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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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DASHBOARD SEBARAN KARTU KMILN/DIASPORA DENGAN MENGGUNAKAN PYTHON LIBRARY PANDAS, NUMPY, MATPLOTLIB DAN GRADIO: DASHBOARD FOR KMILN/DIASPORA CARD DISTRIBUTION USING PYTHON LIBRARIES PANDAS, NUMPY, MATPLOTLIB, AND GRADIO Dana Abdulrachman; Verdi Yasin; Akmal Budi Yulianto
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.v11i1.7140

Abstract

This management of data for the Indonesian Citizens Abroad Card (KMILN) has traditionally been presented in static table formats, making it difficult to analyze trends, monitor diaspora distribution, and generate comprehensive reports. This study aims to design an interactive KMILN/Diaspora dashboard using the prototype method to ensure an iterative development process that aligns with user needs. The dashboard was developed using Python with Pandas, NumPy, Matplotlib, and a Gradio interface to process and visualize the data. The development stages included requirement communication, rapid planning, initial design, prototype construction, testing, and refinement. The results show that the dashboard successfully displays diaspora distribution charts, growth trends, geographic mapping, and employment data interactively, supported by filtering features such as country, Indonesian missions (KBRI), and diaspora categories. The implementation of this dashboard simplifies data analysis, enhances information accuracy, and supports data-driven decision-making within the Directorate of Diaspora Affairs.
PENERAPAN INDOBERT DAN BERTOPIC DALAM ABSA UNTUK EVALUASI KUALITAS APLIKASI E-GOVERNMENT INDONESIA Nathanael Abel Adrielvino; Afifah Trista Ayunda
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.v11i1.7143

Abstract

This research examines user responses concerning public service applications from the Indonesian government. The information collected from the Google Play Store between January 1, 2025, and October 31, 2025, final dataset  consists of 7,000 reviews that were labeled manually. The primary goal is to apply Aspect-Based Sentiment Analysis (ABSA) via E-S-QUAL to assess sentiment across four dimensions (efficiency, fulfillment, system availability, and privacy). In the classification process, IndoBERT Fine-Tuned model's efficacy was compared to traditional models (SVM and Naïve Bayes, showing superiority by reaching an overall accuracy of 91%, positioning it as a benchmark for all sentiment analysis. The model then directed the BERTopic analysis to pinpoint and explain the specific issues raised by users. The general results indicate that user sentiment towards the applications is predominantly Negative (62.71%). Through the application of topic modeling and E-S-QUAL mapping, it was found that System Availability (58.24% Negative) and Fulfillment (78,32% Negative) are the key contributors to dissatisfaction, arising from particular grievances such as system outages, service disruptions, and Privacy aspect (91.67% Negative) that concerns users. Conversely, the Efficiency aspect emerges as a crucial service strength, garnering a dominant positive reaction (58.96%). Therefore, these findings clearly suggest that the government must prioritize immediate action to improve system stability and ensure consistent delivery of services, thereby realizing the full potential of its digital transformation efforts.  
ANALISIS METRIK KETERLIBATAN TERHADAP PERTUMBUHAN PENGIKUT AKUN INSTAGRAM ITBSS: PENDEKATAN ANALISIS STATISTIK Melyanto Melyanto
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.v11i1.7147

Abstract

Digital transformation has significantly changed higher education marketing. Higher Education is now competing not only in academic quality but also in marketing strategies, particularly through social media platforms such as Instagram. However, the success of marketing campaigns on Instagram needs to be measured quantitatively using available metrics such as engagement which includes likes, comments, shares, reach, and profile visit. These metrics could be used to analyze and evaluate the impact for institution brand awareness by identifying the numbers of followers’ growth. This quantitative research utilized insights data from the Institut Teknologi dan Bisnis Sabda Setia (ITBSS) Instagram account engagement metrics. Statistical analysis involved including descriptive analysis, Pearson’s correlations and regression analysis. This research found an outlier and multicollinearity between interaction and profile visit metrics. Engagement metrics have positive correlation and 58% of follower’s growth explained by engagement metrics. The F-test yielded a result of 0.19, indicating that the regression model is not yet significant in predicting follower growth. The findings are expected to assist ITBSS digital transformation by utilizing engagement metrics efficiently to increase follower’s growth. Digital transformation in Instagram usage as marketing medium for ITBSS contributes significantly through findings of a positive correlation between engagement metrics and follower growth. However, in terms of the model, it is not yet significant and requires further research in the future.  
WEB SCRAPING DAN FINE-TUNING INDOBERT UNTUK ANALISIS SENTIMEN BERBASIS ASPEK (ABSA) PADA DATA TWITTER/X: STUDI KASUS TOPIK KURIKULUM MERDEKA Dzaki Syauqi Anthera Mumtaz; Nuur Wachid Abdul Majid
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.v11i1.7155

