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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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PENDEKATAN EXPLAINABLE MACHINE LEARNING UNTUK ANALISIS FAKTOR DROP OUT MAHASISWA MENGGUNAKAN XGBOOST Agnes Putri Istiwana; Ramadhan Rakhmat Sani; Yuventius Tyas Catur Pramudi
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.v11i1.7218

Abstract

The problem of student dropout is a strategic issue in higher education because it has a direct impact on academic quality, institutional efficiency, and university accreditation. This study aims to statistically analyze the factors that contribute to the variation in GPA of students who have dropped out using the Explainable Machine Learning approach. The predictive model was built using the Extreme Gradient Boosting (XGBoost) algorithm to obtain optimal prediction performance, while the Shapley Additive Explanations (SHAP) method was used to provide interpretation of the contribution of each feature in the model. The research dataset includes academic, administrative, and demographic data of students who have dropped out in the last two academic years. The evaluation results show that the XGBoost model shows excellent predictive performance with an R² value of 0.820 indicating that most of the GPA variation can be explained by the model, and is supported by a Root Mean Squared Error (RMSE) value of 0.344 and a Mean Absolute Error (MAE) of 0.172 indicating that the prediction error rate is relatively low. SHAP analysis revealed that the number of credits taken and tuition payment status were the two factors that statistically significantly contributed to GPA changes in the predictive model. This study provides more comprehensive insights by combining high predictive performance and model interpretability, enabling educational institutions to identify student academic risk earlier and based on data.
APLIKASI PENJUALAN FURNITUR MENGGUNAKAN MARKERLESS AUGMENTED REALITY DENGAN FLUTTER Naufal Nur Faiq; Erik Iman Heri Ujianto
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.v11i1.7224

Abstract

The rapid expansion of e-commerce has transformed consumer purchasing behavior from physical stores to digital platforms. In the furniture category, limited product visualization often leads to discrepancies between customer expectations and the actual products received. This study aims to develop a markerless Augmented Reality (AR)-based furniture sales application using the Flutter framework and ar_flutter_plugin to support in-situ product visualization. The research adopts a prototyping approach consisting of literature review, system requirement analysis, user interface design, application implementation, and evaluation through functional testing and usability assessment. The developed application enables real-time visualization of 3D furniture models without physical markers and provides button-based controls for object manipulation. Experimental results indicate that the system can render 3D objects stably under various lighting conditions and operate smoothly across multiple Android devices. These findings suggest that Flutter-based AR applications have the potential to enhance users’ understanding of furniture products in online shopping environments.  
PERANCANGAN SISTEM AUTENTIKASI MULTI-ROLE BERBASIS RBAC PADA PLATFORM E-LEARNING PEMBERDAYAAN EKONOMI PEREMPUAN Rizal Firmansyah; Aulia Hamdi; Dini Riandini
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.v11i1.7226

Abstract

Digital transformation provides significant opportunities for women’s economic empowerment. The UMI (Usaha Mandiri Ibu) platform was developed as a web-based Learning Management System (LMS) to bridge the gap for housewives by monetizing their domestic skills. However, the presence of multiple user roles (Participants, Mentors, and Super Admins) creates a complex interaction ecosystem. This diversity of roles introduces potential security risks, especially related to unauthorized access or privilege escalation if not managed with a proper access-control mechanism. This study aims to design and implement a multi-role authentication system on the UMI platform using the Role-Based Access Control (RBAC) method. The system development process adopts the Waterfall model, consisting of requirement analysis, system design, implementation using ReactJS and ExpressJS, and testing. Specifically, the built system integrates the RBAC mechanism at the middleware layer to verify user authority in real-time, ensuring strict access separation between administrative and learning features. The testing results demonstrate that the authentication process and role-based authorization restrictions function correctly with a 100% success rate. These findings indicate that the RBAC model is effective in enforcing strict separation of access privileges, enhancing data security and system integrity for the UMI platform.
TIME SERIES FORECASTING SAHAM PT ASTRA MENGGUNAKAN ALGORITMA AUTOREGRESSIVE INTEGRATED MOVING AVERAGE DAN PROPHET Naufal Malik Herlambang; Harun Al Azies
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.v11i1.7231

