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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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EVALUASI LAYANAN KINERJA DOSEN BERBASIS SERVQUAL, CSI DAN IPA PADA POLITEKNIK NEGERI SEMARANG Wakhid Rokhayadi; Aji Supriyanto
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.v11i1.6969

Abstract

This research aims to evaluate the service quality of lecturer performance in the Electronics Study Program at Semarang State Polytechnic using the SERVQUAL approach, Customer Satisfaction Index (CSI), and Importance-Performance Analysis (IPA). A total of 8,131 student responses covering 124 courses and 26 lecturers were analyzed based on the five SERVQUAL dimensions: Tangible, Reliability, Responsiveness, Assurance, and Empathy. The analysis results indicate that all dimensions have negative gap scores between student perceptions and expectations, meaning the service level received is still below expectations. The Tangible dimension recorded the largest gap (-0.99), followed by Reliability and Responsiveness, which also showed relatively low scores (-0.96). A CSI value of 80.8% indicates a high level of student satisfaction. However, the IPA mapping reveals that several indicators still require attention and priority improvement, particularly concerning physical facilities and the reliability of the learning process. These findings contribute to the development of performance-based academic service improvement strategies through an integrated quantitative evaluation model. The key strategic implication of this paradoxical finding is that, although students express satisfaction (high CSI), the existence of negative gaps and priority areas in the IPA indicates that focused service quality improvements are still necessary to transform this basic satisfaction into deeper loyalty and more sustainable academic excellence.
INTEGRASI AGILE DAN DESIGN THINKING UNTUK PENINGKATAN EFEKTIVITAS PENGEMBANGAN APLIKASI PENCEGAHAN STUNTING: INTEGRATION OF AGILE AND DESIGN THINKING TO IMPROVE THE EFFECTIVENESS OF STUNTING PREVENTION APPLICATION DEVELOPMENT Syafira Putri Yuanita; Erba Lutfina; Galuh Wilujeng Saraswati; Resha Meiranadi Caturkusuma
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.v11i1.6978

Abstract

The prevalence of stunting in Ngasem District, Kediri, poses a significant challenge, with one of the inhibiting factors being the conventional and unengaging educational methods for health cadres. This condition results in low material mastery by cadres and minimal community participation. To address these issues, this study aims to design and implement the SIGAP (Sistem Informasi Gizi Anak dan Pencegahan Stunting / Child Nutrition and Stunting Prevention Information System) mobile application by integrating Agile and Design Thinking methodologies. This approach incorporates the Empathize, Define, and Ideate stages of Design Thinking into the Agile framework to ensure a profound understanding of user needs. The application was developed using Flutter, and its Usability was tested on 25 health cadres in Ngasem District as the primary respondents using the System Usability Scale (SUS) method. The test results showed that the SIGAP application obtained an average SUS score of 90.0. This score classifies the application's Usability into the "Excellent" (A) category, proving that the integration of both methods successfully created an educational platform that is highly usable, interactive, and effective. Broadly, this study contributes as a reference model for the digitalization of public health services and enriches the literature regarding the effectiveness of the hybrid Agile-Design Thinking approach in social technology development.
PERBANDINGAN METODE CLUSTERING K-MEANS++, DBSCAN, DAN AGGLOMERATIVE PADA DATA KUESIONER DASS-21 Nur Nafiiyah
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.v11i1.6980

