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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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ANALISIS KOMPARASI QOS DAN QOE PROTOKOL VPN PADA JARINGAN REDISTRIBUSI RUTE OSPF EIGRP Ariel Ramadhan Diva Aretha Putra Retnawan; ABDUS SALAM
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.v11i2.8018

Abstract

The integration of OSPF and EIGRP dynamic routing through route redistribution aims to connect different domains, but it has the potential to cause routing loops and increased latency, thus requiring a Virtual Private Network (VPN) to protect data privacy and integrity. This research contributes as a study that systematically evaluates the QoS and QoE of three VPN protocols in an OSPF-EIGRP route redistribution environment, to fill a gap in the literature that was previously limited to static networks. Using the GNS3 simulator, PPTP, OpenVPN, and WireGuard were compared across three comprehensive scenarios: bandwidth measurement (iPerf3), file transfer of various formats (Seafile), and video streaming (HTML5). QoS parameters were evaluated based on the TIPHON standard, while QoE was measured using ITU-T G.107 to calculate the R-Factor and MOS. Wireshark analysis results show that WireGuard outperforms the others overall. In the bandwidth scenario, WireGuard achieved a transfer rate of 17.0 MB with a throughput of 14.0 Mbps. For file transfers, this protocol recorded the lowest delay of 32.27 seconds for document sized at 41.74 megabytes. and the highest throughput of 12.14 Mbps for images with minimal jitter. In the streaming scenario, WireGuard produced an R-Factor of 94.085 and a MOS of 4.421, falling into the “Very Good” and “Excellent” categories with no packet loss. In conclusion, WireGuard is recommended as the most optimal VPN protocol for securing dynamic network infrastructure with complex route redistribution.
ANALISIS KOMPARATIF SISTEM ERP UNTUK USAHA KECIL MENENGAH (UKM) RETAIL MENGGUNAKAN METODE TOPSIS Abdul Azis Al Baehaqi; Supriyono; Soni Adiyono
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.v11i2.8023

Abstract

Retail SMEs in Indonesia face significant challenges in selecting the right Enterprise Resource Planning (ERP) system due to budget constraints, limited human resources, and lack of systematic evaluation guidance. This research develops a desktop-based decision support system using Python 3.12 with the Flet framework, implementing the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) method to assist retail SMEs in interactively selecting optimal ERP. The research analyzes seven ERP alternatives (SAP Business One, Oracle NetSuite, PeopleSoft, webERP, Compiere, Odoo, and Accurate Online) using eight main criteria with 26 sub-criteria covering Cost, Functionality and Integration, Time and Availability, Usage and Support, Data Management, Reputation and Strategy Vendor, System Quality, and Scalability. Criteria weights are established referring to systematic literature review with System Quality (0.254) and Data Management (0.248) as highest priorities. Each alternative was assessed based on a review of official vendor documentation, verified review platforms, and relevant academic literature. Analysis results show Odoo ranks first (Ci* = 0.7668), followed by Accurate Online (Ci* = 0.6985), indicating the superiority of open-source and local solutions in cost, system quality, and flexibility for Indonesian retail SME context. The developed decision support system provides practical contribution for retail SMEs in strategic ERP selection decision-making while offering an adaptive evaluation framework for various industry contexts.
PERKEMBANGAN PENELITIAN GREEN HOUSING TAHUN 2020-2025: SYSTEMATIC LITERATURE REVIEW MENGGUNAKAN PUBLISH OR PERISH DAN VOSVIEWER febby asteriani; Ade Wahyudi; rona muliana
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.v11i2.8024

