Amin Suharjono
Electrical Engineering Dept., Institut Teknologi Sepuluh Nopember (ITS) Surabaya 60111, Indonesia Electrical Engineering Dept., Politeknik Negeri Semarang (POLINES) Semarang 50275, Indonesia

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Journal : JOIV : International Journal on Informatics Visualization

A Low-Cost Nursing Robot with Telemedicine using ESP32 and Robot Operating System-based Suharjono, Amin; Apriantoro, Roni; Supriyo, Bambang; Wardihani, Eni Dwi; Yunanto, Bagus; Hidayat, Wahyu; Prasetio, Katon; Reynaldi, Rindang; Fahrul Aji, Achmad
JOIV : International Journal on Informatics Visualization Vol 9, No 2 (2025)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62527/joiv.9.2.2793

Abstract

The COVID-19 pandemic has presented unprecedented challenges to the healthcare sector, particularly frontline healthcare workers. These professionals face high infection risks and physical and mental exhaustion due to intensified workloads and staffing shortages. Robots are seen as a potential solution to this predicament, performing tasks such as delivering supplies and monitoring patients. However, widespread adoption of such robots, particularly in resource-constrained settings, has been hampered by the exorbitant costs associated with their acquisition and maintenance. To address this problem, the authors developed a low-cost nursing robot based on the ESP32 and the Robot Operating System (ROS). This robot facilitates hospital logistics and patient monitoring through telemedicine. The robot is controlled by remote control or Wi-Fi connection through the RViz Graphical User Interface (GUI) and uses odometry and PID control methods to follow specified paths autonomously. Accessible via local area networks and the Internet, the telemedicine system demonstrates robust performance with minimal X and Y axis control errors, zero packet loss, an average Round Trip Delay (RTD) of less than 150 ms, and jitter values of less than 20 ms, in line with TIPHON standards. This innovation provides a cost-effective solution to support healthcare workers during the ongoing health crisis. In future development, incorporating LiDAR, computer vision, and AI-based decision-making into the robot can facilitate obstacle detection and real-time decision-making to enable fully autonomous movement. These advancements will enhance the robot’s adaptability and accuracy in navigation and positioning.
Performance Evaluation of RESTful Services and AMQP Protocol in Cashless Parking Payment Mobile Apps Farrabi, Dimas Rizky; Suharjono, Amin; Apriantoro, Roni
JOIV : International Journal on Informatics Visualization Vol 9, No 4 (2025)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62527/joiv.9.4.2798

Abstract

This study explores the challenges surrounding the inefficient use of e-parking applications in Semarang, Indonesia, where issues such as illegal parking and an underperforming digital infrastructure have resulted in lost regional revenue from parking fees. The lack of a robust, user-friendly system has hindered user adoption and limited the application’s effectiveness in managing urban parking. To address these concerns, we developed a modernized e-parking application focused on enhancing usability and system performance. The application design prioritizes intuitive user interaction and seamless integration with existing parking infrastructure. Usability testing using the System Usability Scale (SUS) yielded a score of 80, indicating a high level of user satisfaction and ease of use. In addition, we conducted a comparative analysis of two communication protocols—RESTful and Advanced Message Queuing Protocol (AMQP)—during key application processes, such as vehicle check-in and check-out transactions. Results revealed that AMQP significantly outperformed RESTful in terms of Quality of Service (QoS), particularly with lower response times and minimal packet loss. AMQP consistently met TIPHON standards, achieving a QoS index score of 4, further supporting its suitability for real-time transaction systems in urban environments. This study highlights the critical role of technology optimization in addressing urban mobility issues, reducing illegal parking, and improving public service efficiency. Looking ahead, future development should focus on refining secondary features and introducing new capabilities such as reservation systems, dynamic pricing, and real-time availability tracking to further enhance user engagement and operational effectiveness. The findings emphasize the potential of well-designed e-parking systems to transform urban parking management through smart, scalable technology.
Predicting Battery Storage of Residential PV Using Long Short-Term Memory Rakasiwi, Rizky Khaerul Maulana; Kurnianingsih, Kurnianingsih; Suharjono, Amin; Enriko, I Ketut Agung; Kubota, Naoyuki
JOIV : International Journal on Informatics Visualization Vol 8, No 1 (2024)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62527/joiv.8.1.1603

Abstract

Solar power panels, or photovoltaic (PV), have recently grown rapidly as a renewable alternative energy source, especially since the increase in the basic electricity tariff. PV technology can be employed instead of the state electricity company to reduce the electricity used. Indonesia is one of the countries that have great potential in producing electricity from PV technology, considering that most of Indonesia's territory gets sunlight for most of the year and has a large land area. Considering the benefits of PV technology, it is necessary to carry out predictive monitoring and analysis of the energy generated by PV technology to maximize energy utilization in the future. The Internet of Things (IoT) and cloud computing system was developed in this research to monitor and collect data in real-time within 27 days and obtained 7831 data for each parameter that affects PV production. These data include data on the light intensity, temperature, and humidity at the location where the PV system is installed. The feature selection results using Pearson correlation revealed that the light intensity parameter significantly impacted the PV production system. This research used the Long Short-Term Memory (LSTM) method to predict future PV production. By tuning hyperparameters using 3000 epochs, the resulting RMSE value was 171.5720. The results indicated a significant change in the RMSE value compared to 100 epochs of 422.5780. This model can be applied as a forecasting system model at electric vehicle charging stations, given the increasing use of electric vehicles in the future.     Keywords— Forecasting; energy; Photovoltaic; LSTM; Internet of Thing.Â