Abstract

This study aims to compare the performance of three clustering methods k-Means++, DBSCAN (Density-Based Spatial Clustering of Applications with Noise), and Agglomerative Clustering in grouping data obtained from the Depression Anxiety Stress Scale (DASS-21) questionnaire. The questionnaire data represent three primary psychological dimensions: depression, anxiety, and stress. Performance evaluation was conducted by calculating the degree of agreement between the clustering results and the actual classes determined based on DASS-21 scores. The analysis results show that k-Means++ achieved the highest agreement rate of 35.63%, followed by Agglomerative Clustering at 34.39% and DBSCAN at 26.81%. These findings indicate that k-Means++ is more effective in forming clusters that align with the psychological dimensions present in the DASS-21 data. However, the relatively low agreement rates suggest overlapping emotional dimensions and the inherent complexity of psychological data. Therefore, further research is required through parameter optimization, data preprocessing, or the application of hybrid approaches to improve the quality and representativeness of the clustering results.  
ANALISIS SENTIMEN MASYARAKAT PADA KOMENTAR INSTAGRAM TERHADAP PROGRAM PEMERINTAH KOTA PALEMBANG DALAM PENCAPAIAN SDG 6 MENGGUNAKAN ALGORITMA NAÏVE BAYES Keisha Angelina Tompunu; Ari Wedhasmara
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.v11i1.6992

Abstract

Sustainable development is the main focus of local governments in their efforts to improve community welfare, particularly through the achievement of Sustainable Development Goals (SDGs). One of these goals, SDG 6 (Clean Water and Sanitation), emphasizes the importance of access to clean water and proper sanitation for all. This study aims to analyze public sentiment towards the Palembang City Government's program in achieving SDG 6 based on comments on the Instagram platform. This study uses a quantitative approach with text mining and Natural Language Processing (NLP) techniques. The dataset used consists of 5,881 Instagram comments collected through crawling and scraping processes. The research stages include data pre-processing (cleaning, case folding, normalization, tokenizing, stopword removal, and stemming), sentiment labeling (positive, negative, and neutral), and classification using the Multinomial Naïve Bayes algorithm. The test results showed an accuracy rate of 80%, with the highest precision value in the negative class at 0.89 and the highest recall value in the neutral class at 0.97. Sentiment distribution shows that the majority of comments are neutral, reflecting the public's informative perception of issues related to clean water, sanitation, and flooding. The dominance of negative sentiment reflects continued public dissatisfaction with clean water services and drainage infrastructure, while positive comments show appreciation for improvements in government services. The results of this study confirm that social media sentiment analysis can be used as an evaluative tool to measure public perception and monitor progress toward achieving SDG 6 at the local level.
EVALUATION OF MACHINE LEARNING AND DEEP LEARNING ALGORITHMS WITH FEATURE SCALING AND K-FOLD CROSS-VALIDATION FOR DIABETES CLASSIFICATION ANGGA KURNIAWAN; MAWARDI KUDIN; ABDUL SALAM AT-TAQWA
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.v11i1.7005

Abstract

Diabetes is a growing global health challenge that requires advanced approaches for early detection and prevention. Previous research has often been limited to evaluation using a single data split, which can potentially yield unreliable model performance estimates. This study addresses that limitation by conducting a comprehensive and rigorous evaluation of eight machine learning algorithms—including classical models, ensemble methods, and Multi-Layer Perceptron (MLP)—using the Pima Indians Diabetes dataset. The applied methodology includes data preprocessing, systematic hyperparameter optimization, and, most importantly, robust performance validation through Multi K-Fold Cross-Validation (K=5,10,15,20). Initial results showed perfect accuracy (100%) for the K-Nearest Neighbors (KNN) model; however, this finding was proven to be an artifact of a fortunate data split (lucky split) after undergoing cross-validation procedures. The more reliable validation results instead revealed the exceptional superiority of the Multi-Layer Perceptron (MLP) model, which achieved 94.96% accuracy with high stability (standard deviation 0.0356) in 20-fold cross-validation. Meanwhile, classical models such as Logistic Regression and Support Vector Machine (SVM) demonstrated high robustness and consistency. These findings significantly contribute to the field of health informatics by emphasizing the importance of rigorous validation methodology and identifying MLP as a highly strong predictive model candidate for diabetes detection. For practical application, this study recommends MLP along with stable classical models as the foundation for developing reliable clinical decision support systems, with the note that further external validation testing is necessary.
OPTIMALISASI STRATEGI PROMOSI PROGRAM STUDI MELALUI DASHBOARD BERBASIS DATA PEMINATAN DAN ANALITIK INSTAGRAM DENGAN ICFS: OPTIMIZING STUDY PROGRAM PROMOTION STRATEGIES THROUGH INTEREST DATA-BASED DASHBOARDS AND INSTAGRAM ANALYTICS WITH ICFS Sri Mulyani; Yunus Anis; Sunardi; Vici Tiara Anjarsari; Hersatoto Listiyono
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.v11i1.7021