Abstract

This study develops an Aspect-Based Sentiment Analysis (ABSA) system to assess public opinion on the Kurikulum Merdeka policy using data from platform X. The approach integrates web scraping, Indonesian linguistic-aware preprocessing, fine-tuning of IndoBERT, Naive Bayes, and an ensemble weighted voting strategy to extract aspect-level sentiment. The dataset consists of 345 tweets collected between October 20 and November 10. The findings indicate that the most frequently discussed aspects include general issues, teacher readiness, and student impact, with negative sentiment emerging in several categories. The model achieved an accuracy of 91.59%, with a macro F1-score of 86.43% and a weighted F1-score of 91.69%. The study also identifies data limitations, particularly tweets that are short or multimedia-based, which often result in neutral classifications. Future improvements may include expanding the dataset, enhancing annotation quality, and exploring newer transformer-based approaches.
EKSPLORASI PRE-PROCESSING SPEKTROSKOPI VIS UNTUK REGRESI DEEP LEARNING DALAM PENGUKURAN ASAM URAT NON-INVASIF: EXPLORATION OF VIS SPECTROSCOPY PRE-PROCESSING FOR DEEP LEARNING REGRESSION IN NON-INVASIVE URIC ACID MEASUREMENT Farras Adhani Zayn; Heru Agus Santoso
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.v11i1.7165

Abstract

Invasive methods of blood sampling can cause mild pain or anxiety for patients. Non-invasive devices developed have the potential for finger shifting or misplacement, resulting in data noise. Erick et al. conducted a study extracting features from photoplethysmograms to estimate blood cholesterol. However, this study focused solely on calculating estimated cholesterol values. This study aimed to gain insight into the potential impact of data processing on visual spectroscopy data. Preprocessing studies were conducted on the visual spectroscopy dataset using value conversion, transpose, and CTGAN as sampling-based data augmentation methods. After separate training of all four methods, it was found that dataset size, both the number of features and the number of samples, significantly impacted the deep learning model, particularly the relationship between labels and features. Although the transpose method provided the highest R2 score of 0.123 with an MAE of 1.03 in the CNN-1D model, compared to the MLP model, which produced an R2 score of 0.122, and the LSTM model, which produced an R2 coefficient of 0.026, the accuracy of uric acid prediction still did not reach clinical levels.
INDOBERT-BASED DETECTION OF HATE SPEECH AND HOAXES CONTENT RELATED TO DPR-RI Fadillah Zalsa Dira; Heru Agus Santoso
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.v11i1.7170

Abstract

Social media has become one of the primary communication channels for Indonesians, but it has also contributed to the increasing circulation of hate speech and hoaxes, which can polarize communities and undermine public trust. This situation highlights the urgent need for an automated system capable of identifying harmful content quickly and accurately. This study aims to develop a multi-label classification model to detect hate speech and hoax-related content in Indonesian social media text using a Transformer-based architecture. The model employed is IndoBERT, a variant of the Bidirectional Encoder Representations from Transformers (BERT) that is specifically trained on a large Indonesian corpus, making it more contextually relevant and effective than multilingual models for this task. The dataset consists of 1020 Indonesian social media texts annotated with binary labels for two categories: hate speech (0 = non-hate, 1 = hate) and hoax (0 = non-hoax, 1 = hoax). The research process includes text cleaning, tokenization, handling class imbalance, fine-tuning the model, and evaluating its performance using exact match accuracy, precision, recall, and F1-score at both micro and macro levels. To address class imbalance in the hoax label, the RandomOverSampler (ROS) technique was applied to enhance model stability and generalization. The experimental results show that IndoBERT achieved an accuracy of 97.55%, indicating that the model performs effectively in detecting harmful content and provides meaningful support for digital content moderation within the Indonesian online ecosystem.
PEMODELAN TOPIK PEMBERITAAN KESEHATAN MASYARAKAT DI MEDIA ONLINE MENGGUNAKAN LATENT DIRICHLET ALLOCATION: TOPIC MODELING OF PUBLIC HEALTH NEWS IN ONLINE MEDIA USING LATENT DIRICHLET ALLOCATION Nur Bainatun Nisa; Armansyah
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.v11i1.7172