Abstract

Stock price movements are highly volatile, requiring reliable forecasting models to support investment decision-making. This study compares the performance of time-series models, namely the Autoregressive Integrated Moving Average (ARIMA) and the Prophet, in predicting the closing price of PT Astra International Tbk (ASII.JK). The study employs secondary data obtained from Yahoo Finance covering the period from October 2020 to October 2025. Model evaluation is conducted using an out-of-sample backtesting scheme with RMSE, MAE, MAPE, and directional accuracy (DA) as performance metrics. The results indicate that the ARIMA(2,1,2) model provides superior numerical accuracy, achieving a MAPE of 6.26%, while the Prophet model with a changepoint prior scale of 0.5 yields a MAPE of 7.33%. In terms of price movement direction, Prophet demonstrates a higher DA value of 57.26%. Visual analysis shows that ARIMA predictions closely track actual price movements, with relatively small deviations, whereas Prophet produces increasingly wide uncertainty intervals at longer forecasting horizons. Based on these findings, ARIMA is more suitable for precise price forecasting, while Prophet is better suited for analyzing price direction and trend dynamics.
PERANCANGAN SISTEM HR-WELLBEING: PANDUAN INTERVENSI DIGITAL DAN PENGUKURAN KINERJA TERINTEGRASI KESEJAHTERAAN KARYAWAN Anip Febtriko; Tri Rahayuningsih; Nelia Afriyeni
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.v11i1.7232

Abstract

Digital transformation in the workplace demands that organizations build human resource management systems that integrate employee productivity and well-being. Findings on the High Wellbeing and Performance Work System (HWBPWS) demonstrate that psychological, social, and workplace well-being play a crucial role in driving sustainable performance. This study aims to design an HR-Wellbeing System, a digital model that combines well-being intervention guidance with an integrated individual performance measurement system based on the dimensions of Employee Well-Being (EWB) and Individual Work Performance (IWP). This study employs a Research and Development (R&D) method, encompassing needs analysis, system prototype design, and integration of well-being and performance measurement instruments. Data were collected through literature reviews, instrument readability tests, and expert validation regarding system design and indicator suitability. The results demonstrate that the designed system is capable of providing digital intervention features in the form of psychoeducational modules, self-reflection, burnout and presenteeism indicators, and a performance analytics dashboard connected to employee well-being data. This integration makes it easier for organizations to monitor HR conditions in real-time and carry out more targeted interventions in a humane and data-based manner. In conclusion, the HR-Wellbeing System can be a strategic innovation in modern HR practices, providing a sustainable digital approach to improving employee well-being and performance in various organizational sectors, including the public and private sectors that are adapting to HR 5.0.
PEMANFAATAN AUGMENTED REALITY (AR) UNTUK MENINGKATKAN PARIWISATA BUDAYA SEJARAH BABAD PASIR LUHUR DI DESA WISATA TAMANSARI DEUIS NUR ASTRIDA
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.v11i1.7233

Abstract

Cultural tourism plays a crucial role in preserving the history and local wisdom of a region. One such heritage site is the Babad Pasir Luhur in Tamansari Tourism Village, Banyumas. However, the delivery of historical information to tourists remains limited and conventional. This study aims to develop and test Augmented Reality (AR) technology as an interactive medium for introducing the history of Babad Pasir Luhur. The method used is Research and Development (R&D) with the ADDIE (Analysis, Design, Development, Implementation, Evaluation) model. The research stages include analyzing tourist needs, designing an AR system based on Unity 3D and Vuforia, creating 3D models of historical figures, and testing the application with tourists. The results of the limited trial indicate an increase in interactivity, historical understanding, and cultural tourism appeal through the immersive experience provided by the AR application. Tourists expressed feeling more involved and helped in understanding the historical narrative of Babad Pasir Luhur. In conclusion, AR technology has the potential to be an effective supporting medium for enhancing the cultural tourism experience while supporting the digital preservation of local history.
APLIKASI DETEKSI KOMENTAR JUDI ONLINE PADA PLATFORM YOUTUBE DENGAN MENGGUNAKAN METODE LSTM M Iqbal Husaini; Mansur Mansur
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.v11i1.7234

Abstract

The increasing spread of online gambling promotions in YouTube comment sections poses a serious digital security threat, particularly as perpetrators often disguise words, symbols, and writing patterns to evade automatic moderation systems. This condition highlights the need for an adaptive detection system capable of understanding linguistic variations and contextual patterns. This study aims to design and develop a web-based system for detecting online gambling promotional comments using the Long Short-Term Memory (LSTM) method integrated with the YouTube Data API v3 for real-time comment retrieval. The data processing stages include text cleaning, tokenization, vector transformation using word embeddings, LSTM model training, and performance evaluation. The system is implemented using Laravel as the backend platform, while the deep learning model is developed using Python. The results indicate that the LSTM model is able to classify comments containing online gambling promotions, including those using disguised spelling patterns, based on evaluation using an independent test dataset. The developed system allows users to view detection results and remove flagged comments directly through the dashboard. This research contributes to the development of an integrated and practical web-based content moderation system for digital platforms.  
EVALUASI KINERJA ARSITEKTUR CNN BERBASIS TRANSFER LEARNING XCEPTION DAN MOBILENETV2 UNTUK KLASIFIKASI CITRA LIMBAH Wachid Zufar Ramadhan; Harun Al Azies
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.v11i1.7235