Abstract

This research was motivated by the need for new Study Programs (Prodi) to make fast, measurable, and data-driven promotional decisions on the main channel for prospective students, namely Instagram, while content evaluation has been sporadic and metrics are scattered. The goal is to design a framework and prototype of an Excel dashboard that integrates Instagram analytics with a focus on Reels and Carousel formats and student interest data through the ICFS (Interest Content Fit Score) metric to support editorial and promotional decisions. The method used is R&D and prototyping with a mixed methods design: a dataset of 192 TRMG IG content published between November 16, 2022, and November 4, 2025, as well as student interest survey data involving 14 active TRMG Study Program students. Instagram content data is processed into Engagement indicators (likes, comments, saves, shares), ERR (Engagement/Reach), ERI (Engagement/Impressions), ERV (Engagement/Views), paired with interest/preference surveys, the results are realized in an Excel dashboard containing KPI cards, a content leaderboard, a time/content type slicer, and an automatic ICFS calculation. The main results show an operational and low-cost prototype, empirically Reels consistently drives reach and engagement, while Carousel strengthens referral behavior (save/share). The dashboard facilitates weekly content meetings and guides a targeted composition (60% Reels, 25% Carousel, 15% Image). The implication is that this solution shortens the data-to-decision distance, standardizes the evaluation of Study Program promotional content, and provides a conceptual contribution through the formulation of ICFS and a format-based engagement measurement framework. At the same time, its practical contribution is an Excel asset that is ready to be replicated in similar vocational study programs and becomes the basis for migration to an automated web dashboard.
ANALISIS KOMPARATIF MODEL YOLO DAN PENGARUH OVERSAMPLING ON-THE-FLY TERHADAP KINERJA DETEKSI KERUSAKAN JALAN: COMPARATIVE ANALYSIS OF YOLO MODELS AND THE INFLUENCE OF ON-THE-FLY OVERSAMPLING ON ROAD DAMAGE DETECTION PERFORMANCE Sherina Nur Anggraeni; Muljono
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.v11i1.7023

Abstract

Automated road damage detection using computer vision technology remains an active research challenge, particularly do to issues of imbalanced datasets and variations in road surface conditions. While deep learning (YOLO) methods have been widely applied, a comprehensive performance comparison of the latest architectures and the impact of data balancing strategies require further analysis. This study aims to compare the performance of the latest YOLO generations (YOLOv8, YOLOv9, YOLOv10, and YOLO11) and conduct an ablation study to determine the influence of on-the-fly oversampling. The research began by collecting road damage image data (pothole, crack, and manhole) from the public source: "potholes, cracks and openmanholes (Road Hazards)" on Kaggle. Following the data cleaning process, experiments were conducted using 1845 images. The dataset split ratio for train:valid:test sets was 70:15:15. The dataset was then trained using the four YOLO architectures across two case studies: an imbalanced dataset and a balanced dataset achieved through oversampling on-the-fly of the minority classes. Model performance was evaluated using Precision, Recall, F1-Score, mAP50, and mAP50-95 metrics. The results show no significant indications of overfitting and confirm the models' strong generalization capabilities. The application of on-the-fly oversampling quantitatively improved the performance of all models, with the highest mAP50-95 increase observed in YOLO11m (+0.055). This strategy successfully improved the detection of minority classes (crack and manhole) without degrading performance on the majority class (pothole). In the balanced dataset scenario, YOLO11m (mAP50-95: 0.468) and YOLOv8m (mAP50-95: 0.467) demonstrated the best overall performance. A unique finding was shown by YOLOv9m, which achieved the highest Precision value of 0.914.
STUDI KINERJA ALGORITMA LOAD BALANCING STATIS PADA INFRASTRUKTUR HOMOGEN TERHADAP APLIKASI WEB MICROSERVICES Alysha Namora Putri Harahap; Galura Muhammad Suranegara
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.v11i1.7029

