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Spatial Semantic Analysis and Origin-Destination Prediction Based on Extensive GPS Trajectory in Jakarta

2025-02-07T09:26:31+00:00

The rapid growth of mobility data from GPS trajectories offers unprecedented opportunities to gain deep insights into human mobility behavior, with significant implications for urban planning, traffic management, public transportation optimization, emergency response, and smart city development. However, a key challenge lies in transforming raw GPS trajectory data, consisting of sequences of coordinates and timestamps, into meaningful, context-rich information that can support analysis and decision making. This study proposes a semi-supervised framework to enhance the contextual and semantic understanding of journeys, using Grab Jakarta GPS trajectory data as a case study. The framework involves extracting origin-destination pairs, augmenting the data with temporal (day, time) and spatial (postal code, land use) contexts through public datasets, assigning cluster labels to characterize groups of journeys, analyzing mobility patterns, and ultimately predicting trip destinations. Origin-destination clustering, performed using the DBSCAN algorithm, identified five meaningful clusters, achieving the highest silhouette score of 0.56 with epsilon = 7.0 and min_samples = 5. Subsequently, a regression-based prediction model was developed, employing nine algorithms, including three deep learning approaches. The LSTM model demonstrated the best performance, yielding a mean squared error of 0.0053 and a coefficient of determination (R²) of 86.20% in predicting trip destinations. These findings highlight the potential of integrating spatial-temporal enrichment and machine learning to derive actionable insights from GPS trajectory data.

A Web-Based Forecasting Approach to Estimating the Number of Low-Income Households Eligible for Social Food Aid Using Holt’s Double Exponential Smoothing

2025-03-16T12:38:07+00:00

This work presents a web-based forecasting methodology for predicting the quantity of low-income households qualified for social food assistance utilizing Holt’s Double Exponential Smoothing (HDES) technique. Precise assessment is crucial for governmental bodies and social welfare organizations to guarantee efficient aid distribution and effective resource allocation. The proposed method amalgamates time series forecasting models with a web-based application to deliver real-time predictions and accessibility for decision-makers. Historical data on low-income household statistics were employed to formulate and authenticate the forecasting model. The findings indicate that HDES delivers dependable short-term predictions with low error rates, accurately reflecting patterns in the data. This online application offers policymakers an effective means for monitoring socio-economic trends and enhancing the responsiveness of social assistance initiatives. This research contributes by integrating statistical forecasting with web-based applications to aid social policy decisions.

ECO-FISH: Enhanced Cloud Task Scheduling Using an Opposition-Based Artificial Fish Swarm Algorithm

2025-09-20T13:02:02+00:00

The rapid expansion of cloud computing has increased the complexity of task scheduling and resource management across heterogeneous and dynamic environments. Conventional heuristic methods often suffer from premature convergence, resulting in imbalanced virtual machine (VM) utilization. To address these challenges, this study proposes ECO-FISH, a hybrid Opposition-Based Artificial Fish Swarm Algorithm (AFSA) designed for efficient cloud task scheduling. AFSA is selected for its swarm intelligence behaviors—prey, follow, and swarm—which enable effective local exploration with relatively low computational cost. To enhance global exploration, Opposition-Based Learning (OBL) is incorporated by evaluating opposite task–VM mappings, allowing the algorithm to escape local optima and maintain population diversity. This synergy improves the balance between exploration and exploitation while retaining algorithmic simplicity. The proposed ECO-FISH algorithm is implemented using CloudSim and benchmarked against GA, PSO, and the baseline AFSA using three workload distributions: uniform, normal, and stratified. Experimental results demonstrate that AFSA alone reduces makespan by 28–45%, increases throughput by 34–84.9%, and improves utilization by 44.12–64.59% compared to GA. The OBL enhancement in ECO-FISH provides additional gains of up to 1.6%, showing the most significant improvement under heterogeneous, stratified workloads with high variance. Overall, AFSA performs well on uniform datasets, while ECO-FISH (AFSA with OBL) exhibits superior adaptability and stability in variable cloud environments.

