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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 219 Documents
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Data Visualization of the Maturity Level From the Perspective of Business-IT Alignment at PT. XYZ Juwita, Oktalia; Arifin, Fajrin Nurman; Ailsya , Rachma; Nugrahani, Tri Agustina
Register: Jurnal Ilmiah Teknologi Sistem Informasi Vol 10 No 2 (2024): July
Publisher : Information Systems - Universitas Pesantren Tinggi Darul Ulum

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26594/register.v10i2.4619

Abstract

This article presents descriptive information about the results of the performance analysis and the maturity of the business-IT alignment process at PT.XYZ through the implementation of data-based visualization techniques within the SAMM perspective. This research is divided into four stages: preliminary research, data collection, data analysis, and organizational development analysis. Based on the results of the analysis using SAMM from the perspective of top-level management of an organization, PT. XYZ has achieved maturity in the attribute of good cooperation. This indicates that the company and organization understand the need for business-IT alignment for their development. However, the attainment level of maturity in the skills and communication attributes is less than optimal. This impacts the communication process and the distribution of information in the company’s development through IT implementation. This research only provides data visualization of the maturity achievements of PT. XYZ’s business alignment and information technology are based on the SAMM method. The process of analyzing the company's strategic alignment data and providing recommendations for implementing Information Technology to optimize the company's business was not included in this research.
Classification of Betel Leaf Diseases Based on Convolutional Neural Network to Increase Production Herbal Spice Materials Tri Wahyuningrum, Dr. Rima; Hamed Ayani, Irham; Bauravindah, Achmad; Siradjuddin, Indah Agustien; Faradisa, Irmalia Suryani
Register: Jurnal Ilmiah Teknologi Sistem Informasi Vol 11 No 1 (2025): January
Publisher : Information Systems - Universitas Pesantren Tinggi Darul Ulum

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26594/register.v11i1.4653

Abstract

Traditional medicine is the practice of utilizing medicinal plants to treat various illnesses, passed down from generation to generation. In Indonesia, there are various traditional medicines, one of which is using green betel leaves. One part of the green betel plant that is commonly attacked by pests is the leaf. The Convolutional Neural Network (CNN) method is a very common method used for image classification because this method produces the highest accuracy in classification and pattern recognition. This research uses data totaling 4000 images which are divided into four classes: healthy green betel leaves, anthracnose green betel leaves, bacterial spot betel leaves, and healthy red betel leaves. Detecting the disease type facilitates farmers in acknowledging the necessary measures required to provide treatment. Therefore, this study utilizes the benefits of the CNN approach, specifically its capability to conduct precise object detection and classification in image data, to minimize the widespread of disease. The CNN architectures implemented are DenseNet201, EfficientNetB3V2, InceptionResNetV2, MobileNetV2 and XceptionResnet50V2. Based on our research, the InceptionResNetV2 model achieved the highest performance with an accuracy of 86.0%, loss of 0.3880, and ROC of 98.0%. In the other hand, the MobileNetV2 and EfficientNetV2B3 models suffered from overfitting and underfitting and the models failed to classify betel leaf diseases.
Deep Learning-Based Inpainting for Reconstructing Severely Damaged Handwritten Javanese Characters Damayanti, Fitri; Yuniarno , Eko Mulyanto; Suprapto , Yoyon Kusnendar
Register: Jurnal Ilmiah Teknologi Sistem Informasi Vol 10 No 2 (2024): July
Publisher : Information Systems - Universitas Pesantren Tinggi Darul Ulum

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26594/register.v10i2.4929

Abstract

Long-term storage at museums can damage ancient Javanese manuscripts; for instance, temperature changes and other factors may cause parts of the script to disappear. The Javanese script exhibits similarities among its letters, making the reconstruction process challenging, particularly when dealing with severe damage to the script's characteristic areas. To address this issue, we conducted a character painting technique that utilizes deep learning architecture, specifically the convolutional autoencoder, partial convolutional neural network, UNet, and ResUNet. The dataset contains 12,000 handwritten Javanese characters. We evaluated the restoration of missing characters using SSIM and PSNR metrics. The ResUNet achieves the best performance compared to other methods, with an SSIM value of 0.9319 and a PSNR value of 18.9507 dB. According to this study, the ResUNet models can reconstruct Javanese manuscripts with strong performance, offering an alternative solution to ensure the preservation and accessibility of these valuable historical documents for future generations.
Improving Urban Heat Island Predictions Using Support Vector Regression and Multi-Sensor Remote Sensing: A Case Study in Malang Arif, Yunifa Miftachul; Rohma, Salma Ainur; Nurhayati, Hani; Kusumadewi, Tarranita; Nugroho, Fresy; Karami, Ahmad Fahmi
Register: Jurnal Ilmiah Teknologi Sistem Informasi Vol 10 No 2 (2024): July
Publisher : Information Systems - Universitas Pesantren Tinggi Darul Ulum