Abstract

This research is a systematic study based on Systematic Literature Review (SLR) using a bibliometric approach to map the development of scientific literature on green housing research topics from 2020 to 2025. A total of 100 scientific articles in the form of journals and books were obtained from the Google Scholar database as the main source of data collection. The literature selection process was carried out using Publish or Perish (PoP) software to extract and filter articles based on relevance and citations within the year range and the selection results obtained 90 scientific articles, while network analysis and visualization were carried out using VOSviewer. The main objectives of this study are to map the emergence of keywords (co-occurrence) and bibliometric analysis of collaboration between authors (co-authorship) to identify patterns, trends, and research networks in the field of green housing over the past five years. The results of the co-occurrence analysis produced seven thematic research clusters, namely: technological innovation in the development of environmentally friendly housing, urban green spaces, environmentally friendly housing and consumer preferences, sustainable housing and green architecture, green gentrification, green infrastructure and green innovation. Meanwhile, a bibliometric analysis of co-authorship identified the five most productive and influential authors in the 2020–2025 period. All of these most active researchers were affiliated with academic institutions in China, indicating a lack of cross-border collaboration. These findings provide a comprehensive overview of the current green housing research landscape and can serve as a strategic reference for researchers, practitioners, and policymakers in determining future research directions.
KLASIFIKASI JENIS PERMASALAHAN APLIKASI GOJEK PADA GOOGLE PLAY STORE BERDASARKAN ULASAN PENGGUNA MENGGUNAKAN ALGORITMA SUPPORT VECTOR MACHINE DAN NAIVE BAYES Nur Rahma Keysha Maharani Maharani; Rujianto Eko Saputro
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.v11i2.8029

Abstract

User reviews of the Gojek application on the Google Play Store contain various types of information regarding problems experienced by users. However, most previous studies have focused on sentiment analysis and have not been able to identify specific problem types. This study aims to classify problem types in Gojek user reviews into five categories: login, transaction, system disruption, feature error, and other issues. Data were collected through web scraping from the Google Play Store between January 2024 and April 2026, targeting 5,000 reviews. After removing empty and duplicate records, 3,753 reviews were retained for analysis. Labeling was performed using a keyword-based rule-based approach, followed by preprocessing stages including case folding, cleaning, tokenization, stopword removal, and stemming. Feature representation was conducted using TF-IDF with a maximum of 2,500 features. Class imbalance in the training data was addressed using Random Over Sampling (ROS), while the dataset was split using an 80:20 ratio through stratified sampling. This study compares the performance of Support Vector Machine (SVM) and Naïve Bayes classifiers using accuracy, precision, recall, F1-score, and ROC-AUC metrics, with model validation performed through 5-fold Stratified K-Fold Cross Validation. The results show that SVM achieved the best performance, with an accuracy of 0.846, precision of 0.872, recall of 0.846, F1-score of 0.856, ROC-AUC of 0.903, and an average cross-validation F1-score of 0.8509. In contrast, Naïve Bayes achieved an accuracy of 0.555, precision of 0.828, recall of 0.555, F1-score of 0.635, ROC-AUC of 0.836, and an average cross-validation F1-score of 0.6281. These results indicate that SVM performs better in classifying problem types in Gojek user reviews.
KOMPARASI KINERJA MACHINE LEARNING TEROPTIMASI SMOTE DAN PSO PADA KLASIFIKASI SENTIMEN ULASAN ROBLOX Amanda Diyas Setiyoadi; Yudie Irawan; Soni Adiyono
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.v11i2.8034

Abstract

The emergence of digital platforms like Roblox has led to an increase in the number of user reviews on the Google Play Store. These reviews contain important information regarding public perception, satisfaction levels, and user complaints about the app. However, the large volume of reviews and the unstructured nature of the text make manual analysis inefficient. Therefore, an automated solution in the form of machine learning-based sentiment classification is needed. This study was conducted to evaluate and compare the effectiveness of three machine learning algorithms, namely Logistic Regression, Support Vector Machine (SVM), and Random Forest, in classifying Roblox app review sentiment into three categories: positive, neutral, and negative. The research data consisted of 10,000 reviews collected through a crawling process from the Google Play Store. Synthetic Minority Oversampling Technique (SMOTE) was applied to address class imbalance, while Particle Swarm Optimization (PSO) was used to optimize model parameters. Experimental results show that Random Forest combined with SMOTE achieved the highest performance with an accuracy of 0.7219, a precision of 0.7241, a recall of 0.7219, an F1-score of 0.7228, and an AUC of 0.778. However, the accuracy of 72.19% is still a limitation for direct practical application, so further improvements are needed. This study also developed a Streamlit-based dashboard to monitor sentiment classification results in real-time. Based on these findings, the combination of Random Forest and SMOTE can be considered quite effective, although it still has limitations in the level of model accuracy.
ANALISIS SENTIMEN TERHADAP PROGRAM MAKAN BERGIZI GRATIS DI MEDIA SOSIAL X BERBASIS PEMBELAJARAN MESIN aufa hanif; Muhammad Arifin; Yudie Irawan
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.v11i2.8035

