Dashboard Design Research Articles

772. Learning from our patients: An exploratory study to inform the development of a case tracking dashboard for subspecialty fellows

Abstract Background Educators identify case tracking (following-up prior patient encounters) as a valued practice to cultivate physician expertise. Much of the existing research has focused on the practices of general medicine providers, where challenges include insufficient time and difficulty tracking patients in the electronic health record (EHR). Subspecialty fellows constitute a group of advanced learners who see a high volume of patients and need to quickly become subject matter experts, yet little is known about their particular needs and motivations around case tracking. In this study, we performed a needs assessment with fellows to inform the design of an EHR-based case tracking dashboard that can facilitate learning from prior patient encounters. Methods We conducted semi-structured interviews with 22 medicine subspecialty fellows (including 4 Infectious Diseases [ID] fellows) at a single academic medical center that explored participants’ motivations, preferences, and practices around case tracking. Interview transcripts were coded and analyzed with directed qualitative content analysis. Results of this analysis will be used to inform the design of a subspecialist EHR-based case tracking dashboard. Results Participants were motivated to engage in case tracking, with curiosity regarding patient outcomes as a strong motivator. Although some attempted to track all consults, most focused on subsets of patients, feeling that not all encounters were of equal educational value. Participants highlighted particular results, specialty-specific notes, and major outcomes as the most useful information. ID fellows specifically were interested in post-discharge ID clinic encounters, send-out tests, and outcomes of their management decisions. Despite widespread motivation to engage in case tracking, manually adding patients to lists and remembering to follow up poses logistical and time barriers. Conclusion An ideal EHR case-tracking dashboard would automatically keep a list of patients cared for by fellows and provide easy access to own-specialty notes, specific results of interest, and information on the global clinical course. The ability to highlight specific cases anticipated to have more educational value would also be of benefit. Disclosures All Authors: No reported disclosures

Interactive dashboard development for Irish general practices to visualise and compare patient data

Abstract Background Health information technology has increased worldwide, transforming health and healthcare programs in which information visualisation and visual analytics processes are central. However, aggregating and comparing patient data remains challenging. Therefore, a dashboard was developed with and for Irish general practitioners (GPs) to improve their understanding of their patient population, disease management and prescribing. This paper describes the design and development process of the CARA dashboard. Methods The design and development of the CARA dashboard used an Action Design Research approach, which consists of (1) problem formulation, (2) building, intervention and evaluation, (3) reflection and learning and (4) formalisation of learning. The process included interviews, to explore what type of information GPs need, as well as iterative testing of the CARA dashboard prototype. Results Key challenges identified from the interviews (Context, Sense-making, Audits, Relevancy, Action, Engagement and Ease of Use) formed the basis for the development of the CARA dashboard prototype. The first exemplar dashboard focused on antibiotic prescribing to develop and showcase the proposed platform, including automated audit reports, filters (within practice) and between-practice comparisons as well as a visual overview of practice demographics. Conclusions User involvement in the design and development of the CARA dashboard is crucial to ensure GPs are enabled and supported to visually access and monitor their patients’ data. Next steps will include an exploratory study on dashboard engagement and the development of additional dashboards for topics such as chronic disease and mental health. Key messages • Good knowledge of operating systems, effective use of data, user interaction and context factors are required to design and implement an interactive dashboard. • Crucial components to make sense of the information provided in dashboards include the context, transparency about the data transformation processes and adherence to design principles.

Development of a cloud intelligent machine monitoring and control system

Facing the trend of Industry 4.0, the cloud-based supervisory control and data acquisition (SCADA) system employing cloud computing and IoT technology can help the manufacturing industry reduce software investment and system maintenance costs. However, manufacturers may need to install new sensors and controllers, the connection of SCADA system and shop floor machine controller, monitoring dashboard design and implementation usually need to outsource to an experienced system integration company, which may impede medium-sized manufacturing enterprises (SMEs). This paper aims to develop a cloud-based intelligent machine monitoring and control system (CIM-MCS) framework, the service structure, and approach to deploying the CIM-MCS in a public cloud infrastructure platform and service provider. The package diagram is proposed for building the CIM-MCS’s virtual factory model to improve modeling efficiency and data stability. CIM-MCS and its SCADA application in a leading automatic filling and packaging production line show that the CIM-MCS is easy to implement. The transmission time is short and acceptable for practical application. The integration of CIM-MCS with a cloud-based advanced planning scheduling system has the advantage of real-time monitoring, production progress reporting, scheduling, and dispatching and achieves the goal of anytime, anywhere, anyone, and any platform operating an intelligent factory.