Analytical Estimation of Jitter for the MMPP-N Traffic Model

2025-06-10T20:29:45+00:00

Nowadays, consumers demand powerful and adequate communication techniques to meet their desires for communication and exchange. These techniques, such online chats, VoIP, video conferencing, and AR/VR services, require well-defined Quality of Service (QoS) properties, such as minimal transmission delay, high throughput, low inter-arrival time, and low packet delay variation. Among these, jitter is an important QoS parameter in IP networks that can affect the performance of real-time applications and services. In this paper, we provide an overview of jitter and relevant studies. We propose an analytical calculation of jitter for the MMPP-2 and MMPP-N traffic models and analyze the jitter behavior for voice traffic.

Development of an Interactive Augmented Reality-Based Weather Forecast Simulation with Automated BMKG Data for Enhanced TV Broadcasting

2024-11-21T11:48:58+00:00

The weather has a significant impact on human activities. Weather data are used in various industries. Recently, Augmented Reality (AR) technology has been widely used to enhance content representation through multidimensional visualization. Television stations utilize AR to capture viewers' attention in the programs they broadcast; AR has the potential to enhance communication between viewers and the content. Our research on developing an AR application for an engaging immersive reality implementation aims to simulate a virtual stage in TV broadcasts, displaying real-time weather data from the Indonesian Meteorology, Climatology, and Geophysics Agency (BMKG) using the latest SQL database. The implementation of the AR system as a multimedia mode in delivering weather information increases the attractiveness and engagement of viewers. This system operates by translating weather data from SQL into key parameters for pre-built 3D visualizations, which appear at marked coordinates. This application runs on the Windows platform and can be managed via Android, allowing presenters to convey engaging scenarios. Research on East Java BMKG data showed that the design function to transform BMKG data into a virtual weather animation data was significantly successful in both synchronizations.

Architectural Consideration for Gamified Learning Systems: An Exploration of Offline-First Progressive Web Application

2024-11-10T01:37:31+00:00

This paper presents a detailed exploration of the architectural development of an offline-first Progressive Web Application (PWA) prototype, specifically designed for gamified learning systems. The core research involves the technical examination of the PWA prototype, emphasizing its architectural design and crucial offline capabilities in the context of gamification elements. The study highlights the critical architectural components necessary for offline-first PWAs in educational settings, including the usage of PWA service workers and caching mechanisms. Key gamification features were identified and integrated, differentiating between those suitable for offline settings (like Points Systems, Badges/Achievements, Progress Tracking) and those requiring online connectivity (like Global Leaderboards and Social Interactions). The prototype, built using the React frontend framework and Supabase BAAS for the backend, demonstrates the potential of offline-first strategies in educational technology. It provides practical insights into the challenges and opportunities of maintaining engaging, uninterrupted learning experiences, particularly in low-connectivity environments. In conclusion, while offline-first PWAs effectively support learning activities with intermittent internet access, the research suggests that a careful balance must be managed between robust offline functionality and the richness of dynamic online interactivity to fully maximize the benefits of gamification.

From Clicks to Cradles: Mapping the Digital Landscape of Maternal Support through Bibliometric Analysis

2025-02-26T02:52:27+00:00

Online platforms have emerged as crucial parenting information sources in the digital age, revolutionizing how moms seek help, make choices, and deal with the difficulties of raising children. This change demonstrates the increasing scholarly interest in comprehending how digital resources affect the experiences of mothers. With an emphasis on performance and co-citation analyses, this study offers a bibliometric analysis of studies on mothers' interactions with online parenting resources. After a thorough screening process, 453 studies were eventually included out of the 1,352 records that were first found using the Web of Science database. While co-citation analysis finds thematic clusters like maternal mental health, digital parenting literacy, and online community engagement, performance analysis identifies important contributors, such as top authors, organizations, and nations. The findings show that, especially in reaction to the COVID-19 pandemic, academics are increasingly focusing on digital platforms as the main sources of parenting information. The results indicate that although mothers can benefit greatly from online resources, problems with disinformation and inequalities in digital literacy still exist. Since it highlights the necessity of trustworthy digital parenting resources to support maternal mental health and child development, this study is in line with Sustainable Development Goal (SDG) 3: Good Health and Well-Being. For a more comprehensive understanding of digital parenting practices, future research should incorporate qualitative methods and investigate cross-cultural viewpoints. A systematic road map for upcoming studies, the creation of policies, and real-world applications in the rapidly changing field of online parenting support is offered by this bibliometric analysis.