Abstract

Waste management is an increasingly crucial environmental issue, particularly given the growing volume of waste and the lack of an effective sorting system. Reliance on manual sorting is considered inefficient, difficult to scale, and prone to errors, necessitating an automated approach based on visual intelligence. This study analyses the performance of two transfer learning-based Convolutional Neural Network (CNN) architectures, namely Xception and MobileNetV2, for image sorting of organic and inorganic waste. The Garbage Classification dataset, consisting of 15,515 images, was used, with preprocessing stages including normalisation, augmentation, handling class imbalance via class weights, and training using K-Fold Cross Validation and hyperparameter tuning. Validation results show that MobileNetV2 achieves the highest accuracy of 98.03%, but its performance decreases on the test data to 85.50%. In contrast, Xception demonstrates better generalisation with a test accuracy of 92.50%, an AUC of 0.918, and stable precision, recall, and F1-score metrics. A t-test also confirmed a statistically significant difference in the performance of the two models. Xception was deemed more feasible for implementation in an automated waste image sorting system under operational conditions. These results provide a basis for recommendations to developers and stakeholders to strengthen innovative waste management strategies and mitigate environmental impacts.
PEMODELAN TOPIK CUITAN TENTANG DANANTARA MENGGUNAKAN BERTOPIC TEROPTIMASI UMAP DAN HDBSCAN Noor Akhnafal Aban; Chanifah Indah Ratnasari
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.v11i1.7238

Abstract

This study examines public discourse surrounding Danantara by applying BERTopic optimized with UMAP dimensionality reduction and HDBSCAN clustering to model thematic structures within Indonesian-language tweets. The increasing volume of digital conversations and uncertainty surrounding Danantara necessitate analytical approaches that go beyond conventional sentiment classification to capture thematic diversity. The dataset underwent extensive preprocessing, including duplication removal and temporal anomaly detection, to reduce noise and mitigate distortion from non-organic conversational bursts. BERTopic was configured using IndoSBERT-large, a transformer-based embedding model specifically designed for Indonesian-language semantic representation, alongside optimized UMAP–HDBSCAN parameters to ensure stable clustering. The analysis identified fifteen distinct topics spanning economic optimism, energy and state-owned enterprises, concerns over legality and accountability, political narratives, and informal or humorous interactions. The findings demonstrate that public discourse on Danantara is highly heterogeneous and shaped by socio-political dynamics on social media. Overall, the proposed approach proves effective in uncovering layered discourse structures, as reflected in the coherence and diversity of the generated topics, and it provides a data-driven foundation for analyzing public perception and informing policy communication strategies.
IMPLEMENTASI ANTARMUKA SINGLE PAGE APPLICATION PADA E-LEARNING UMI MENGGUNAKAN REACT.JS DAN REST API Ibrahim Ali Abel; Ito Setiawan; Aulia Hamdi
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.v11i1.7245

Abstract

Digital transformation in education demands the availability of e-learning platforms that are not only informative but also responsive and easy to use. This study focuses on the development of the frontend interface of the Usaha Mandiri Ibu (UMI) e-learning platform using a React.js-based Single Page Application (SPA) architecture integrated with a REST API built using Express.js on Node.js. This approach was chosen to address the limitations of conventional Multi-Page Applications (MPA), which are less efficient in page loading, and to meet the needs of the target users, namely housewives, for an intuitive and fast interface. System development was carried out using the Waterfall method, which includes the stages of analysis, design, implementation, and testing. The results show that the implementation of React.js successfully produces a modular interface that separates presentation logic from server-side business logic. Black Box testing on eight main scenarios indicates that all functions operate correctly. In addition, network performance testing using Chrome DevTools shows that page navigation is performed without a full page reload, as indicated by only a single document request (Doc request) during the initial load. These findings demonstrate that the SPA architecture improves navigation efficiency and response time, thereby supporting a more comfortable user experience.

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