cover
Article Per Year (5 Year)
All
Home Page
OAI Link
Editorial Team
Contact
Reviewer
Google Scholar
Contact Name
Salamun
Contact Email
salamun@univrab.ac.id
Phone
-
Journal Mail Official
Jurnal.ti@univrab.com
Editorial Address
Redaktur Jurnal RABIT Teknik Informatika Universitas Abdurrab: Gedung Universitas Abdurrab Pekanbaru Jl. Riau Ujung No. 73 Pekanbaru Riau - Indonesia
Location
Kota pekanbaru,
Riau
INDONESIA
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
 65   66   67   68   69 
ANALISIS KEPUASAN PENGGUNA APLIKASI SIAPEL-TEGAS MENGGUNAKAN END USER COMPUTING SATISFACTION (EUCS) DAN PERCEIVED SERVICE QUALITY Worih Ilhamanto; Ahsanun Khudori; Risqy Pradini
Rabit : Jurnal Teknologi dan Sistem Informasi Univrab Vol 11 No 2 (2026): Juli
Publisher : LPPM Universitas Abdurrab

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36341/rabit.v11i2.8333

Abstract

SIAPEL-TEGAS is a digital population administration service platform managed by the Population and Civil Registration Office (Dispendukcapil) of Malang City. This study aims to measure user satisfaction with SIAPEL-TEGAS using the End User Computing Satisfaction (EUCS) method, supplemented by a Perceived Service Quality construct. A descriptive quantitative approach was applied to 35 administrators managing cooperation agreements between healthcare facilities and Dispendukcapil Malang City, selected through purposive sampling. Data were collected using a 24-item questionnaire on a 5-point Likert scale, tested for validity (r-count > 0.334) and reliability (Cronbach's Alpha > 0.80). The results show that all dimensions fall within the very satisfied category, with the highest index recorded for Content and Format (89.14%), followed by Perceived Service Quality (88.34%), Ease of Use (88.23%), Accuracy (88.14%), and Timeliness as the lowest index (87.62%). These findings indicate that SIAPEL-TEGAS has met users' technical and functional needs, although continuous improvement in timeliness remains necessary to optimize digital population administration services in Malang City.  
RANCANG BANGUN SISTEM E-COMMERCE MENGGUNAKAN ALGORITMA CONTENT BASED FILTERING Purinda Karolin Karolin
Rabit : Jurnal Teknologi dan Sistem Informasi Univrab Vol 11 No 2 (2026): Juli
Publisher : LPPM Universitas Abdurrab

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36341/rabit.v11i2.8334

Abstract

The rapid expansion of technology has profoundly reshaped the commercial sector, shifting conventional trading activities toward web-based e-commerce platforms due to their superior efficiency, practicality, and accessibility. Although numerous digital marketplaces exist, several vendors still lack built-in product customization. A product recommendation system plays a critical role, as the absence of personalized features may impair the quality of the user experience and eventually lead to lower platform engagement. This study aims to build an e-commerce platform integrated with the Content Based Filtering algorithm, which provides tailored item suggestions matching distinct user preferences. The system engineering process follows the structured phases of the Waterfall model, incorporating requirement analysis, architectural design, implementation, software testing, and system maintenance. Finally, the validation framework relies on Blackbox Testing to verify execution accuracy alongside the System Usability Scale (SUS) method to assess the metrics of user satisfaction and interface friendliness.
PERANCANGAN SISTEM PAKAR BERBASIS WEB UNTUK DIAGNOSIS KERUSAKAN LAPTOP MENGGUNAKAN METODE FORWARD CHAINING lulu; lulu julia julia; Dahlan; Syarifuddin
Rabit : Jurnal Teknologi dan Sistem Informasi Univrab Vol 11 No 2 (2026): Juli
Publisher : LPPM Universitas Abdurrab