Design and Evaluation of a Performance Dashboard for the Faculty of Allied Medical Sciences, AJA University of Medical Sciences: A Protocol for a Mixed Methods Study

Background: Faculties, as educational systems, comprise various educational groups, faculty members, researchers, students, and administrative staff. The management of data records related to the performance and activities of the faculty and its members leads to better monitoring, identification of weaknesses and strengths, and, ultimately, promotion of the faculty's performance. Dashboards are data management tools that can be used for monitoring and evaluating a faculty's performance. Objectives: This study aims to develop a protocol for the design of a faculty performance dashboard with a sequential mixed methods approach. Methods: This cross-sectional study will be conducted in the Faculty of Allied Medical Sciences, AJA University of Medical Sciences, in 2023. A mixed methods study with a sequential mixed (qualitative and then quantitative) design will be conducted in four phases. First, all the resources related to the functional dashboard are reviewed to identify its operational requirements. Second, the requirements and necessities of the software are determined by qualitative (interviews) and then quantitative (Delphi) methods. In this phase, 8 people will be interviewed during the qualitative phase, and thematic analysis will be used to analyze the data. For the quantitative step, the 2-round Delphi technique will be conducted by the purposive selection of 21 individuals. Data analysis for the quantitative step will be conducted in SPSS v. 22 by using descriptive statistics, including mode, median, mean, and percentage of agreement. Third, software coding will be performed in C# programming language in Visual Studio. Finally, 15 people among faculty members and managers will be selected by using purposive sampling to evaluate the software. In this phase, the qualitative method and then the quantitative method are used for software evaluation. In the qualitative method, the think-aloud protocol will be used to evaluate usability, and in the quantitative method, the users' satisfaction with the dashboard software will be assessed using a questionnaire. The validity and reliability of the questionnaire have been confirmed previously (Cronbach's alpha: 0.94). The data will be analyzed using descriptive statistics in SPSS v. 21. Results: The paper identifies four steps that should be followed when designing and adopting performance dashboards to support student agency and empowerment. Conclusions: The final product of this study is a dashboard for monitoring, evaluating performance, and managing resources at the faculty level.

Automated Gaze-Based Identification of Students’ Strategies in Histogram Tasks through an Interpretable Mathematical Model and a Machine Learning Algorithm

As a first step toward automatic feedback based on students’ strategies for solving histogram tasks we investigated how strategy recognition can be automated based on students’ gazes. A previous study showed how students’ task-specific strategies can be inferred from their gazes. The research question addressed in the present article is how data science tools (interpretable mathematical models and machine learning analyses) can be used to automatically identify students’ task-specific strategies from students’ gazes on single histograms. We report on a study of cognitive behavior that uses data science methods to analyze its data. The study consisted of three phases: (1) using a supervised machine learning algorithm (MLA) that provided a baseline for the next step, (2) designing an interpretable mathematical model (IMM), and (3) comparing the results. For the first phase, we used random forest as a classification method implemented in a software package (Wolfram Research Mathematica, ‘Classify Function’) that automates many aspects of the data handling, including creating features and initially choosing the MLA for this classification. The results of the random forests (1) provided a baseline to which we compared the results of our IMM (2). The previous study revealed that students’ horizontal or vertical gaze patterns on the graph area were indicative of most students’ strategies on single histograms. The IMM captures these in a model. The MLA (1) performed well but is a black box. The IMM (2) is transparent, performed well, and is theoretically meaningful. The comparison (3) showed that the MLA and IMM identified the same task-solving strategies. The results allow for the future design of teacher dashboards that report which students use what strategy, or for immediate, personalized feedback during online learning, homework, or massive open online courses (MOOCs) through measuring eye movements, for example, with a webcam.

