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Pengembangan Aplikasi Telemedicine Carevul Sebagai Optimalisasi Pelayanan Kesehatan Berbasis Cloud Nurul Ismawati; Egia Rosi Subhiyakto
Infotekmesin Vol 15 No 1 (2024): Infotekmesin: Januari, 2024
Publisher : P3M Politeknik Negeri Cilacap

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35970/infotekmesin.v15i1.2150

Abstract

Telemedicine is a healthcare service that utilizes information and communication technology to offer virtual medical services, eliminating the need for the physical presence of patients and medical professionals in a single location. The development of telemedicine enhances the accessibility of healthcare services, helps overcome constraints of time and resources, and can reduce healthcare service costs. Consultation services at UPT Puskesmas Tirto Kota Pekalongan are still manual and have not fully optimized the use of technology. The process of meeting the needs for consultations with doctors requires a direct visit to the health center, resulting in a significant use of time and resources. Therefore, a website-based system is needed as a platform to provide easy and practical online telemedicine consultation services. This research employs the prototyping development method, allowing users to participate by evaluating and providing feedback during the development process. System testing using Blackbox and User Acceptance Test (UAT) shows a user satisfaction percentage of 89.6%, indicating that the majority of users strongly agree with the developed website. The development of this website is expected to facilitate users and medical personnel in conducting online consultations anywhere and anytime without the need to visit the health center.
User-Centered Design Approaches to Enhance Employee Attendance Applications Wahyu Maulana Prawiro; Egia Rosi Subhiyakto
Advance Sustainable Science Engineering and Technology Vol. 6 No. 3 (2024): May - July
Publisher : Science and Technology Research Centre Universitas PGRI Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26877/asset.v6i3.798

Abstract

In the current digital era, Human Resource Management (HRM) has undergone significant transformation with the advent of technology, including employee attendance applications. These applications replace inefficient and error-prone manual methods, facilitating effective attendance, leave, and monitoring of working hours. This study aims to apply a user-centered design (UCD) approach to developing an employee attendance application to enhance user experience (UX). Evaluation using the System Usability Scale (SUS) revealed a usability score of 86.37, classified as "Excellent." This score reflects positive user reception and underscores the importance of user-focused approaches in development. This research shows how user-tailored features can improve attendance management efficiency and effectiveness by aligning the application with user workflows. Despite the positive results of this study, recommended to continue periodic design iterations with regular user feedback to improve usability and satisfaction.
Implementing Long Short Term Memory (LSTM) in Chatbots for Multi Usaha Raya Ilham Dwi Raharjo; Egia Rosi Subhiyakto
Advance Sustainable Science Engineering and Technology Vol. 6 No. 4 (2024): August-October
Publisher : Science and Technology Research Centre Universitas PGRI Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26877/asset.v6i4.934

Abstract

The furniture industry is an important sector in Indonesia that supports the economy and provides quality furniture. An in-depth understanding of the furniture business is essential for industry players to improve operational efficiency and customer satisfaction. This research aims to develop a chatbot for Multi Usaha Raya furniture company to improve customer service and operational efficiency. In its development, the Machine Learning Model Development Life Cycle (MDLC) and deep learning approach using the Flask platform are employed. LSTM, a type of recurrent neural network (RNN) architecture capable of handling long-term dependencies, is utilized in this chatbot model. The model training results show an accuracy of 99%, validation accuracy of 96%, loss of 0.1%, and validation loss of 0.2% after 200 epochs, demonstrating the effectiveness of the LSTM algorithm for developing a chatbot in this company.