Abstract

Online media has become the primary source of public health news due to its ease of access and wide dissemination of information. However, the abundance of information makes it difficult to identify key issues and map public opinion trends, resulting in a mismatch between information needs and the necessary policy responses. This study aims to automatically model public health news topics in online media to reveal latent structures and quantify media attention to various health issues. A total of 8,799 public health news articles from the national news portals Detik and Kompas were analyzed using text pre-processing, numerical corpus formation using the Bag of Words method, topic modeling using Latent Dirichlet Allocation, and evaluation using the C_v coherence metric. The test results showed that the optimal model was obtained for three main topics with a coherence C_v value of 0.61. The distribution of media attention to these three topics can be quantified as follows: free health services at 46.4%, community lifestyle and nutrition at 35.3%, and health insurance policies and systems such as BPJS and JKN at 18.4%, which are visualized with clear topic separation and stable semantic relationships using pyLDAvis.  
SISTEM PERSEDIAAN STOK BARANG MENGGUNAKAN METODE AGILE BERBASIS WEB PADA BENGKEL THO’BONE SHANTIKA; Erick Irawadi Alwi; St. Hajrah Mansyur
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.v11i1.7174

Abstract

Inventory management at Bengkel Tho’Bone was previously conducted manually, which often resulted in data inconsistencies, delays in stock monitoring, and difficulties in generating accurate inventory reports. This study aims to design a web-based inventory management system using the Agile method as a software development approach to improve efficiency and accuracy in stock management. The study applies the CodeIgniter framework as the system development framework because it adopts the Model–View–Controller (MVC) architecture, which facilitates the separation of business logic, user interface, and data processing. The system development process is carried out through the stages of planning, design, development, testing, and evaluation, which are performed repeatedly according to user requirements. The system is designed to assist administrators and staff in managing item data, suppliers, as well as incoming and outgoing stock transactions. The results show that the developed system is capable of automating stock recording, providing restock notifications when inventory reaches the minimum threshold, and generating structured and easily accessible inventory reports. The implementation of this system improves operational efficiency, reduces human error, and enhances transparency in inventory management. Overall, the application of a web-based inventory management system using the Agile approach and the CodeIgniter framework has proven effective in optimizing inventory control and supporting accurate decision-making at Bengkel Tho’Bone.  
ENHANCED CHRONIC KIDNEY DISEASE PREDICTION USING OPTIMIZED SUPPORT VECTOR MACHINE WITH HYPERPARAMETER TUNING AND SMOTE CINANTYA PARAMITA; Warih Prasetyaningtyas
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.v11i1.7179

Abstract

Chronic Kidney Disease (CKD) is an escalating global health concern that demands faster and more accurate diagnostic solutions than traditional laboratory-based assessments. This study evaluates and compares the performance of three machine learning algorithms Random Forest (RF), Support Vector Machine (SVM), and Extreme Gradient Boosting (XGBoost) for early CKD prediction using the publicly available dataset from the UCI Machine Learning Repository. The research workflow includes comprehensive data preprocessing, handling missing values, addressing class imbalance using the Synthetic Minority Over-sampling Technique (SMOTE), and performing hyperparameter optimization with GridSearchCV combined with 5-fold cross-validation. The experimental results indicate that all evaluated models demonstrate strong predictive performance, with SVM achieving the best recall-oriented results, recording an accuracy of 0.93, a recall of 0.99, and an F1-score of 0.96. Feature importance analysis identifies Serum Creatinine, Glomerular Filtration Rate (GFR), Hemoglobin, Blood Pressure, and Age as the most influential clinical predictors of CKD. Overall, the findings demonstrate that integrating SMOTE-based imbalance handling with systematic hyperparameter tuning significantly enhances model robustness, while the SVM model provides high sensitivity, making it particularly suitable for early CKD screening and clinical decision-support applications.
OPTIMASI KINERJA SISTEM DETEKSI INTRUSI MENGGUNAKAN HYBRID XGBOOST DAN ARSITEKTUR DEEP LEARNING EFISIEN Bernandiko Priyambodo; Wildanil Ghozi; Fauzi Adi Rafrastara
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.v11i1.7182

Abstract

Rapid digital transformation has expanded the cyber attack surface, demanding a responsive and reliable Intrusion Detection System (IDS). The main obstacle in developing Deep Learning-based IDS is the high dimensionality of network traffic features and data class imbalance, which can trigger excessive computational loads. This study aims to develop an efficient and robust IDS model for detecting various types of cyber attacks using the CIC-IDS2017 dataset. The proposed method applies a hybrid approach integrating the XGBoost algorithm for high-importance feature selection to reduce data dimensionality, alongside a Multilayer Perceptron (MLP) architecture for classification. This research explores various neural network depth configurations combined with tanh and SELU activation functions to handle data non-linearity. Model performance is evaluated based on standard classification metrics as well as operational security metrics. Experimental results demonstrate that the proposed model achieved an accuracy of 99.39% with a low False Alarm Ratio (FAR) and Attack Miss Ratio (AMR) of 2.90%. This study contributes by presenting an intrusion detection framework capable of balancing architectural complexity and computational efficiency for implementation in modern network environments.

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