Adaptively Learned Modeling for a Digital Twin of Hydropower Turbines with Application to a Pilot Testing System

In the development of a digital twin (DT) for hydropower turbines, dynamic modeling of the system (e.g., penstock, turbine, speed control) is crucial, along with all the necessary data interface, virtualization, and dashboard designs. Since the DT must mimic the actual dynamics of the hydropower turbine accurately, adaptive learning is required to train these dynamic models online so that the models in the DT can effectively follow the representation of the actual hydropower turbine dynamics accurately and reliably. This study presents an adaptive learning method for obtaining the hydropower turbine models for DT development of hydropower systems using the recursive least squares algorithm. To simplify the formulation, the hydropower turbine under consideration was assumed to operate near a fixed operating point, where the system dynamics can be well represented by a set of linear differential equations with constant parameters. In this context, the well-known six-coefficient model for the Francis turbine was formulated as the starting point to obtain input and output models for the turbine. Then, an adaptive learning mechanism was developed to learn model parameters using real-time data from a hydropower turbine testing system. This led to semi-physical modeling, in which first principles and data-driven modeling are integrated to produce dynamic models for DT development. Applications to a pilot system at the Norwegian University of Science and Technology (NTNU) were made, and the models learned adaptively using the data collected from the university’s pilot system. Desired modeling and validation results were obtained.

Designing a Cycling Dashboard as a Way of Communicating Local Sustainability

This paper conceptualizes the use of interactive urban dashboards in collecting and visualizing sustainability indicators at local scales through a cycling dashboard prototype for Münster. Urban dashboards are integrated platforms that bring various data types and sources together and automatize the visualization of information in real time. They can function as information hubs that work with mobile applications, sensor-based data, and crowdsourced platforms. Visualization of information can present both map-based data, text, and graphical information. In this study, a cycling dashboard is introduced that is developed as a prototype. The dashboard is based on the sustainability indicators related to cycling infrastructure defined by the German Sustainable Building Council (DGNB). It retrieves crowdsourced open data from Open Street Map (OSM) and automatically updates the information when new data are entered into the platform. This enables detailed exploration of the geo-referenced information up to street level and comparisons between different districts. In order to achieve a comprehensive framework, four main steps were included into the design and development process “determining a content by integrating future users, creating and evaluating a data inventory, designing the architecture of the dashboard, and implementing the prototype ”. As a result, this study holistically draws a comprehensive framework for the urban cycling dashboard around three main modules focusing on the sustainability of cycling infrastructure “infrastructure guide, cyclists’ voice, and idea center”. Additionally, the first prototype of an open local cycling dashboard has been implemented. The prototype enables the automation of collection, analysis, visualisation, and deep exploration of sustainability-related data at local scales. The results of this study contribute to the status quo by supporting the design and development process of local urban dashboards through a participatory approach.

Rancang Bangun Dashboard Penjualan pada PT. XYZ dengan Microsoft PowerBI

One of the most popular and most widely used technologies among people today is the internet. Starting from studying, shopping, working, doing business, communicating, everything can be accessed only by using the internet or in other words referred to as online activities. People are now trying to expand their business online, one of which is PT. XYZ. This company is a retail company that has an online and offline distribution system and sells power tools and household appliances. This company already has an application-based system. Therefore, the dashboard design for this company will be used to provide a summary of sales for three months from tokopedia, shoppe, lazada and tiktok shop to make it easier for company leaders to make decisions. The data collection method was carried out by interviewing and dashboard design using the prototyping method. This design will produce a dashboard using the Microsoft PowerBI Desktop application which can provide visual information about PT.XYZ's sales data.

Graphical features of interactive dashboards have little influence on engineering students performing a design task

This study investigates how interactive dashboards influence decision making by exploring how specific dashboard features impact design task performance, efficiency, understanding, and confidence. An experiment was conducted in which undergraduate student participants were given a design activity and randomly assigned to one of five dashboards, each using the same underlying functions but varying in the visualization features employed. These features include different graphical representations of the design decision inputs and performance outputs. Participants were first asked to use their assigned dashboard to design a catapult system that maximizes launch distance while meeting requirements related to height, weight, and cost. Following the design task, they were asked a series of questions about their experiences with the dashboard and their understanding of the catapult model. A between-subjects analysis then evaluated how the dashboard design influenced various outcomes of interest. The results show that students who used the most feature-rich dashboard did not perform objectively better than those with the most feature-sparse dashboard, though their self-reported performance was higher. The performance of female versus male participants was also compared, with no significant differences found. The findings support the notion that dashboards should be designed with minimal features to convey the necessary information, and they also point out the disconnect between objective performance and user-assessed performance with interactive dashboards.