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36341/rabit.v11i2.8336

Abstract

Laptop hardware failures are often difficult for users to identify due to limited knowledge of their symptoms and types. This condition often forces users to rely on technicians for an initial diagnosis. This study aims to develop a web-based expert system for diagnosing laptop hardware failures using the Forward Chaining method. The system was developed using the Waterfall model within the System Development Life Cycle (SDLC) framework and implemented using the CodeIgniter 4 (CI4) framework with a MySQL database. Knowledge acquisition was conducted through literature review, observation, and interviews with laptop technicians, and the acquired knowledge was represented as IF–THEN rules within the system's knowledge base. System functionality was evaluated using the Black Box Testing method, while diagnostic validation was performed by comparing the system's diagnosis with the diagnosis provided by expert technicians using 21 test cases. The results showed that all system functions operated as designed, and the validation demonstrated agreement between the system's diagnosis and the expert's diagnosis for the tested cases. These findings indicate that the Forward Chaining method can be effectively applied to support the initial diagnosis of laptop hardware failures through a web-based expert system.
ANALISIS EFEKTIVITAS MODEL PRODUKSI FILM ANIMASI CERITA RAKYAT BABAD BATURRADEN BERBASIS ARTIFICIAL INTELLIGENCE SEBAGAI UPAYA DIGITALISASI DAN PELESTARIAN BUDAYA LOKAL DEUIS NUR ASTRIDA
Rabit : Jurnal Teknologi dan Sistem Informasi Univrab Vol 11 No 2 (2026): Juli
Publisher : LPPM Universitas Abdurrab

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36341/rabit.v11i2.8337

Abstract

Folklore represent an important form of intangible cultural heritage containing historical, philosophical, and educational values that contribute to cultural identity formation. However, the rapid advancement of digital technology has reduced younger generations' interest in traditional narrative media. This study aims to analyze the effectiveness of an Artificial Intelligence (AI)-based animation film production model for the folklore Babad Baturraden as an effort to support cultural digitalization and preservation. The study employed an applied research approach using the Research and Development (R&D) method adopting the ADDIE model (Analysis, Design, Development, Implementation, and Evaluation). The production process was conducted from February to April 2026 using ChatGPT for scriptwriting and storyboard development, Google Flow for visual asset creation and animation production, and Suno AI for music generation. The resulting animation film has a duration of 25 minutes and 44 seconds, consisting of 18 scenes, 24 sequences, and 111 shots. The results indicate that the AI-based production model improved production time efficiency by 64.8%, reduced human resource requirements by 70%, and decreased production costs by 80% compared to conventional production methods. Multimedia expert validation achieved an average score of 4.67, cultural validation obtained an average score of 4.73, and user satisfaction evaluation reached an average score of 4.68, all categorized as excellent. Furthermore, the System Usability Scale (SUS) evaluation produced an average score of 85.4, categorized as excellent. The findings demonstrate that the AI-based animation production model is effective as a strategy for digitalization and preservation of local cultural heritage through animation media.
OPTIMASI ARSITEKTUR BI-LSTM DENGAN ATTENTION MECHANISM UNTUK KLASIFIKASI PERUBAHAN PERANGKAT LUNAK PADA ULASAN PERANGKAT BERGERAK Alifia Puspaningrum; Muhamad Mustamiin; Meyer Mega Eklesia Silaban; Esti Mulyani
Rabit : Jurnal Teknologi dan Sistem Informasi Univrab Vol 11 No 2 (2026): Juli
Publisher : LPPM Universitas Abdurrab

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36341/rabit.v11i2.8338

Abstract

Analyzing mobile application user reviews plays a crucial role in software evolution. However, manual processes are often constrained by the large volume of data and language ambiguity. This research develops an automated classification model to categorize reviews into bug reports, feature requests, and non-informative using a Bi-Long Short-Term Memory (LSTM) architecture reinforced with an Attention Mechanism. Experimental results show that the model achieves 94.33% training accuracy and 71.95% testing accuracy, outperforming the standard Bi-LSTM which only reached 87.58% in training accuracy and 70.85% in testing. In terms of efficiency, this model converges faster, reaching peak performance in only 100 epochs, compared to 500 epochs for the standard Bi-LSTM. Furthermore, experiments show that increasing architectural complexity, such as combining Bi-LSTM with Attention, triggers overfitting and weight fluctuations. Thus, the integration of the Attention Mechanism in BiLSTM is proven to provide an optimal balance between computational efficiency and prediction accuracy, effectively supporting decision-making systems for developers.
OPTIMASI KLASIFIKASI CITRA ALFABET SISTEM ISYARAT BAHASA INDONESIA (SIBI) MENGGUNAKAN AUGMENTASI DATA DAN FINE-TUNING MOBILENETV2 Amelia Safrida; Edy Mulyanto; Muhammad Naufal
Rabit : Jurnal Teknologi dan Sistem Informasi Univrab Vol 11 No 2 (2026): Juli
Publisher : LPPM Universitas Abdurrab

