Engineering Headway Vol. 27

Abstract: Effective cancer pain management can enhance patients' well-being and quality of life. However, the development of related digital tools in nursing remains limited. This qualitative descriptive study was conducted in two Indonesian hospitals, focusing on discussions with 14 certified oncology nurses. Results showed that most participants were female nurses (85.7%) aged 25-51 years. Four main themes emerged: 1) Integrating multidisciplinary approaches in pain management, 2) Innovating digital technology in pain management, 3) Challenging digital technology in pain management: infrastructure, patient demographics, and nurse-related factors, and 4) Supporting strategies for patient pain management. There's potential in integrating digital technology with multidisciplinary strategies to improve cancer pain management. The OOPs prototype developed is expected to illustrate how smartphone applications can be implemented to control pain in cancer patients. Future studies should aim to refine the prototype, test it involving patients and healthcare professionals across various settings, and validate its efficacy across different cultures.

Abstract: Online health communities generate vast knowledge on various topics, making it challenging to track latent themes and correlations among key variables. Identifying these hidden variables and their relationships can enhance user engagement and content curation. This paper introduces a novel approach using fuzzy cognitive maps to uncover these variables and causal relationships in online health communities, providing users with valuable insights for disease management and treatment. The study employed Latent Dirichlet Allocation (LDA) to generate topics from an online health forum, using this as evidence to construct fuzzy cognitive maps. The proposed system reflects disease development and offers significant insights for managing health conditions. The findings have theoretical and practical implications for developing online recommendation systems in the healthcare domain. This study contributes to the literature by proposing a method for identifying key variables and relationships, aiding health professionals and patients in understanding and managing health conditions. Future research will explore the system’s effectiveness in real-world settings and the role of user-generated content in enhancing recommendation systems.

Abstract: Diabetes mellitus is a chronic disease that has become a serious global health problem. The high prevalence of diabetes mellitus and the lack of public awareness of the risk of diabetes are serious problems. Early detection and prevention of this disease are important. However, early detection is often not optimized. The development of information and communication technology causes the use of technologies such as machine learning and web-based applications to be a potential solution to increase public awareness of the risks and early detection of diabetes mellitus. Therefore, this research develops a web-based diabetes mellitus prediction application using machine learning technology with history and advice features that can be used as a health assistant for the general public regarding their diabetes risk.

Abstract: Schizophrenia is a highly debilitating mental disorder, disrupting the thought processes, emotions, and behaviors of those afflicted, often blurring the line between reality and imagination. Residual schizophrenia is a specific subtype of Schizophrenia, in which the patients experience short-term memory impairment that hinders their ability to think and carry out daily activities. We have developed an innovative method to boost short-term memory in residual schizophrenia patients. Our approach takes the form of a serious game, named Memoria, developed using GDLC (Game Development Life Cycle) framework. During the treatment, participants are tasked to navigate a specific path, aiming to memorize it once the path traversal is completed. Over the course of five consecutive days, participants engage with Memoria, and we compare their short- term memory capabilities before and after the treatment. Our findings indicate a significant improvement in short-term memory for those who underwent the treatment (8.44 points, 9.40%), in contrast to those who did not (1.55 points, 1.69%).

Abstract: This paper is trying to address the challenge faced by athletes or recreational runners to predict the performance based on historical performance and current condition. Not only based on individual performance, but the sportsman also needs to understand with current health condition, what the peak performance that can achieve. Recent development of big data technology with streaming pipeline is possible to address the challenge. With Strava Application as an edge technology that user used to gather the data and Big Data platform to process the data in stream, this research is trying to address the problem. This paper presents the automatic big data pipeline that retrieve data from multiple smartwatch platform, process the data with machine learning model and visualize the data and result in web visualization. Multi-Layer Perceptron model is providing the best performance with R-square 0.985 and MAPE 0.047 or 4.7%.

Abstract: Infant growth is an important indicator of their health and development, making accurate and regular measurements essential. However, traditional measurement methods are often inaccurate and require frequent visits to healthcare centers. In this report, we developed a infant height measurement application using smartphone camera technology and image processing algorithms to provide a practical and accurate solution. The application not only simplifies height measurement but also facilitates the recording and analysis of infant growth data. Parents can store measurement history digitally and share this data with doctors or healthcare professionals for a more comprehensive assessment. This feature is especially useful for the early detection of potential growth issues, such as stunting, allowing for timely medical intervention. Testing shows that the application has varying degrees of accuracy, with some subjects demonstrating high accuracy while others require further adjustments for consistent results.

