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RABIT: Jurnal Teknologi dan Sistem Informasi Univrab
Published by Universitas Abdurrab
ISSN : 24772062     EISSN : 2502891X     DOI : https://doi.org/10.36341/rabit
Core Subject : Science, Engineering,
Computer Science & IT Engineering
This journal is called RABIT, where the name comes from two words namely, RAB which means Abdurrab University and IT which means information technology, it can be interpreted as a journal of this journal Journal of Informatics Engineering Study Program Pekanbaru Abdurrab University. This RABIT journal contains various sciences related to the world of computers especially information technology and information systems, namely, this journal is published twice a year where the initial publication is on January 10 while for the second issue which is on July 10.
Arjuna Subject : Ilmu Komputer (semua kategori) - Sistem Informasi
Articles 696 Documents
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PERANCANGAN SISTEM PENGADUAN MASYARAKAT (SIPADU) DENGAN RAD UNTUK PENINGKATAN PELAYANAN PUBLIK DI KALANGDOSARI: DESIGN OF A PUBLIC COMPLAINT SYSTEM (SIPADU) WITH RAD TO IMPROVE PUBLIC SERVICES IN KALANGDOSARI Dzanuar wildan; Saifur Rohman Cholil
Rabit : Jurnal Teknologi dan Sistem Informasi Univrab Vol 11 No 1 (2026): Januari
Publisher : LPPM Universitas Abdurrab

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36341/rabit.v11i1.7358

Abstract

Public services at the village level often struggle with issues of efficiency and transparency due to outdated manual complaint systems. In Kalangdosari Village, having a digital platform that enables communication between residents and village officials is crucial for enhancing service quality. This study aims to design and create a web-based Community Complaint System (SIPADU) that can manage reports from the public in an integrated, transparent, and accountable manner. The method chosen for system development is Rapid Application Development (RAD), which allows for the quick and flexible creation of software through iterative cycles. System evaluation is conducted using two methods: Black Box Testing to verify technical functions and the System Usability Scale (SUS) to assess user satisfaction and experience. Test results indicate that all functional features of the SIPADU system operate according to technical specifications without failures. Usability testing revealed that the system is deemed acceptable (Acceptable) with a good rating (Good), indicating that the system's interface is easy for both village residents and administrators to understand and use. The implementation of SIPADU in Kalangdosari Village has proven effective in digitizing the complaint process and provides a RAD-based development framework that can be used in other villages. This research contributes to the utilization of information technology to enhance more responsive and modern village governance.
PERBANDINGAN RESNET50, MOBILENETV2, DAN MOBILENETV3-LARGE DALAM KLASIFIKASI MOTIF BATIK INDONESIA BERBASIS TRANSFER LEARNING: COMPARISON OF RESNET50, MOBILENETV2, AND MOBILENETV3-LARGE IN CLASSIFICATION OF INDONESIAN BATIK MOTIFS BASED ON TRANSFER LEARNING Wahyuda Amurrokhman; Nur Nafi'iyah
Rabit : Jurnal Teknologi dan Sistem Informasi Univrab Vol 11 No 1 (2026): Januari
Publisher : LPPM Universitas Abdurrab

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36341/rabit.v11i1.7372

Abstract

This study is motivated by the importance of automatic batik motif identification to support cultural preservation and the adoption of intelligent technologies in the creative industry. Although numerous previous studies have applied Convolutional Neural Networks (CNNs) for batik classification, most of them are limited to the use of a single architecture or do not provide a comprehensive performance comparison among modern models. As a result, a research gap remains regarding the selection of the most effective architecture for handling the high complexity of batik motifs. The main challenge lies in the wide variation and intricate patterns of batik designs, which pose significant difficulties for convolutional models in achieving accurate classification. Therefore, this study aims to comparatively evaluate the performance of three transfer learning architectures—ResNet50, MobileNetV2, and MobileNetV3-Large—in classifying 20 Indonesian batik motifs. The research methodology includes dataset collection from Kaggle, image preprocessing, data augmentation, model training for 30 epochs, and performance evaluation using precision, recall, and validation loss metrics. The experimental results indicate that MobileNetV3-Large achieves the best performance, with a training accuracy of 95.67%, a validation accuracy of 96.60%, and a validation loss of 10.17%. ResNet50 ranks second with stable performance, while MobileNetV2 exhibits the lowest accuracy due to its limited capability in capturing complex motif details. Consequently, this study contributes to addressing the identified research gap by providing a more comprehensive comparison of transfer learning architectures and demonstrates that modern Neural Architecture Search (NAS)-based models can significantly improve batik motif classification accuracy. The findings further confirm that MobileNetV3-Large is the most suitable architecture for developing efficient and accurate deep learning–based batik motif identification systems.
ANALISIS KEAMANAN APPLICATION LOG WEBSITE BERBASIS DASHBOARD MENGGUNAKAN SECURITY INFORMATION AND EVENT MANAGEMENT Nurulfadhlia; Kasmawi; Pretti Ristra
Rabit : Jurnal Teknologi dan Sistem Informasi Univrab Vol 11 No 1 (2026): Januari
Publisher : LPPM Universitas Abdurrab

