comprehensive project oversight. This study analyzes the Agile Code Board, a real-time project management platform designed to assist Agile teams in organizing and executing tasks throughout each sprint. The platform offers multiple workspaces with role-based access, enabling task tracking, immediate team collaboration, and sprint-level planning. Key features include a drag-and-drop Kanban board, a collaborative whiteboard, calendar integration for deadlines and milestones, a filterable task table, and a cross-team program board inspired by SAFe. Agile Code Board is built using Next.js and Hono, with Appwrite for authentication and Liveblocks for real-time collaboration. The system introduces mechanisms that facilitate efficient teamwork and project coordination. This research outlines the system architecture, development practices, and the ways in which the platform enhances Agile workflow management in collaborative environments.

The acceleration of information technology advancement necessitates digital system adoption among SMEs to maintain market competitiveness. Toko Susu Bahagia faced operational inefficiencies in inventory management and transaction documentation through manual processes. This research developed a customized e-commerce solution implementing the Waterfall methodology, progressing systematically through requirement analysis, design, implementation, and verification phases. The system architecture utilized PHP (Laravel) framework with MySQL database integration, incorporating essential functionalities: inventory control, order status monitoring, product expiration notifications, and sales analytics. Verification through Black Box Testing yielded a 98% functionality success rate, validating system dependability. Technical obstacles encountered during development included insufficient data validation protocols and limited digital proficiency among users, which were addressed through simplified interface design and enhanced validation mechanisms. The research establishes the practical effectiveness of user-oriented system architecture for small retail enterprises. Operational performance improved through error reduction in manual processes, instantaneous inventory visibility, and expanded market access. The implementation indicates sustained advantages for business growth capacity and technological preparedness. Subsequent research directions may investigate payment gateway integration, language localization capabilities, and responsive design optimization to enhance system versatility.

Queue systems play pivotal roles in banking services, affecting both operational effectiveness and client satisfaction. The issue of long waiting times and unequal distribution of services across all regions of Indonesia remains a significant issue. Using the PRISMA methodology to perform a systematic literature review (SLR), this research investigates the last 5 years of 25 digital queue management academic works. The selection process to narrow this down began with the Sinta and Garuda databases, although more from Scopus were used to validate the research. The research indicates that identified technologies, such as online queue systems, self-service kiosks, and reservation systems, can improve customer satisfaction and experience by greatly decreasing waiting times. However, the majority of the observations are based on theoretical models or narrow case studies, leading to doubts regarding the generalizability of their findings. The effectiveness of the solutions proposed is highly dependent on the region's infrastructure as well as the users' capacity to utilize the available technology. The integration of queue theory and process simulation provides a foundational approach to the design of systems with improved efficiency. However, there are still significant obstacles to overcome: deficient digital infrastructure, low levels of technological literacy, and poorly integrated systems. Sufficient, systematic responses are required to these issues, as well as more localized systematic studies, to improve queue management systems in the context of the Indonesian banking sector.