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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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Beyond User Decline: Investigating the Effects of Social Presence and the Dual Role of Social Connectedness in Sustaining Engagement on Social Networking Platforms Vidayana, Vidayana; Burhanudin, Burhanudin; Lokaadingroho, Indrabudhi; Mansuan, Melki Sadekh
Register: Jurnal Ilmiah Teknologi Sistem Informasi Vol 10 No 1 (2024): January
Publisher : Information Systems - Universitas Pesantren Tinggi Darul Ulum

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26594/register.v10i1.3636

Abstract

Social media platforms are currently grappling with unforeseen difficulties that have hindered their expansion. Previous studies have disclosed that Facebook, one of the most prominent social media sites, has encountered a reduction in its user base. Similarly, other social media platforms have also reported a substantial decline in user numbers and engagement levels, indicating a significant downturn in their popularity. The primary objective of this research is to gain a more comprehensive understanding of the role played by the social presence in human-computer-mediated interactions, specifically in the context of social media. Data from 473 college students from their second to tenth semesters were collected through an online survey. Amos 25 was utilized to test the constructs of the variables of social presence, social connectedness, attitude, and intention to continue using social media. The Hayes Macro Process version 3.5 was used to test the proposed model. The results showed that social connectedness (Con) moderates the effect of social presence and attitude on user intention to continue using social networks. This result extends the body of research on human-computer-mediated interactions by uncovering the role of connectedness in human-computer-mediated interactions and technology adoption.
Comparison of Convolutional Neural Network Methods for the Classification of Maize Plant Diseases Abas, Mohamad Ilyas; Syafruddin Syarif; Ingrid Nurtanio; Zulkifli Tahir
Register: Jurnal Ilmiah Teknologi Sistem Informasi Vol 10 No 1 (2024): January
Publisher : Information Systems - Universitas Pesantren Tinggi Darul Ulum

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26594/register.v10i1.3656

Abstract

The focus of this study is the classification of maize images with common rust, gray leaf spot, blight, and healthy diseases. Various models, including ResNet50, ResNet101, Xception, VGG16, and ENet, were tested for this purpose. The dataset used for corn plant diseases is publicly available, and the data were split into separate sets for training, validation, and testing. After processing the data, the following models were identified: the Xception model epoch with an accuracy of 83.74%, the ResNet model with an accuracy of 97.19% at epoch 8/10, the ResNet101 model with an accuracy of 97.55% at epoch 10/10, and the ENet model with an accuracy of 98.69% at epoch 9/1000. ENet exhibited the highest accuracy among the five models at 98.69%. Additionally, ENet achieved an average accuracy of 95.45%, the highest among all tested models, based on the average accuracy in the confusion matrix. This research indicates that ENet performs best at processing data related to maize plant diseases. Consequently, the analysis of maize plant diseases is expected to evolve as a result of this research. Following the implementation of the system's generated model, this research will continue to explore its impact. The intention is to provide a summary of the comparative classification performance of CNN algorithms.
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%.
Utilization of the Particle Swam Optimization Algorithm in Game Dota 2 Armanto, Hendrawan; Rosyid, Harits Ar; Muladi, Muladi; Gunawan, Gunawan
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.v10i2.3503

Abstract

Dota 2, a Multiplayer Online Battle Arena game, is widely popular among gamers, with many attempting to create efficient artificial intelligence that can play like a human. However, current AI technology still falls short in some areas, despite some AI models being able to play decently. To address this issue, researchers continue to explore ways to enhance AI performance in Dota 2. This study focuses on the process of developing artificial intelligence code in Dota 2 and integrating the particle swarm optimization algorithm into Dota 2 Team's Desire. Although particle swarm optimization is an old evolutionary algorithm, it is still considered effective in achieving optimal solutions. The study found that PSO significantly improved the AI Team's Desire and enabled it to win against Default AI of similar levels or players with low MMR. However, it was still unable to defeat opponents with higher AI levels. Furthermore, this study is expected to assist other researchers in developing artificial intelligence in Dota 2, as the complexity of the development process lies not only in AI but also in language, structure, and communication between files.
Principal Component Analysis on Convolutional Neural Network Using Transfer Learning Method for Image Classification of Cifar-10 Dataset Al Haris, M.; Dzeaulfath, Muhammad; Wasono, Rochdi
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.v10i2.3517