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36341/rabit.v11i1.7374

Abstract

The rapid development of websites as platforms for service delivery and information management has made web application security increasingly important, particularly in monitoring user activities and detecting potential security threats. One important data source in website security is the application log, which records authentication activities and system access. However, most previous studies have focused on analyzing infrastructure logs such as server and network logs, while research that specifically analyzes website application logs and presents them through a centralized security dashboard remains limited. Therefore, this study aims to analyze the security of website application logs using a dashboard-based approach with Security Information and Event Management (SIEM). The system development method employed is the Waterfall model, which consists of requirement analysis, design, implementation, testing, and maintenance stages. The SIEM approach is applied to collect, normalize, correlate, and analyze application log data. The results show that the developed system is able to record and visualize user activities through a security dashboard and detect several types of suspicious activities, including repeated failed login attempts (brute force), access outside operational hours, HTTP error bursts, and access to sensitive routes based on simulation testing. The analysis results are presented in the form of real-time visualizations and risk alerts, facilitating administrators in monitoring and auditing website security. Thus, the proposed dashboard-based application log security analysis system using SIEM can enhance the effectiveness of website security monitoring and support early detection of potential security threats at the web application level.
OPTIMASI MODEL U-NET BACKBONE RESNET50 PADA SEGMENTASI CITRA BANJIR MENGGUNAKAN SEQUENTIAL HYPERPARAMETER TUNING Moh Adzka Fawaid; Ricardus Anggi Pramunendar; Nurul Anisa Sri Winarsih; Muhammad Syaifur Rohman; Danny Oka Ratmana
Rabit : Jurnal Teknologi dan Sistem Informasi Univrab Vol 11 No 1 (2026): Januari
Publisher : LPPM Universitas Abdurrab

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36341/rabit.v11i1.7377

Abstract

Flooding is a natural disaster that has become increasingly frequent and causes significant impacts on urban environments, highlighting the need for rapid and accurate mapping of affected areas. Deep learning–based image segmentation, particularly using the U-Net architecture, has been widely applied for this purpose. However, model performance is not determined solely by network architecture, but is also strongly influenced by the selection of training hyperparameters. This study aims to optimize the performance of a U-Net model with a ResNet50 backbone for flood image segmentation using a Sequential Hyperparameter Tuning approach based on a one-factor-at-a-time scheme. The dataset consists of approximately 3,400 RGB flood images with corresponding binary ground truth masks at an original resolution of 512 × 512 pixels, which are resized to 256 × 256 pixels and preprocessed using CLAHE, gamma correction, and unsharp masking to enhance contrast and boundary clarity of inundated areas. The optimization focuses on optimizer selection, batch size, learning rate, and number of training epochs, as these parameters directly affect convergence stability and segmentation accuracy. Hyperparameter tuning is performed sequentially by evaluating model performance on the validation set using Intersection over Union (IoU) and Dice Similarity Coefficient. Based on this process, the optimal configuration employs the AdamW optimizer, a batch size of 8, a learning rate of 0.00015, and 100 training epochs. Final evaluation is conducted on the test set through retraining with three different random seeds, and performance is reported using mean values. The optimized model achieves a mean IoU of 0.7664 and a mean Dice score of 0.8499, with low standard deviation, indicating stable performance and good generalization capability. These findings demonstrate that systematic hyperparameter optimization plays a crucial role in improving the performance of U-Net ResNet50 for flood image segmentation and provides practical insights for remote sensing–based flood mapping systems.
PENGENALAN FIBER OPTIK MENGGUNAKAN AUGMENTED REALITY SEBAGAI PENGENALAN ALAT PADA SISWA SMKN 1 PURWAKARTA Legira Putri Alina Sidik; Dian Permata Sari; Leonardi Paris Hasugian
Rabit : Jurnal Teknologi dan Sistem Informasi Univrab Vol 11 No 1 (2026): Januari
Publisher : LPPM Universitas Abdurrab