Abstract

Social media X provides many public responses to the Free Nutritious Meal Program (MBG), including support, questions, criticism, and neutral information. This study processes 6,000 tweets related to MBG to identify the direction of public opinion using a machine learning approach. The research flow consists of sentiment labeling, text cleaning, TF-IDF weighting, and model testing using Naive Bayes, Random Forest, and Support Vector Machine. The sentiment distribution shows 3,087 positive tweets, 2,213 neutral tweets, and 700 negative tweets. Model testing shows that Random Forest produced the strongest result with 94.75% accuracy, 95.66% precision, 94.75% recall, and 94.93% F1-score. These findings indicate that Random Forest is more suitable for recognizing sentiment patterns in the MBG tweet dataset than the other two models. The study also presents the analysis through a web-based system containing dashboard, dataset import, sentiment data, preprocessing, training, evaluation, and new opinion classification features.
ANALISIS PERFORMA OCR TESSERACT DAN CRNN PADA DOKUMEN SURAT JALAN SEMI-TERSTRUKTUR: ANALYSIS OF TESSERACT AND CRNN OCR PERFORMANCE ON SEMI-STRUCTURED DELIVERY DOCUMENTS Ali As'ad; Iska Yanuartanti; Danang Erwanto
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.v11i2.8038

Abstract

A delivery note (surat jalan) is a critical document in logistics that demands accurate data recording; however, the manual methods currently employed are often inefficient and error-prone, particularly in high-volume environments. This study aims to evaluate the performance of Tesseract-based Optical Character Recognition (OCR) and Convolutional Recurrent Neural Network (CRNN) in recognizing text on semi-structured documents. Utilizing a comparative experimental approach, this research utilizes a dataset of 200 document images comprising printed text and handwriting under various conditions. A total of 33 images were designated as test data, while the remaining images were used as training data with augmentation. The developed system encompasses image preprocessing, text recognition, and field extraction using regular expressions. Evaluation was conducted using Character Error Rate (CER), Word Error Rate (WER), and Match Error Rate (MER) metrics. The results indicate that Tesseract OCR outperforms at the character level (CER) at 42.84%, whereas OCR+CRNN demonstrates relatively better performance at the word and overall matching levels (WER and MER) at 68.24% and 51.56%, respectively. It is important to note that both values remain very high, a CER of 42.84% indicates that nearly half of all characters are still incorrectly recognized, while a WER of 68.24% means more than two-thirds of words contain errors, rendering the system not yet suitable for practical deployment. However, the performance improvement by CRNN is not yet significant, indicating limitations in the volume and variety of the training data. Furthermore, system performance is highly influenced by document characteristics, where printed text yields better results compared to limited and non-representative handwritten text. In the information extraction phase, structured fields achieve higher accuracy than complex fields, confirming that OCR output quality is the primary factor in extraction success. This study demonstrates that the selection of an OCR method must be tailored to document characteristics and underscores the importance of larger, more diverse datasets to enhance the performance of deep learning-based models.
IMPLEMENTASI FT-TRANSFORMER UNTUK KLASIFIKASI PENYAKIT DARAH BERDASARKAN PARAMETER HEMATOLOGI RUMAH SAKIT ROYAL PRIMA MEDAN Elisya Mutiara Br Sitanggang; Rinaldy Oscar Dery Lubis; Faeri Berkat Zai; Enggar Satrio; sautdohot siregar
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.v11i2.8040