Abstract

The current era was defined by an overwhelming abundance of information, including multimedia data such as audio, images, and videos. However, with such an enormous amount of image data available, accurately and efficiently selecting the necessary images poses a significant challenge. To address this, image classification has emerged as a viable solution for organizing and managing large volumes of image data, thereby mitigating the issue of cluttered image datasets. One of the most popular algorithms for image classification is the Convolutional Neural Network (CNN), which reduces the complexity of network structure and parameters by leveraging local receptive fields, weight sharing, and pooling operations. CNN is a type of artificial neural network specifically designed to process grid-like data, such as images, using convolutional layers to automatically detect local features. Nonetheless, CNN faces several challenges, such as gradient diffusion, large dataset requirements, and slow training processes. To overcome these issues, Transfer Learning has been widely adopted in CNN-based image classification, and Principal Component Analysis (PCA) has been employed to accelerate the training process. PCA is a technique used to reduce data dimensionality by identifying the principal components that account for most of the variance in the data. This study tested the efficacy of PCA-based CNN architecture using the Transfer Learning method on the Cifar-10 dataset. The results demonstrated that the PCA-based CNN architecture achieved the highest accuracy, with a testing accuracy rate of 0.8982 (89%).
Customer Churn Prediction Using the RFM Approach and Extreme Gradient Boosting for Company Strategy Recommendation Irawan, Mohammad Isa; Putris , Nadhifa Afrinia Dwi; Muhammad, Noryanti binti
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.v10i2.4004

Abstract

Customers are vital assets in the growth and sustainability of business  organizations. However, customers may discontinue their engagement with a company and switch to competitors’ products or services for various reasons. This event referred to as customer churn. Losing customers significantly impacts a company's revenue, often resulting in financial decline. Churn events, which are subject to dynamic monthly changes, are further influenced by intense competition and rapid technological advancements. Analyzing customer characteristics is crucial to understanding customer behavior, with metrics such as recency, frequency, monetary (RFM) serving as key indicators of subscription and transaction patterns. The Extreme Gradient Boosting method is applied to address the challenge of classifying churn and non-churn customers. The prescriptive analytics process is carried out to identify the features most influential in prediction outcomes, enabling the formulation of strategic recommendations to mitigate churn problems. The integration of RFM analysis with the XGBoost method provides optimal results, particularly in the third segmentation, achieving an accuracy of = 0.98833, precession = 0.98768, recall = 0.98899, and f1-score = 0.98833. The prescriptive analytics process highlights three critical features, namely city factor, GMV generation, and total customer transaction generation. This findings demonstrate that the segmentation characteristics, data representation, and behavioral approach with RFM analysis have an effect on improving the performance of the model in churn prediction.
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.
A VIKOR-Based Decision Support System for Prioritizing Public Facility Improvements in Malang City with Geotagging Integration Hariyadi, Mokhamad Amin; Fadila, Juniardi Nur; Harini, Sri; Saputra, Muhammad Andryan Wahyu
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.v10i2.4237

Abstract

Public facilities play a crucial role in driving economic growth and development. Nevertheless, the dearth of public information concerning facility enhancements fosters a sense of public distrust towards the government. Additionally, numerous facilities, which should be prioritized for improvement, have not received adequate attention. In contrast to several prior studies, the present study encompasses a broader scope and incorporates geotagging techniques to precisely identify the location of complaints and determine the optimal route to reach them. Moreover, an analysis process utilizing the VIKOR method has been devised to assess the priority of public facility improvements. This method yielded an accuracy rate of 89,7%, signifying a commendable level of precision and a 16% increase in accuracy based on confusion matrix method compared to previous studies. Through user usability testing, it was determined that the majority of users agreed that this system can facilitate public reporting, enable progress monitoring of public facility improvements, and aid in prioritizing such improvements.
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%.

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