Effect of information load and cognitive style on cognitive load of visualized dashboards for construction-related activities

A well-designed visualized dashboard could provide intuitive information to construction project managers for effective decision-making. However, the impact of information load of dashboards on cognitive load has rarely been investigated. The roles of user cognitive styles were also ignored. This study examined the effect of dashboard information load and user cognitive style on cognitive load when using dashboards in the construction industry. We designed a visual search experiment with five information load levels. Fifty-six participants, divided into field-dependent (FD) and field-independent (FI) groups, participated in the experiment. The results showed that the FI group had a lower cognitive load and was more efficient than the FD group. The effect of information load on cognitive load followed a piecewise linear correlation with the two prominent knots. The findings underscore the importance of assessing the information load and providing psychological underpinnings for cognitive-fit dashboard design for smart construction management.

Dashboards as Mechanisms for Community Empowerment: Developing a Prototype for Child and Adolescent Well-being in California.

In recent years, stakeholders in public health have emphasized measuring young peoples' well-being as a more holistic and upstream approach to understanding their health and development. However, summarizing the available indicators of well-being in ways that strengthen ongoing policy and community efforts remains a challenge. Our objective was to develop a measurement framework of young peoples' well-being that would be engaging and actionable to a broad and diverse set of stakeholders in California. We began with a scan of the relevant literature documenting previous efforts to measure young peoples' well-being, both within the United States and internationally. Subsequently, we individually interviewed a set of key informants and then convened a multidisciplinary panel of experts to solicit feedback on our approach. Throughout this iterative and collaborative process, we developed and refined a measurement framework based on the information provided across these various sources. Findings suggest data dashboards are a promising approach for presenting a parsimonious yet holistic picture of young peoples' well-being. Dashboards can highlight well-being's multidimensionality by categorizing indicators over different domains. Our framework organizes indicators over 5 types: child-centric, subjective well-being, contextual determinants, developmental, and equity-focused. The design and flexibility of dashboards can also highlight important gaps in data collection that are of interest to end users such as indicators not yet collected among the broader population. Furthermore, dashboards can include interactive features, such as selecting key data elements, that can help communities articulate priority areas for policy action, thereby generating momentum and enthusiasm for future iterations and improvements. Data dashboards are well suited for engaging a variety of stakeholders on complex multidimensional concepts such as young peoples' well-being. However, to fulfill their promise, they should be codesigned and codeveloped through an iterative process with the stakeholders and community members they intend to serve.

국내 감염병 현황 대시보드 디자인 패턴 분석

In the era of the pandemic, the importance of disease data visualization has been underscored. This study critically examines the design of disease data dashboards, specifically focusing on those utilized by the infectious disease management support teams. These dashboards, primarily intended for professional use, employ detailed charts to represent extensive data sets. Our analysis reveals that while there has been a concerted effort to customize visualizations to fit region-specific data, significant enhancements from a usability perspective are required. Consequently, we argue that infectious disease dashboards should progress towards accommodating personalized and public health needs, taking into account the divergent requirements across user types. In this transformation, the synergistic collaboration of UX/UI designers and data visualization experts is deemed indispensable, with the ultimate aim to construct a system capable of providing macroscopic insights into infection statuses. This study hence contributes to the ongoing discourse by proposing vital elements for the improvement of infectious disease dashboards, anticipating its findings to serve as a valuable resource in the effective construction of future dashboards.

Information Visualization for a Comprehensive Cybersecurity Risk Quantification and Measurement

This paper presents an innovative dashboard design for cybersecurity risk management, addressing the urgent need for effective visualization and quantification of cyber threats in organizations. Through semi-structured interviews with security executives, we identify key challenges in cybersecurity operations and propose a user-centric dashboard. This tool integrates an overall risk score, trend analysis, global threat mapping, and a three-step workflow from risk insights to remediation, all aimed at enhancing decision-making for Chief Information Security Officers (CISOs) and their teams. Usability testing underscores its effectiveness and user-friendliness, signaling a significant step forward in cybersecurity risk management by providing a comprehensive, intuitive, and actionable overview of an organization’s security posture

Dashboard Design to Analyze Chemical Weathering of Clastic Sediments

Dashboard Design to Analyze Chemical Weathering of Clastic Sediments

User-Centered Design of a Machine Learning Dashboard for Prediction of Postoperative Complications.

