Development and Evaluation of a No-Code Key Performance Indicator (KPI) Dashboard Using Base44 for Divisional Performance Monitoring (Case Study: Macwish Indonesia)
DOI:
https://doi.org/10.47709/cnahpc.v8i4.9803Dimension:
Keywords:
Dashboard, Key Performance Indicator, No-Code, Base44, Performance MonitoringAbstract
Divisional performance monitoring that still relies on spreadsheets prevents key performance indicator (KPI) achievements from being presented in a centralized manner, requires manual data processing, and slows down evaluation and decision-making. This condition was found at Macwish Indonesia, a macaroni-based food and beverage business that originated from a campus entrepreneurship program. This study aims to develop and evaluate a no-code KPI dashboard built on the Base44 platform to monitor divisional performance. The study employed a Research and Development (R&D) approach with the Waterfall model, comprising problem identification, literature review, data collection, design and implementation, testing, and evaluation. User requirements were elicited through observation and semi-structured interviews with three division heads. The resulting dashboard provides login, division and KPI management, performance input, a monitoring dashboard, and quarterly evaluation features. Testing was conducted using Black Box Testing and User Acceptance Testing (UAT) with a four-point Likert scale. All 18 Black Box Testing items passed in the functional scenarios tested, which did not cover access rights, data security, or system resilience. The UAT, involving three division heads and one expert practitioner, yielded scores of 96.25% for system functionality, 97.50% for ease of use, 98.75% for performance and reliability, and 96.25% for user satisfaction, with an overall mean of 97.19% ("Very Good" category). These results are an initial indication of user acceptance in a single case study with four respondents and therefore cannot be generalized, and the advantage over spreadsheets was not measured quantitatively. This study has so far demonstrated only functional feasibility and initial user acceptance, not an improvement in monitoring effectiveness. It contributes to the literature on the use of no-code platforms for performance monitoring systems and presents a replicable implementation example for small and medium-sized food and beverage businesses.
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