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26594/register.v10i2.5022

Abstract

The Urban Heat Island (UHI) phenomenon is characterized by higher temperatures in urban areas compared to surrounding rural areas. This condition poses various environmental risks and adversely impacts public health, particularly in Malang, Indonesia. This study aims to predict land surface temperature (LST) in Malang to better understand and mitigate the effects of UHI's. Support Vector Regression (SVR) is employed using remote sensing data from Landsat-8, Sentinel-2, and SRTM. Normalized Difference Vegetation Index (NDVI), Normalized Difference Built-up Index (NDBI), Normalized Difference Water Index (NDWI), Normalized Difference Moisture Index (NDMI), elevation, and LST are calculated and normalized to ensure accurate data representation. Model testing results indicate that the Radial Basis Function (RBF) kernel performs best with hyperparameter settings of C = 10, Epsilon = 0.1, and gamma = 1. This model achieves an R² of 0.887, an MSE of 1.625, and a MAPE of 2.71%. These findings confirm that SVR with an appropriately tuned RBF kernel can improve prediction accuracy. Consequently, the study provides a robust foundation for developing more effective predictive models to address UHI management in urban areas.
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 Fuzzy Control System for Performance Optimization in Wireless Sensor Networks Motwakel, Abdelwahed; Almohamedh , Refan Mohamed; Abdalrahman, Hayfaa Tajelsier Ahmed
Register: Jurnal Ilmiah Teknologi Sistem Informasi Vol 11 No 1 (2025): January
Publisher : Information Systems - Universitas Pesantren Tinggi Darul Ulum

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26594/register.v11i1.5431

Abstract

Wireless Sensor Networks (WSNs) play a vital role in numerous domains such as environmental monitoring, healthcare, industrial automation, and smart city infrastructures. Despite their growing significance, WSNs face persistent challenges, including limited energy resources, high data loss, network instability, and latency issues. To address these concerns, this study explores the integration of fuzzy logic to optimize WSN performance under uncertain and dynamic conditions. A fuzzy logic-based control system was designed to adaptively regulate key parameters, such as node energy, packet loss, and connectivity. Simulations were conducted with varying node densities (100, 200, and 300 nodes) to assess the effectiveness of the approach. The results revealed notable improvements: energy consumption was reduced by up to 0.65%, network lifetime extended by up to 0.28%, packet delivery ratio increased by up to 3.10%, and average latency decreased by up to 43.8%. These outcomes underscore the potential of fuzzy logic to enhance the adaptability, efficiency, and reliability of WSNs, offering a practical and scalable solution for performance optimization in real-world deployments.
Machine and Deep Learning for Intrusion Detection: A PRISMA-Guided Systematic Review of Recent Advances Zmaimita, Hicham; Madani, Abdellah; Zine-Dine, Khalid
Register: Jurnal Ilmiah Teknologi Sistem Informasi Vol 11 No 1 (2025): January
Publisher : Information Systems - Universitas Pesantren Tinggi Darul Ulum

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26594/register.v11i1.5589

Abstract

The massive increase in the number and complexity of cyberattacks has surpassed the capabilities of traditional Intrusion Detection Systems (IDS), prompting a shift toward Machine Learning (ML) and Deep Learning (DL) solutions. This systematic literature review critically examines research published between 2020 and 2025 on ML- and DL-based IDSs, focusing on model architectures, benchmark datasets, evaluation metrics, and key performance results. By adapting a rigorous methodology based on PRISMA 2020, 41 high-quality studies were selected and analyzed. The findings reveal a strong preference for DL models, particularly Convolutional Neural Network (CNN), Long Short-Term Memory (LSTM), Gated Recurrent Unit (GRU), Bidirectional Long Short-Term Memory (BiLSTM) and hybrid ensembles, which demonstrate higher detection rates and robustness compared to traditional deep learning methods. However, persistent challenges such as data imbalance, high false positive rates, adversarial vulnerabilities and real-time deployment constraints, continue to hinder widespread adoption.
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.

Page 22 of 22 | Total Record : 219

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