DEVELOPMENT OF AN IOT-BASED EARLY WARNING AND MONITOR SYSTEM DASHBOARD IN A LOW INFRASTRUCTURE ENVIRONMENT FOR COAL MINES

In operations that involve heavy machinery, it is crucial to ensure that safety is repeatedly checked, both for the equipment used and the personnel operating it. One example of a sector that requires such safety standards is coal mining. In coal mines, numerous heavy machines are used, which mea...

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Bibliographic Details
Main Author: Syahrul Surya Putra, M
Format: Final Project
Language:Indonesia
Online Access:https://digilib.itb.ac.id/gdl/view/85267
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Institution: Institut Teknologi Bandung
Language: Indonesia
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Summary:In operations that involve heavy machinery, it is crucial to ensure that safety is repeatedly checked, both for the equipment used and the personnel operating it. One example of a sector that requires such safety standards is coal mining. In coal mines, numerous heavy machines are used, which means that the personnel operating these machines need to be in optimal condition. Therefore, an early warning and monitoring system dashboard is needed to ensure that the safety of all involved personnel can be monitored at all times. The dashboard for this early warning and monitoring system will be developed using a user-centered design approach to better understand the specific needs of the users. The design of the dashboard is expected to achieve effectiveness in use as a usability goal and helpfulness as a user experience goal. To test whether these usability and user experience goals are met, usability testing will be conducted with metrics such as task completion rate, system usability scale, and single ease question. For the high-fidelity design, scores above 78% for task completion rate, 98.75 for system usability scale, and 6.97/7 for single ease question were obtained. This indicates that the developed dashboard has met the established usability and user experience objectives. It is hoped that this dashboard will enhance safety and efficiency in coal mining operations through real-time monitoring and early warnings.