Machine learning models can help anesthesiology clinicians assess patients and make clinical and operational decisions, but well-designed human-computer interfaces are necessary for machine learning model predictions to result in clinician actions that help patients. Therefore, the goal of this study was to apply a user-centered design framework to create a user interface for displaying machine learning model predictions of postoperative complications to anesthesiology clinicians. Twenty-five anesthesiology clinicians (attending anesthesiologists, resident physicians, and certified registered nurse anesthetists) participated in a 3-phase study that included (phase 1) semistructured focus group interviews and a card sorting activity to characterize user workflows and needs; (phase 2) simulated patient evaluation incorporating a low-fidelity static prototype display interface followed by a semistructured interview; and (phase 3) simulated patient evaluation with concurrent think-aloud incorporating a high-fidelity prototype display interface in the electronic health record. In each phase, data analysis included open coding of session transcripts and thematic analysis. During the needs assessment phase (phase 1), participants voiced that (a) identifying preventable risk related to modifiable risk factors is more important than nonpreventable risk, (b) comprehensive patient evaluation follows a systematic approach that relies heavily on the electronic health record, and (c) an easy-to-use display interface should have a simple layout that uses color and graphs to minimize time and energy spent reading it. When performing simulations using the low-fidelity prototype (phase 2), participants reported that (a) the machine learning predictions helped them to evaluate patient risk, (b) additional information about how to act on the risk estimate would be useful, and (c) correctable problems related to textual content existed. When performing simulations using the high-fidelity prototype (phase 3), usability problems predominantly related to the presentation of information and functionality. Despite the usability problems, participants rated the system highly on the System Usability Scale (mean score, 82.5; standard deviation, 10.5). Incorporating user needs and preferences into the design of a machine learning dashboard results in a display interface that clinicians rate as highly usable. Because the system demonstrates usability, evaluation of the effects of implementation on both process and clinical outcomes is warranted.

Integrating human-centered design in public health data dashboards: lessons from the development of a data dashboard of sexually transmitted infections in New York State.

The increased availability of public data and accessible visualization technologies enhanced the popularity of public health data dashboards and broadened their audience from professionals to the general public. However, many dashboards have not achieved their full potential due to design complexities that are not optimized to users' needs. We used a 4-step human-centered design approach to develop a data dashboard of sexually transmitted infections for the New York State Department of Health: (1) stakeholder requirements gathering, (2) an expert review of existing data dashboards, (3) a user evaluation of existing data dashboards, and (4) an usability evaluation of the prototype dashboard with an embedded experiment about visualizing missing race and ethnicity data. Step 1 uncovered data limitations and software requirements that informed the platform choice and measures included. Step 2 yielded a checklist of general principles for dashboard design. Step 3 revealed user preferences that influenced the chart types and interactive features. Step 4 uncovered usability problems resulting in features such as prompts, data notes, and displaying imputed values for missing race and ethnicity data. Our final design was accepted by program stakeholders. Our modifications to traditional human-centered design methodologies to minimize stakeholders' time burden and collect data virtually enabled project success despite barriers to meeting participants in-person and limited public health agency staff capacity during the COVID-19 pandemic. Our human-centered design approach and the final data dashboard architecture could serve as a template for designing public health data dashboards elsewhere.

Developing a Multimodal Classroom Engagement Analysis Dashboard for Higher-Education

Developing learning analytics dashboards (LADs) is a growing research interest as online learning tools have become more accessible in K-12 and higher education settings. This paper reports our multimodal classroom engagement data analysis and dashboard design process and the resulting engagement dashboard. Our work stems from the importance of monitoring classroom engagement, which refers to students' active physical and cognitive involvement in learning that influences their motivation and success in a given course. To monitor this vital facade of learning, we developed an engagement dashboard using an iterative and user-centered process. We first created a multimodal machine learning model that utilizes face and pose features obtained from recent deep learning models. Then, we created a dashboard where users can view their engagement over time and discover their learning/teaching patterns. Finally, we conducted user studies with undergraduate and graduate-level participants to obtain feedback on our dashboard design. Our paper makes three contributions by (1) presenting a student-centric, open-source dashboard, (2) demonstrating a baseline architecture for engagement analysis using our open-access data, and (3) presenting user insights and design takeaways to inspire future LADs. We expect our research to guide the development of tools for novice teacher education, student self-evaluation, and engagement evaluation in crowded classrooms.