Abstract

This study aims to develop a Deep Learning model based on the Feature Tokenizer Transformer (FTTransformer) architecture for blood disease classification using 103,024 retrospective secondary Complete Blood Count (CBC) patient data from Royal Prima Hospital Medan. The self-attention mechanism in this model is implemented to automatically map complex interactions among hematological parameters without requiring manual feature engineering. Testing results demonstrate that the FT-Transformer effectively overcomes the challenges of highly imbalanced clinical data, yielding superior and stable multi-class classification performance. This is evidenced by Accuracy, Precision, Recall, and F1-Score metrics reaching 0.98 to 1.00 across four main diagnostic categories: Normal, Anemia, Sepsis/Infection, and Thrombocytopenia. Overall, this computational approach successfully produced a robust and high-precision Clinical Decision Support System (CDSS) prototype for interpreting tabular laboratory results.
PERSPEKTIF BIBLIOMETRIK TERHADAP INTEGRASI METAVERSE DALAM PENDIDIKAN: TREN PUBLIKASI, PENULIS, DAN ARAH PENELITIAN TERKINI Muhammad Fadli; Dian Sri Purwanti; Windia Hanifah; Valdi Mughni Budiman; Rifka Simbolon; Riyan Maruly; Amarudin
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.v11i2.8047

Abstract

The Metaverse has emerged as a promising digital technology in education due to its ability to create immersive, interactive, and collaborative learning environments. This study aims to analyze the development of research on the integration of the Metaverse in education through a bibliometric approach. The research data were collected from the Dimensions database using the keywords “Metaverse” and “Education,” resulting in a total of 636 publications published between 2015 and 2025. Bibliometric analysis was conducted and visualized using VOSviewer software to identify publication trends, productive authors, leading contributing countries, and emerging research themes. The findings reveal a significant increase in the number of publications since 2021, with the highest publication output recorded in 2024. China, South Korea, and the United States were identified as the leading contributors to the field, while Hwang G.J. was recognized as one of the most prolific authors. Keyword network analysis indicates that the dominant research themes focus on the Metaverse, Virtual Reality, Augmented Reality, learning, and immersive learning experiences. Furthermore, emerging topics such as digital twins, artificial intelligence, and technology acceptance have begun to gain attention as potential directions for future research. These findings suggest that Metaverse research in education is developing in a multidisciplinary manner and holds significant potential to support the transformation of digital learning in the future.
IMPLEMENTASI SECURITY INFORMATION AND EVENT MANAGEMENT (SIEM) MENGGUNAKAN WAZUH UNTUK DETEKSI DAN ANALISIS INSIDEN KEAMANAN WEB SERVER: IMPLEMENTATION OF SECURITY INFORMATION AND EVENT MANAGEMENT (SIEM) USING WAZUH FOR DETECTION AND ANALYSIS OF WEB SERVER SECURITY INCIDENTS Rizky Adhytia; Taqwa Hariguna
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.v11i2.8054

Abstract

The rapid development of web server technology has increased the risk of cyber threats such as brute force and Distributed Denial of Service (DDoS) attacks. This study aims to implement a Security Information and Event Management (SIEM) system using Wazuh to detect and analyze security incidents on a web server in real time. The research method used is experimental, consisting of requirements analysis, system design, implementation, testing, and evaluation. The system is built using a Wazuh Server, a Wazuh Agent installed on an Ubuntu-based web server, and Telegram notification integration for automatic alerts to administrators. Testing was conducted through attack simulations using Hydra for SSH brute force, Slowloris, and DDoS-Ripper for DoS attacks. The results show that the system successfully detected various attacks with Rule ID 5712 and 5763 for SSH brute force, Rule ID 100502 for Slowloris HTTP flood, and Rule ID 100500 and 100501 for DDoS-Ripper. All attacks were successfully detected and reported to Telegram in real time. The Wazuh-based SIEM implementation improves monitoring, detection, and response capabilities for web server security incidents in a centralized manner and provides better security visibility for administrators in handling cyber threats.  

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