SciTech Framework

Evaluation of a Web-based Medical Record System: A Black Box Testing Approach

Muhammad Reksa Ariansyah — 2020-08-18

The efficient implementation and evaluation of web-based medical record systems are crucial for enhancing healthcare management and improving patient care. This research focuses on evaluating the functionality and usability of a web-based medical record system through the application of black box testing. The objective is to assess the system's performance and effectiveness in real-world scenarios, including patient registration, login functionality, medication data management, payment processing, doctor and administrative functionalities, and patient diagnosis outcomes. The alpha testing phase involved rigorous black box testing, ensuring that the system operates according to its intended tasks without knowledge of its underlying code. The findings demonstrate the successful completion of essential functionalities, such as robust error handling during login attempts, accurate retrieval and display of patient data, and seamless navigation for administrators, doctors, and patients. The system provides an intuitive user interface, allowing efficient access to comprehensive medical information. The outcomes of this research contribute to advancing web-based medical record systems, providing healthcare professionals with a reliable tool for managing patient records and streamlining healthcare processes. Additionally, the insights gained from the black box testing approach serve as valuable guidance for future system improvements and expansion. The results affirm the system's feasibility and highlight its potential to positively impact healthcare delivery and patient outcomes.

Advancing Hand Hygiene Practices: Developing a Sensor-Driven Touchless Hand Wash System

Ari Hadi Wibowo — 2020-08-18

Effective hand hygiene practices are crucial in preventing the transmission of infectious diseases. In light of the global COVID-19 pandemic, the demand for touchless hand wash systems has intensified, aiming to minimize the risk of cross-contamination and promote convenient and hygienic handwashing experiences. This research presents the design, development, and evaluation of a touchless automatic hand wash system utilizing microcontroller technology. The system eliminates the need for physical contact with surfaces through the integration of sensor technologies and a central microcontroller controller. The research objectives include assessing the system's effectiveness compared to traditional hand wash methods, evaluating user satisfaction and acceptance, and analyzing the cost-effectiveness and practicality of implementation. The study employs a mixed-methods approach, incorporating design and development processes, comparative experiments, user feedback, and surveys. The findings contribute to the enhancement of hand hygiene practices, offering insights into the efficacy of touchless technologies and their potential impact on public health. The research outcomes provide valuable information for policymakers and stakeholders considering the implementation of touchless hand wash systems in various settings, with the ultimate goal of reducing the spread of infectious diseases and improving overall hygiene practices.(*JFR*)

Tracking Tourist Visits: A Computer Vision Approach using Amazon Rekognition for Data Processing and Analysis

Jack Febrian Rusdi — 2020-08-18

This research presents a novel approach for tracking tourist visits using computer vision technology and Amazon Rekognition for data processing and analysis. The proposed method aims to provide valuable insights into understanding tourist behavior, optimizing tourist destinations, and aiding tourism development strategies. The research methodology consists of three main stages: digital object capture, data processing, and information processing. Digital objects, represented by tourists, are captured through cameras strategically placed in the monitored area. The captured images are then sent to a server, where they undergo data processing. Utilizing Amazon Rekognition, the images are analyzed to identify the number of people in each image. The processed data is stored in a database, allowing for further analysis and retrieval. Information processing involves filtering the data based on time constraints and specific criteria, followed by calculations to generate meaningful insights. The obtained information is displayed in a web-based format, providing tourism destination managers with valuable reports and visualizations for decision-making purposes. The research contributes to the understanding of tourist behavior by utilizing computer vision technology and machine learning algorithms. It enables tourism destination managers to optimize resource allocation, enhance tourist experiences, and formulate effective tourism development strategies. The proposed approach offers potential for further integration with other technologies and the creation of a comprehensive monitoring and reporting system for tourist destinations.

Discussion of the method of tracking the number of visits captured through CCTV cameras
Discussion of the stages and steps in the process from capturing images to producing reports on the level of tourist visits
Utilization of Computer Vision technology integrated with Amazon Rekognition.