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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.
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Articles 7 Documents
 1 
Search results for , issue "Vol 11 No 1 (2025): January" : 7 Documents clear
APRS and SSTV Technology for Audiovisual Data Transmission in Internet Blank Spot Areas to Increase the Effectiveness of SAR Activities Christanto, Febrian Wahyu; Handayani, Sri; Handayani, Titis; Dewi, Christine
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.v11i1.3205

Abstract

Volcanic eruptions can be detected through several warning signs. The Indonesian National Disaster Management Agency (BNPB) reported that between 2010 and 2021, Indonesia experienced 156 volcanic eruptions. The most recent occurred in 2021 when Mount Semeru erupted, forcing 10,395 people to evacuate, injuring 104, and causing 51 fatalities. The BNPB often experiences problems in carrying out mitigation, evacuation, rehabilitation, and reconstruction in disaster areas. On average, the search and evacuation process for victims takes about 3-7 days, so the probability of finding disaster victims is only about 50%. The proposed solution is a combination of radio transmission with Auto Packet Reporting System (APRS) technology as a medium for determining evacuation locations and Slow-Scan Television (SSTV) as a medium for transmitting audio and images of disaster sites, called Radio All-in-One (RAIONE). Using the Prototype method, this research has been tested for about 7 months with continuous improvements. The results show that the maximum distance covered is approximately 20 km with a minimum central antenna height of 7-10 meters, which increases the time effectiveness of SAR operations. The probability of finding survivors in a disaster increases to 75%, and SAR operations speed up to 1-2 days because of acceleration in the determination of search and evacuation locations in the Blank Spot Areas, reaching 91.30%.
Robust Classification of Beef and Pork Images Using EfficientNet B0 Feature Extraction and Ensemble Learning with Visual Interpretation Taufiq Akbar, Ahmad; Saifullah, Shoffan; Prapcoyo, Hari; Yuwono, Bambang; Rustamaji, Heru Cahya
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.v11i1.4045

Abstract

Distinguishing between beef and pork based on image appearance is a critical task in food authentication, but it remains challenging due to visual similarities in color and texture, especially under varying lighting and capture conditions. To address these challenges, we propose a robust classification framework that utilizes EfficientNet B0 as a deep feature extractor, combined with an ensemble of Regularized Linear Discriminant Analysis (RLDA), Support Vector Machine (SVM), and Random Forest (RF) classifiers using soft voting to enhance generalization performance. To improve interpretability, we incorporate Gradient-weighted Class Activation Mapping (Grad-CAM) to visualize classification decisions and validate that the model focuses on relevant regions of the meat, such as red-channel intensity and muscle structure. The proposed method was evaluated on a public dataset containing 400 images evenly split between beef and pork. It achieved a hold-out accuracy of 99.0% and a ROC-AUC of 0.995, outperforming individual learners and demonstrating strong resilience to limited data and variation in imaging conditions. By integrating efficient transfer learning, ensemble decision-making, and visual interpretability, this framework provides a powerful and transparent solution for binary meat classification. Future work will focus on fine-tuning the CNN backbone, applying GAN-based augmentation, and extending the approach to multiclass meat authentication tasks.
Enhancing Bank Financial Performance Assessment: A Literature Review of Deep Learning Applications Using the Kitchenham Method Ali, Mahrus; Gernowo, Rahmat; Warsito, Budi; Muthmainah, Faliha
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.v11i1.4224

Abstract

The assessment of bank financial performance is crucial for ensuring the stability of the banking sector. With advancements in technology, especially deep learning (DL), there is increasing potential to improve the accuracy of risk prediction and financial performance evaluation in banks. However, challenges related to data imbalance and model complexity require more efficient approaches. This study aims to examine the application of DL in assessing bank financial performance, with a focus on credit risk, fraud detection, and bankruptcy prediction. A Systematic Literature Review (SLR) was conducted using the Kitchenham approach, analyzing 697 relevant articles to address nine research questions regarding the implementation of DL in the banking sector. This study contributes by providing insights into effective DL models that enhance financial performance and risk prediction in banks, while also offering recommendations for the development of more transparent models. The results indicate that models such as Long Short-Term Memory (LSTM) and Convolutional Neural Networks (CNN) perform well in handling large financial data. Additionally, hybrid models that combine DL with traditional models demonstrate higher accuracy in bankruptcy prediction and fraud detection.
One-Way Communication System using CNN for Interaction between Deaf and Blind People Sulaksono, Juli; Ayu Dwi Giriantari , Ida; Sudarma, Made; Swarmardika , Ida Bagus Alit Swarmardika
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.v11i1.4556

Abstract

Communication is essential for everyone, including for individuals who are deaf and blind. People with disabilities must have equal rights to communicate, just like the general public. A one-way communication system between deaf and blind people is therefore necessary. The input to the system is in the form of spoken language, and the output is in the form of Braille. The input uses SIBI (Indonesian Sign Language System), which is recorded with a camera and then processed using a Convolutional Neural Network (CNN). The CNN is divided into three parts: the Training Process using a Tecnable machine, the SIBI DataSet model, and the Detection Process. The output of this process is text. The conversion of text into Braille is conducted using an image index. The resulting Braille can be read by blind users. System performance is analysed using a Confusion Matrix. The analysis results show an accuracy of 85%, a precision of 90%, and a recall of 82%.
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.
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.

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