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36341/rabit.v11i2.8340

Abstract

The Indonesian Sign Language System (SIBI) is a communication medium used by the deaf community in Indonesia. However, classifying SIBI alphabet images presents challenges due to visual similarities between signs for different letters and limited training data variation. This study aims to optimize SIBI alphabet image classification by applying data augmentation and fine-tuning to the MobileNetV2 model. The dataset consists of 7,582 SIBI alphabet images sourced from Kaggle, covering 24 classes (letters A through Y, excluding J and Z). The research process involved preprocessing, data augmentation, dataset splitting into training, validation, and testing sets, model development using MobileNetV2 transfer learning, and performance evaluation based on accuracy, precision, recall, and F1-score. The results show that the model without augmentation achieved 96.75% accuracy, whereas the model with augmentation achieved 97.10% accuracy, accompanied by improvements in precision, recall, and F1-score. These results indicate that data augmentation enhances the model's generalization capabilities, resulting in more accurate and consistent classification. Thus, the combination of data augmentation and MobileNetV2 fine-tuning is effective for SIBI alphabet image classification. McNemar's test revealed a statistically significant difference in model performance following the application of data augmentation (p = 9.6517 × 10⁻¹²).
ANALISIS PENINGKATAN PERFORMA CONVOLUTIONAL NEURAL NETWORK MENGGUNAKAN HYPERPARAMETER TUNING DAN ENSEMBLE LEARNING PADA KLASIFIKASI CITRA MRI TUMOR OTAK SILA MILDAWATI; Putri Taqwa Prasetyaningrum
Rabit : Jurnal Teknologi dan Sistem Informasi Univrab Vol 11 No 2 (2026): Juli
Publisher : LPPM Universitas Abdurrab

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36341/rabit.v11i2.8341

Abstract

This study aims to improve brain tumor MRI image classification performance through selected hyperparameter combinations and ensemble learning. The publicly available dataset consists of 3,264 training images and 394 testing images categorized into four classes: glioma, meningioma, no tumor, and pituitary tumor. The preprocessing stage includes resizing images to 224 × 224 pixels, normalization, and training-data augmentation. Two pre-trained CNN architectures, VGG16 and EfficientNetV2B0, were fine-tuned using selected combinations of the Adam and SGD optimizers, learning rates of 0.0001 and 0.001, and batch sizes of 32. The best configuration was obtained using Adam, a learning rate of 0.0001, and a batch size of 32. VGG16 achieved an accuracy of 90.00%, while EfficientNetV2B0 reached 98.73%. Combining the prediction probabilities of both models using soft-voting ensemble learning increased the accuracy to 99.49%, with two misclassified images. These results indicate that an appropriate training configuration and soft-voting ensemble learning can numerically improve MRI-based brain tumor classification performance.
COMPARATIVE ANALYSIS OF CONVNEXT V2 AND VISION TRANSFORMER FOR CLASSIFICATION OF STROKE LESIONS ON MRI IMAGING Nursila Latambaga; Putri Taqwa Prasetyaningrum
Rabit : Jurnal Teknologi dan Sistem Informasi Univrab Vol 11 No 2 (2026): Juli
Publisher : LPPM Universitas Abdurrab

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36341/rabit.v11i2.8342

Abstract

Stroke is a major cause of neurological disability, and rapid, accurate diagnostic support remains essential. Because MRI interpretation still depends heavily on radiologist expertise, automated classification models are needed to support Computer-Aided Diagnosis (CAD). This study compares ConvNeXt V2 and Vision Transformer (ViT) for classifying stroke lesions in MRI images, using 7,463 images (4,423 Stroke; 3,040 Normal) integrated from two public Kaggle datasets. The pipeline covered dataset integration, preprocessing, augmentation, transfer learning with full fine-tuning, and evaluation on a held-out 20% test set using accuracy, precision, recall, specificity, F1-score, and AUC-ROC, supported by Grad-CAM and attention-rollout saliency analysis. ConvNeXt V2 achieved 94.98% accuracy, 95.81% precision, 95.71% recall, 93.91% specificity, 95.76% F1-score, and 99.25% AUC, outperforming ViT (92.77% accuracy, 94.30% precision, 93.45% recall, 91.78% specificity, 93.87% F1-score, 98.54% AUC) across all metrics. The largest gap appeared in accuracy and recall, and a paired McNemar test confirmed the difference is statistically significant (χ² = 14.42, p < 0.001). These findings support ConvNeXt V2 as a promising architecture for MRI-based stroke CAD systems, while highlighting the continued need for external clinical validation.  
COMPARATIVE ANALYSIS OF TRANSFER LEARNING PERFORMANCE USING VGG16 AND XCEPTION ARCHITECTURES FOR BREAST CANCER HISTOPATHOLOGICAL IMAGE CLASSIFICATION raden roro christawani herawati; putri taqwa prasetyaningrum
Rabit : Jurnal Teknologi dan Sistem Informasi Univrab Vol 11 No 2 (2026): Juli
Publisher : LPPM Universitas Abdurrab