Abstract

Technological developments have led to the emergence of cloud computing as a solution to improve system reliability and scalability. One of its components, load balancer, plays an important role in distributing the workload to keep the system optimal, so choosing a load balancing algorithm is a crucial step. This study tested several static load balancing algorithms in a homogeneous server environment based on microservices architecture on two cloud platforms, namely AWS and GCP. With the current experimental design, the tests showed that no algorithm consistently excelled in all scenarios, while AWS tended to display more stable performance than GCP. These results can serve as a basis for further research exploring variations in configuration, load scenarios, or larger system scales to evaluate algorithm and platform performance more comprehensively.
TRANSFORMASI DIGITAL PROSES PENDAFTARAN MELALUI SISTEM INFORMASI BERBASIS WEB DENGAN UI/UX MODEL delta khairunnisa; surahmat surahmat; arif rahman; arabiatul adawiyah; rahul sabilillah; panisah panisah; zalsabila herawaty
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.v11i1.7039

Abstract

Digital transformation in the world of education is one of the strategic steps to support the improvement of the quality of public services. Currently, Al-Asri Islamic Elementary School in Palembang still uses a manual method in the new student registration process, relying on paper forms, and prospective students must meet directly with the school. This manual method can cause various problems, including long queues to obtain paper forms, inefficiency in terms of time, requiring many staff to provide services, and being prone to data errors. This study aims to develop a web-based registration information system with a User Interface (UI)/User Experience (UX) approach. The method used in this study is a prototype approach, through the stages of user needs analysis, information system design, implementation, testing, and evaluation of the information system with users. The results of black box testing were 100 percent and usability testing obtained a score of 84.3 percent, with a very feasible category. This study succeeded in designing a web-based new student registration system that is able to increase the effectiveness, efficiency, and transparency of the student admission process.
SMART ATTENDANCE SYSTEM BERBASIS WEB REAL-TIME MENGGUNAKAN FACENET Anggi Saputri; Ida Nurhaida; Revaldo Ilfestra Metzi Zen
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.v11i1.7041

Abstract

Manual attendance recording often leads to inefficiency and potential data manipulation. To address these issues, this study develops a web-based attendance system with real-time facial recognition using FaceNet. The model is designed by utilizing Dlib for face detection, FaceNet for facial feature embedding generation, and Support Vector Machine for identity classification. The system is implemented using Flask as the backend, Bootstrap for a responsive user interface, and SQLite as a lightweight database. The research dataset consists of 50 individuals and is used to compare the performance of three feature extraction models, namely FaceNet, MobileNet, and VGG-16. The evaluation results indicate that FaceNet achieves the best performance, with a training accuracy of 99.92%, a testing accuracy of 99.82%, an F1-score of 0.9983, and a training time of 9.74 seconds. MobileNet also demonstrates strong performance, achieving a training accuracy of 99.88%, a testing accuracy of 99.73%, and an F1-score of 0.9973. Meanwhile, VGG-16 shows relatively lower performance, with a training accuracy of 99.71%, a testing accuracy of 99.51%, an F1-score of 0.9951, and a training time of 24.61 seconds. These findings indicate that FaceNet is more effective and efficient in extracting facial features. Therefore, the developed system has the potential to replace conventional attendance methods that are prone to errors while supporting the implementation of a smart campus.

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