Indonesia ranks third globally in e-Health utilization, which is carried out by 57% of respondents. It shows that public awareness of the importance of technology in the health sector has changed significantly. This awareness needs to be continuously enhanced. However, this is not in line with the use of health services for pregnant women. This is related to the factor of adopting health technology that is in line with the features of e-Health itself. This research examines sentiment analysis on 17 e-Health in Indonesia to gain in-depth insight into user perceptions and responses related to features, especially Technical Factors and Security/Privacy Factors taken through the Google Playstore. The methods include sentiment labelling with TextBlob and modelling using Convolutional Neural Network (CNN). The overall model produced an accuracy of 71.67%, indicating that the model is most reliable in classifying aspects of Perceived Ease of Use and Performance Risk. This research is expected to provide valuable insights for application developers and the government in optimizing the quality and trust in e-Health in Indonesia.
Precise and prompt product identification is vital for efficient inventory management in the retail grocery industry. While deep learning models have significantly improved in classification accuracy, dynamically changing environments, seasonal variations, and new products pose significant challenges. These factors create new data on which pre-trained models are not adequately trained, leading to a substantial drop in accuracy. Continual learning has emerged as a solution, allowing models to adapt to new data without forgetting previously learned information. However, this solution still suffers from catastrophic forgetting, especially when dealing with large datasets. This paper aims to explore strategies to mitigate catastrophic forgetting and develop a robust model for fruit and vegetable classification in the retail sector. The models used are two memory-based models (DER++ and ER) and two regularization-based models (EWC and EWC + CPR). The result indicates that memory-based models outperform regularization-based models, with Dark Experience Replay++ (DER++) showing the highest accuracy in both classincremental and task-incremental settings. Memory-based models are less affected by catastrophic forgetting as they have direct access to past data, allowing them to have better knowledge preservation. Furthermore, the study also reveals that hybrid approaches (e.g., EWC + CPR) can further enhance retention accuracy.
School bullying remains an important issue affecting the safety and well-being of students. This study introduces a novel anti-bullying system that utilizes Internet of Things (IoT) sensors to predict and prevent bullying in educational Settings. The project consists of four main parts: introduction, literature review, proposed methodology, and project timeline with milestones. Through an extensive analysis of bullying in schools, including existing research and empirical data, the study identifies powerful strategies to mitigate such incidents. The proposed system combines voice recognition technology with the Internet of Things, utilizing hardware components such as Raspberry Pi, Arduino and various sensors to detect and respond to bullying. Voice sensors are strategically installed throughout the campus to identify and analyze sensitive speech and distress signals, immediately alerting school authorities for timely intervention. This integrated approach aims to enhance campus safety, ensure the physical safety of students and promote a healthy development environment. The success of the project relies on the joint efforts of schools, students, parents and the wider community to comprehensively address and reduce bullying. This paper Outlines the technical specifications, design, and implementation phases, and critically evaluates the effectiveness and limitations of the system.
Event Monitoring in Heterogeneous networks (HetNets) is extremely challenging due to the diverse network technologies and protocols involved. This study addresses the common challenge of event correlation in Heterogeneous Networks (HetNets). Which is crucial for ensuring network reliability and efficiency. In HetNets, we propose a novel way to integrate ServiceNow’s AIOps platform with advanced machine learning techniques to enhance correlation accuracy and reduce response times. Based on our findings, the proposed methodology outperforms existing solutions, resulting in significant improvements in both network performance and reliability. The research encompasses the integration of the ServiceNow platform with real-time HetNet of a big FinTech company. The platform ingests real-time data, advanced analytics, and predictive intelligence algorithms to detect, group, classify, and respond to network events. Key performance metrics, such as event detection accuracy, response time, and resource utilization, were systematically measured and analyzed. The findings indicate that the ServiceNow platform substantially enhances event correlation capabilities of HetNets. Significant improvements were observed in event detection accuracy and reduced response times when compared to distributed monitoring systems. The platform’s adaptability to different network configurations and its scalable architecture are important advantages. Additionally, the integration of machine learning techniques augments predictive intelligence capabilities, thereby reducing network downtime and enhancing automation and overall reliability. In conclusion, the ServiceNow platform’s advanced event monitoring mechanism provides a robust solution for heterogeneous networks. Future research will aim to further refine the machine learning models and expand the platform’s capabilities to support emerging network technologies.
The rail industry requires robust technology to support environmentally friendly and sustainable transportation, of which diesel generators are a key component. Currently, maintenance approaches for railway diesel generators tend to use preventive strategies that are less efficient and costly. Therefore, this study proposes the estimation of diesel generator health indicators to support predictive maintenance by utilizing multivariate stochastic approaches and exponential-based degradation models. This degradation model contains deterministic and stochastic parameters estimated using Bayes filter. In addition, to improve forecasting accuracy, this approach involves more than one type of measurement variable data and applies one of the multivariate analysis techniques, namely principal component analysis (PCA). The PCA variables are then used to determine the health indicators of diesel generators. As a case study, this research was conducted using data from a diesel generator on a power car. The results show that the health indicator is obtained when the tresshold has been reached.