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36341/rabit.v11i2.8344

Abstract

Breast cancer ranks among the most prevalent malignancies in women globally and demands reliable image-based diagnostic support systems for accurate clinical decision-making. This study compares two transfer learning architectures, VGG16 and Xception, for breast cancer histopathological image classification using the BreakHis dataset, partitioned into training, validation, and testing subsets through stratified splitting. The pipeline included preprocessing, data augmentation, and a two-phase training strategy (freeze then fine-tuning), with performance assessed using eight metrics: accuracy, precision, recall, specificity, F1-score, AUC-ROC, Matthews Correlation Coefficient (MCC), and Cohen Kappa. Testing on 747 images demonstrated that Xception attained an accuracy of 95.72%, F1-score of 95.72%, AUC-ROC of 0.9926, MCC of 0.9144, and Cohen Kappa of 0.9143, compared to VGG16's accuracy of 90.90%, F1-score of 90.90%, AUC-ROC of 0.9686, MCC of 0.8181, and Cohen Kappa of 0.8179. Confusion matrix analysis revealed that Xception produced only 18 false negatives in the malignant class, versus 37 for VGG16, a clinically meaningful reduction. VGG16 required fewer parameters and shorter training duration, whereas Xception delivered superior classification performance. These findings suggest that Xception is the more suitable architecture for breast cancer Computer-Aided Diagnosis (CAD) system development, particularly where diagnostic sensitivity and overall accuracy outweigh computational cost.  
PERBANDINGAN PREDIKSI HARGA SAHAM DENGAN MENGGUNAKAN METODE RECURRENT NEURAL NETWORK DAN LONG SHORT TERM MEMORY Fatia Naura; Safwandi; Rizki Suwanda
Rabit : Jurnal Teknologi dan Sistem Informasi Univrab Vol 11 No 2 (2026): Juli
Publisher : LPPM Universitas Abdurrab

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36341/rabit.v11i2.8345

Abstract

Unpredictable stock price fluctuations encourage the use of artificial intelligence methods based on deep learning. This study compares the performance of Recurrent Neural Network (RNN) and Long Short-Term Memory (LSTM) in predicting the share price of PT Pacific Strategic Financial Tbk. using 346 daily historical data (January 2024–June 2025) sourced from Investing.com. The data collected includes daily stock prices, such as opens, closes, highs, lows, and trading volumes, which will be used to train and test the prediction model. The research stages include pre-processing of data (Min-Max normalization and windowing), model design and training, evaluation using Mean Squared Error (MSE) and Mean Absolute Error (MAE), 5-fold cross-validation, and window size sensitivity analysis. The results showed that RNN was slightly superior to LSTM in prediction accuracy (MSE 0.001417 vs 0.001514; MAE 0.030937 vs 0.031938), inter-fold consistency, and computational efficiency of RNN parameters is only a quarter and memory usage is 1.5 times more efficient than LSTM. In contrast, LSTMs produce predictive patterns that are more visually refined and potentially more suitable for long-term trend analysis. The limited number of data (346 observations) on one issuer is allegedly a factor that limits the theoretical advantages of LSTM gating architecture. These findings imply that RNNs are a more efficient option for short-term stock price predictions with limited data, while LSTMs are more relevant for long-term trend analysis needs on larger volumes of data.  

Page 67 of 70 | Total Record : 696

 65   66   67   68   69 

Filter by Year

2016 2026


Reset
Filter By Issues
All Issue Vol 11 No 2 (2026): Juli Vol 11 No 1 (2026): Januari Vol 10 No 2 (2025): Juli Vol 10 No 1 (2025): Januari Vol 9 No 2 (2024): Juli Vol 9 No 1 (2024): Januari Vol 8 No 2 (2023): Juli Vol 8 No 1 (2023): Januari Vol 7 No 2 (2022): Juli Vol 7 No 1 (2022): Januari Vol 6 No 2 (2021): Juli Vol 6 No 1 (2021): Januari Vol 5 No 2 (2020): Juli Vol 5 No 1 (2020): Januari Vol 4 No 2 (2019): Juli Vol 4 No 1 (2019): Januari Vol 3 No 2 (2018): Juli Vol 3 No 2 (2018): Juli Vol 3 No 1 (2018): Januari Vol 3 No 1 (2018): Januari Vol 2 No 2 (2017): Terbitan Ke-4 Bulan Juli 2017 Vol 2 No 2 (2017): Juli 2017 Vol 2 No 1 (2017): Januari 2017 Vol 1 No 2 (2016): Juli 2016 Vol 1 No 1 (2016): Januari 2016 More Issue