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Register: Jurnal Ilmiah Teknologi Sistem Informasi
Published by Universitas Pesantren Tinggi Darul ulum
ISSN : 25030477     EISSN : 25023357     DOI : https://doi.org/10.26594/register
Core Subject : Science,
Computer Science & IT
Register: Scientific Journals of Information System Technology is an international, peer-reviewed journal that publishes the latest research results in Information and Communication Technology (ICT). The journal covers a wide range of topics, including Enterprise Systems, Information Systems Management, Data Acquisition and Information Dissemination, Data Engineering and Business Intelligence, and IT Infrastructure and Security. The journal has been indexed on Scopus (reputated international indexed) and accredited with grade “SINTA 1” by the Director Decree (1438/E5/DT.05.00/2024) as a recognition of its excellent quality in management and publication for international indexed journal.
Arjuna Subject : -
Articles 3 Documents
 1 
Search results for , issue "Vol 11 No 2 (2025): July (In Progress)" : 3 Documents clear
Spatial Semantic Analysis and Origin-Destination Prediction Based on Extensive GPS Trajectory in Jakarta Simanjuntak, Humasak; Hutauruk, Agnes; Situmorang, Haryati; Silitonga, Yoshua
Register: Jurnal Ilmiah Teknologi Sistem Informasi Vol 11 No 2 (2025): July (In Progress)
Publisher : Information Systems - Universitas Pesantren Tinggi Darul Ulum

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26594/register.v11i2.5388

Abstract

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 Masrur, Mukhamad Masrur; Solikhin, Solikhin; Churum, Muhammad Walid Syahrul; Abdillah, M. Zakki; Putra, Toni Wijanarko Adi
Register: Jurnal Ilmiah Teknologi Sistem Informasi Vol 11 No 2 (2025): July (In Progress)
Publisher : Information Systems - Universitas Pesantren Tinggi Darul Ulum

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26594/register.v11i2.4922

Abstract

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 Shiddiqi, Ary Mazharuddin; Ciptaningtyas, Henning Titi; Leonardo, Jonathan; Rahma, Fayruz
Register: Jurnal Ilmiah Teknologi Sistem Informasi Vol 11 No 2 (2025): July (In Progress)
Publisher : Information Systems - Universitas Pesantren Tinggi Darul Ulum

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26594/register.v11i2.5340

Abstract

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.

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