#3226 DEVELOPMENT AND USABILITY ASSESSMENT OF AN AI-ENHANCED DASHBOARD SUPPORTING AVF MANAGEMENT IN CLINICAL PRACTICE

Abstract Background and Aims Despite the rise in publications reporting Artificial Intelligence (AI) solutions in healthcare, their usage in clinical practice remains limited. Prominent barriers for their implementation in clinical practice are the lack of integration of AI-based tools in the hospital health information system and clinical workflows. We previously developed an AVF Failure risk score to assess the expected incidence of AVF failures within 3 months based on routinely collected dialysis data. The risk score showed high accuracy (AUC = 0.81) and calibration. Based on this risk score, we developed an AI-enhanced dashboard supporting the management of AVF in hemodialysis patients. The usability and usefulness of the AI-enhanced AVF management dashboard was evaluated in a pilot quality improvement program. Method A quality improvement framework was used to evaluate the usability of an AI-enhanced dashboard for AVF management (Fig. 1A). First, healthcare professionals (HCP) were involved to provide inputs for model design, endpoint definition, and potential predictors through various qualitative interview techniques such as brainstorming sessions and focus groups (Phase I). A machine learning model was then developed to assess the expected incidence of AVF failure within 3 months. The model used routinely collected clinical data to minimize additional burden on clinical workflow. (Phase II). After developing and validating the model, a model error analysis was conducted using case studies methodology to improve model performance (Phase III). Once the model reached a good accuracy, think-aloud interview was used to abstract HCP needs and to design a user interface for model output. A user-friendly dashboard was developed to display the risk of AVF failure within 3-months and patient's vascular access history (Phase IV). This solution was integrated in the health record system and updated monthly with the latest risk score and medical information. A pilot quality improvement program was conducted in 15 clinics across 4 countries (IT, CZ, SK, SG) from May to December 2022. HCP were asked to evaluate their agreement with the risk score using a 5-point Likert scale (completely disagree to agree) and assess the usefulness of the dashboard in clinical decision making. At this stage the dashboard is not used for medical decision making (Phase V). The evaluation results were compiled into a monthly report and shared with all the HCP involved in the project to promote awareness and engagement. After 3 months of usage, a round-table discussion was held with all participants to gather their feedback and discuss clinical cases (Phase VI). Results Access to the AI-enhanced dashboard increased during the evaluation period. In the first two months of the pilot quality improvement program, the AI-enhanced dashboard was evaluated for 20% patients with an AVF, a proportion that doubled by the end of the program (Fig. 1B). HCP reported 90% agreement with risk model rating (Fig. 1C), suggesting that the model capture relevant physiological status of the patient and its output is understandable by the healthcare staff. Qualitative analysis of round-table discussions showed that the dashboard is perceived as useful and clear. HCP suggested few changes in the user interface to enhance actionability and improve clinical workflow integration. Conclusion Our results support the usefulness and intelligibility of the AI-enhanced AVF management dashboard. The advance of AI-models highlighted the importance to develop new strategies for their integration and evaluation in clinical practice. The engagement of HCP in the early phases of AI-model design and development grants a continuous feedback loop bringing clinical and AI world together. A clinical perspective in dashboard design reflects the needs of healthcare professionals and enhance consideration of patient medical needs. The synergy between theoretical and clinical perspective is crucial to improve model performance, usability, and implementation in clinical practice.

PERANCANGAN DASHBOARD LAPORAN PENJUALAN PADA TOKO HBI

HBI is an accessories store that sells women's jewelry accessories. HBI Shop sells various women's jewelry products such as bracelets, rings, necklaces, earrings and other custom accessories. Each sales activity must have a sales document that contains information about sales reports. sales reports at HBI Stores still use Microsoft Excel, so it takes a long time to get information about sales report developments. Increasing product sales requires implementing a business strategy to improve the quality of the company, such as packaging it into a visualization on the dashboard.
 Dashboard is a tool for presenting concise information in diagrams, visual indicators, or graphics in the form of information. The purpose of this dashboard design is to form a sales report visualization display that users can use to see the situation in the company's sales reports. The data used is sales data from 2021 to 2022. The dashboard design method that will be used is the prototyping method. The sales report results visualized are in the form of a sales report dashboard at the HBI Store which can make it easier for HBI Shop owners to find out information on sales reports and assist companies in making decisions.

PERANCANGAN DASHBOARD UNTUK MEMANTAU KINERJA DOSEN FAKULTAS TEKNOLOGI INFORMASI DI UNIVERSITAS TARUMANAGARA

In every organization, it should be able to manage all processes quickly. Therefore, the institution monitors the entire process of lecturer performance so that it can achieve benefits and improve quality. This Dashboard design uses the prototyping method, by observing this information we can create a Web-based Dashboard to monitor the performance of the Faculty, using the UML method in system design and using mySql and Xampp to create a web-based Dashboard visualization. The results of making this Dashboard can provide ease in monitoring the overall performance of the FTI Faculty.