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36341/rabit.v11i1.7378

Abstract

Learning about fiber optic materials in vocational high schools still faces several challenges, particularly due to limited practical equipment and the absence of interactive digital learning media that students can access independently. This has implications for students' less-than-optimal understanding of the shapes and functions of fiber optic equipment. This research aims to design and evaluate Augmented Reality-based learning media as an interactive tool to introduce fiber optic practical equipment to students. The method applied is the 4D development model, encompassing the Define, Design, and Modified Develop stages. The Modified Develop stage is an adaptation of the Develop stage, with a primary emphasis on the evaluation and improvement of the learning media design. This approach is carried out through two rounds of usability testing, without proceeding to the Disseminate stage. In the testing, 15 students from the Computer Network and Telecommunications Engineering (TJKT) department participated as research subjects. The evaluation shows significant progress in the media design quality after revisions, reflected in the increase of the UMUX score from 6.7 in the initial testing to 7.0 in the subsequent testing. Additionally, there were improvements in various aspects, including effectiveness, efficiency, learnability, error aspects, and satisfaction. Students successfully completed all tasks with better performance and expressed high satisfaction with the design. Therefore, it can be concluded that the designed Augmented Reality-based learning media has potential as a support for fiber optic learning in vocational high schools and is worthy of further development toward the implementation stage.  
SISTEM PREDIKSI HARGA PANGAN DI KABUPATEN BENGKALIS BERBASIS WEB MENGGUNAKAN METODE SARIMA Azmil Fikri; Nurmi Hidayasari; Zuliar Efendi
Rabit : Jurnal Teknologi dan Sistem Informasi Univrab Vol 11 No 1 (2026): Januari
Publisher : LPPM Universitas Abdurrab

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36341/rabit.v11i1.7384

Abstract

Fluctuations in food commodity prices such as chili, onions, and other staple commodities in Bengkalis Regency often occur unpredictably due to the influence of weather conditions, distribution, supply availability, and increased demand during certain periods, such as religious holidays. The historical daily food price data used in this study indicate the presence of weekly and annual seasonal patterns, making them suitable for modeling using the Seasonal Autoregressive Integrated Moving Average (SARIMA) method. This study aims to develop a web-based food price prediction system that is capable of presenting daily price information as well as price forecasts for the upcoming days. The data used consist of daily records from January 2022 to October 2025, obtained from the Bengkalis Regency Food Security Agency. The data were processed through data cleaning, stationarity testing, model training, and forecasting stages using the Python programming language with the statsmodels library. The system was developed using the Flask framework and a MySQL database. The results show that the SARIMA model is able to generate price forecasts that follow the trends and seasonal patterns of the historical data. Accuracy evaluation using the Root Mean Square Error (RMSE) indicates that the Minyakita commodity has an RMSE of 1,415.04, equivalent to approximately 8–9% of its average price, while commodities with high volatility, such as bird’s eye chili and imported soybeans, exhibit higher prediction errors.  
PENGAMANAN CONTROL AKSES PADA SISTEM PENDUKUNG KEPUTUSAN PENERIMA BANTUAN SOSIAL MENGGUNAKAN RBAC DAN SAW Febi Wulan Adha Febi Wulan Adha; Kasmawi
Rabit : Jurnal Teknologi dan Sistem Informasi Univrab Vol 11 No 1 (2026): Januari
Publisher : LPPM Universitas Abdurrab

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36341/rabit.v11i1.7385

Abstract

The distribution of social assistance at the village level still faces various challenges, such as subjective beneficiary selection processes, lack of transparency, and weak control over system user access rights. These conditions may lead to inaccurate targeting, reduce public trust in social assistance programs, and create opportunities for data manipulation and misuse. This study aims to design and develop a decision support system for the Autonomous BPNT social assistance program in Buruk Bakul Village by implementing Role-Based Access Control (RBAC) and the Simple Additive Weighting (SAW) method. RBAC is applied to regulate user access rights based on roles, namely Village Administrator, Social Assistance Operator, and RT/RW, ensuring that each user can only access features according to their authority. The SAW method is utilized to support the objective assessment of prospective beneficiaries based on three main criteria: low-income households not receiving other assistance, DTKS status, and possession of the Bengkalis Prosperous Card. The evaluation is conducted using criterion weights that have been previously determined by the village authorities. The results indicate that the implementation of RBAC improves data security and the orderly management of social assistance information, while the SAW method produces an objective and measurable ranking process based on predefined criteria and weights. The developed system is expected to enhance targeting accuracy, transparency, and accountability in the distribution of social assistance at the village level.
SEGMENTASI PELANGGAN UNTUK ISP MENGGUNAKAN ALGORITMA K-MEANS: STUDI KASUS PADA DATA CHURN yuli astuti
Rabit : Jurnal Teknologi dan Sistem Informasi Univrab Vol 11 No 1 (2026): Januari
Publisher : LPPM Universitas Abdurrab