Abstract: Near Vertical Incidence Skywave (NVIS) is one of the High Frequency (HF) radio communication modes that could be used for many activities on any circumstances due to its advantages. However, to ensure a communication’s success the working frequency or carrier frequency selection should be taken considerately based on the ionosphere condition that varies over time. This paper explains the development of the opening channel information system based on ionosphere observation over Kupang district, Nusa Tenggara Timur by pulsed HF radar or Ionosonde. The system periodically generates information from one-day observation of the Ionosonde’s echo intensity data (Ionogram) which were appended and converted to an image object. Further, the image processing technique is performed to reveal the information of the usable HF frequency range. The Connected Component Labelling and the Contour Detection methods were implemented to automatically determine the usable frequency of both the lowest and the most frequency values from echo data. The range of the frequency that could be reflected by the ionosphere is provided as the boundary of a lower and upper carrier frequency that could be chosen in NVIS communication as called usable HF frequency. This frequency-range information then could be used as a reference for selecting the carrier frequency with a radius up to 300 km from the center point of ionosphere observation.

Abstract: This research proposes an approach to trajectory prediction using the Interacting Multiple Model Kalman Filter (IMM-KF), which forecasts the future positions of multiple sur rounding objects and determines their crossing status - whether they will cross or stay on their paths. The method accommodates diverse object behaviors by integrating constant velocity and constant acceleration models. Building on previous work, this approach enhances the accuracy of trajectory prediction for maneuvering objects, enhancing decision-making capabilities for autonomous trams to improve safety. The proposed method, which integrates a trajectory motion model with the IMM-KF algorithm, has been validated through both simulations and real-world testing on a vehicle platform. The results demonstrate significant improvements in safety and risk assessment.

Abstract: Indonesia, situated at the convergence of active tectonic plates and surrounded by active mountain ranges known as the Ring of Fire, enjoys advantages such as fertile land and a tropical climate. However, these benefits also pose threats when nature is not in its best state, rendering certain regions of Indonesia susceptible to natural disasters. The consequences not only entail material and human losses but also extend to the detriment of critical infrastructure, including communication networks. Communication disruptions in crucial conditions undoubtedly hinder rescue and recovery efforts. This study focuses on designing communication scenarios based on Mobile Ad-hoc Network (MANET) as a post-disaster communication solution, utilizing the NS-3 simulator with proactive routing protocols OLSR and DSDV, alongside an analysis of their performance in scenarios involving the addition of nodes. Performance metrics, including Packet Loss, Throughput, and Delay, are compared based on the TIPHON QoS standard. The experimental results demonstrate the superiority of OLSR routing performance over DSDV, with the best average throughput value at 2292.57 Kbps, a delay of 76.98 ms, and a packet loss of 0.12% for a scenario involving 150 nodes. This research aims to provide an alternative solution for developing post-disaster emergency communication networks and contribute to post-disaster rescue and recovery efforts.

Abstract: The ultra-rapid mobility of cellular device-to-device (D2D) users degrades network connections and enhances renewal interconnection issues, eventually lowering the overall throughput performance. Moreover, a limited cellular infrastructure in rural areas hinders smooth network communication, leading to massive network outages and disconnections. Therefore, to maintain high data reception, the users require a safe moving zone, i.e., an area where the performance degradation is still tolerable. However, determining the safe moving zone of D2D users is categorized as a nonlinear problem that magnifies solutions into combinatorial possibilities. Based on that challenging issue, this study proposes a binary message-passing method to cluster safe moving zones by finding the minimum pathloss. The messagepassing technique splits the centralized D2D issues into local cases involving only adjacent D2D users. By enabling a distributive manner, the proposed algorithm determines the moving zone with low computational complexity. The evaluation results show that the proposed algorithm provides a higher homogeneity and completeness value than conventional clustering techniques, indicating a clustering model’s high fitness. The simulation result demonstrates that the proposed algorithm only requires 29 iterations with a high homogeneity of 0.727 and completeness of 0.752, Moreover, the proposed algorithm outperforms D2D conventional techniques regarding pathloss degradation. For 25 numbers of the dataset, the proposed algorithm only experiences 59.52% data loss, which provides more efficient performance than K-means and color graphs that correspond to 60.94% and 61.97%.