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36341/rabit.v11i1.7394

Abstract

Customer segmentation is a crucial strategy for understanding consumer behavior and enhancing customer retention, particularly for companies operating in the Internet Service Provider (ISP) industry. One of the most common challenges faced by ISP companies is customer-related issues, especially the difficulty of retaining subscribers to continue using internet services. This study aims to segment ISP customers based on patterns of digital service usage and monthly billing using the K-Means Clustering algorithm, with a specific focus on a churn data case study. The dataset used in this research was obtained from Kaggle, namely customer_churn_prediction_dataset.csv. Eight variables representing customer behavior were selected for analysis: InternetService, OnlineSecurity, OnlineBackup, DeviceProtection, TechSupport, StreamingTV, StreamingMovies, and MonthlyCharges. The preprocessing stage involved one-hot encoding to transform categorical variables and data normalization using the StandardScaler technique. Cluster evaluation was conducted using the Silhouette Score and the Davies–Bouldin Index to determine the optimal number of clusters. The results indicate that the optimal configuration was achieved with k = 3 clusters, yielding a Silhouette Score of 0.46 and a Davies–Bouldin Index of 0.82. The resulting clusters exhibit distinct characteristics, namely passive customer clusters, low-cost customer clusters, and premium customer clusters. This segmentation provides strategic insights for ISPs in designing more targeted promotional strategies, such as focusing marketing efforts on specific customer clusters, determining retention priorities, and developing more personalized service offerings. The findings demonstrate that the combination of service usage and cost variables serves as an effective parameter for differentiating customer preferences.  
PEMETAAN TREN GLOBAL DALAM PENELITIAN PEMBELAJARAN BERBASIS PERMAINAN DIGITAL: TINJAUAN BIBLIOMETRIK (2001–2024) Umi Rosyidah; Purwanto Purwanto; Putri Taqwa Prasetyaningrum
Rabit : Jurnal Teknologi dan Sistem Informasi Univrab Vol 11 No 1 (2026): Januari
Publisher : LPPM Universitas Abdurrab

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36341/rabit.v11i1.7398

Abstract

The rapid advancement of digital technology has driven the rise of Digital Game-Based Learning (DGBL) as an innovative educational approach. Yet, the field’s research themes, key actors, and future directions remain insufficiently mapped. This study conducts a bibliometric analysis of international publications from 2001 to 2024, using Scopus and IEEE databases, with the number of data analyzed as many as 5,854 scientific articles, to identify leading keywords, authors, institutions, and countries, examine major issues and sources, and trace the evolution of themes and emerging topics. Results indicate that educational games and game-based learning remain central, while frontier technologies such as artificial intelligence and augmented reality have proliferated since 2018. Research trends cluster into emerging (AI, AR, cybersecurity, critical thinking), established (GBL, e-learning, curricula, cognitive training), and declining (CAI, aggression, action video games) topics. Network analysis shows that the United States dominates at around 30%, Spain at 17%, and the United Kingdom at 14%. In practice, they offer opportunities for innovative instructional design that leverage AI for adaptive systems and AR for immersive educational experiences. In addition, the opportunity to develop new conceptual models that explain how games can function as adaptive learning ecosystems, combining pedagogical theory, cognitive psychology, and digital technology, is very open.  
IMPLEMENTASI METODE FP-GROWTH DALAM ANALISA POLA PEMBELIAN PELANGGAN PADA SWALAYAN PANTES PATI BERBASIS WEB: IMPLEMENTATION OF THE FP-GROWTH METHOD IN WEB-BASED ANALYSIS OF CUSTOMER PURCHASE PATTERNS AT PANTES PATI SUPERMARKET Salum Ainayya Alfatikhah; Supriyono Supriyono; Soni Adiyono
Rabit : Jurnal Teknologi dan Sistem Informasi Univrab Vol 11 No 1 (2026): Januari
Publisher : LPPM Universitas Abdurrab

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36341/rabit.v11i1.7401

Abstract

Swalayan Pantes, located in Pati Regency, is a retail store that provides daily necessities. However, its current data processing still relies on manual management through Microsoft Excel, which is less efficient for conducting sales analysis. This study aims to develop a web-based purchasing pattern analysis system by employing the Frequent Pattern Growth (FP-Growth) method. The methodological stages include determining support values, constructing the FP-Tree, generating the conditional pattern base, building the conditional FP-Tree, identifying frequent itemsets, and calculating confidence values. Based on 1,725 transaction records collected between January 2025 and July 2025, the study identified 13 items most frequently purchased together, with a confidence threshold of 60% and a minimum support value of 5. The FP-Growth method was subsequently implemented into a web-based system, complemented with additional features such as “Pindah” (Move) and “Promo” (Promotion), to facilitate decision-making and follow-up actions derived from the analysis results.

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