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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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VALIDASI ALGORITMA CHUMLEA UNTUK PREDIKSI TINGGI BADAN BERBASIS SENSOR ULTRASONIK PADA KURSI RODA CERDAS SMATSI Natasha Putri Rondonuwu; Jeki Kuswanto; Wahid Miftahul Ashari
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.v11i1.7188

Abstract

Height measurement is an important clinical indicator for nutritional status assessment but is difficult to perform on patients with mobility limitations or spinal disorders. This study validates the accuracy of the Chumlea Algorithm in predicting height using automatic knee height measurement based on ultrasonic sensors on the SMATSI Smart Wheelchair. A cross-sectional validation study was conducted on 60 measurement data (30 males and 30 females) from an elderly adult population. The best algorithm selection used Multi-Criteria Decision Making (MCDM) methods: Simple Additive Weighting (SAW) and Analytical Hierarchy Process (AHP) with four criteria (Bias, MAE, RMSE, Correlation). Results show that the Chumlea Algorithm has the highest accuracy with an average difference of 0.3cm (males) and 0.8 cm (females) from actual height, not statistically significant (p > 0.05). MCDM analysis confirmed Chumlea as the best choice with first ranking in both methods (SAW: 1.000; AHP: 0.524). Pearson correlation shows very strong relationship (r = 0.82-0.85, p < 0.001). Bland-Altman analysis shows Limits of Agreement (LoA) -4.0 to +5.1 cm, which is within clinically acceptable range for BMI calculation in this population. This accuracy is much better than conventional studies in Indonesia showing bias of 3.44–7.33 cm. Automatic knee height measurement using ultrasonic sensors eliminates systematic errors of manual measurement.
ANALISIS PENGARUH CUACA DAN PM2.5 TERHADAP PREVALENSI ISPA DI KOTA SEMARANG DENGAN PENDEKATAN STATISTIK DAN MACHINE LEARNING: ANALYSIS OF THE INFLUENCE OF WEATHER VARIABLES AND PM2.5 ON THE PREVALENCE OF ACUTE RESPIRATORY INFECTIONS IN SEMARANG CITY USING STATISTICAL AND MACHINE LEARNING APPROACHES Cahyono Rahadiyanto; 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.v11i1.7190

Abstract

Acute Respiratory Infection (ARI) remains a significant public health problem in Indonesia, particularly in tropical coastal urban areas such as Semarang City. This study aims to analyze the effect of air quality (PM2.5) together with weather variables such as wind speed, temperature, humidity, and rainfall on ARI prevalence. Daily ARI case data based on ICD-10 codes from hospitals and meteorological data from BMKG for the period 2017–2024 were analyzed using Negative Binomial Regression for statistical inference, and artificial intelligence algorithms, namely Random Forest and SVM, for predictive modeling. The results show that ARI dynamics are predominantly influenced by temporal factors from previous cases, with weekly case variables (cases_ma_7 and total_kasus_lag7) emerging as the most important predictors. Environmental variables act as secondary modulators: temperature (TAVG_lag9) has an adverse effect, while wind speed (FF_AVG_lag0) has a direct positive impact. The Random Forest model demonstrates the best predictive performance with R² = 0.4443, RMSE = 4.98, MAE = 3.88, and MAPE = 35.77%. This study concludes that an ensemble learning approach using Random Forest is more accurate than SVM and statistical models for ARI prediction. Future research can explore hybrid artificial intelligence prediction and classification models that may serve as a basis for developing weather‑based early warning systems to support public health mitigation policies, particularly for ARI.
INTEGRASI DESIGN SCIENCE RESEARCH DAN DESIGN THINKING DALAM OPTIMALISASI FITUR SISTEM INFORMASI AKADEMIK (SIAKAD) PRADITA: INTEGRATION OF DESIGN SCIENCE RESEARCH AND DESIGN THINKING IN OPTIMIZING THE FEATURES OF PRADITA'S ACADEMIC INFORMATION SYSTEM (SIAKAD) Tara Anabel Christy Lianda; 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.v11i1.7193

Abstract

This study aims to design and evaluate a new feature prototype in the Pradita Academic Information System (SIAKAD) as an artifact within the context of Design Science Research (DSR), with the goal of improving student satisfaction with academic services. Design Thinking (DT) and DSR methodologies are systematically integrated to develop a user-centered solution validated by empirical data. Qualitative evaluation is conducted through a Heuristic Evaluation (HE) conducted by a user experience (UX) expert, while quantitative evaluation is conducted using the Post-Study System Usability Questionnaire (PSSUQ). The results of this study show improvements in usability and interface quality, with an average PSSUQ score of 2.90, which is included in the "good" category. From a practical perspective, this study contributes by providing a structured framework that can be adopted by other academic system developers to optimize user experience through evidence-based design iterations.
ANALISIS KEAMANAN SISTEM INFORMASI MANAJEMEN PENGAWASAN INSPEKTORAT JENDERAL KEMENTERIAN KEHUTANAN MENGGUNAKAN METODE OWASP ZAP: INFORMATION SYSTEM SECURITY ANALYSIS OF THE INSPECTORATE GENERAL AT THE MINISTRY OF FORESTRY USING THE OWASP ZAP METHOD Allan Yuliansyah; Zulhalim; Anton Zulkarnain Sianipar
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.v11i1.7195

Abstract

This Supervision Management Information System (SIMAWAS) is used to support the internal oversight process within the Inspectorate General of the Ministry of Forestry. However, the application has never undergone a comprehensive security assessment, especially after the domain change. This study aims to identify vulnerabilities in SIMAWAS using penetration testing methods with OWASP ZAP. The testing stages include planning, automated scanning, alert analysis, and recommendation formulation. The risk levels (Medium, Low, and Informational) were classified based on the OWASP Risk Rating Methodology, considering the likelihood and potential impact of each identified vulnerability. The results show that SIMAWAS has no high-risk vulnerabilities, but several Medium, Low, and Informational weaknesses were found, such as security misconfiguration, missing security headers, and insecure cookie settings. These vulnerabilities may be exploited if not addressed properly. Improvement recommendations were developed based on OWASP standards to enhance application security and prevent potential exploitation in the future.
EARLY DETECTION OF DOWN SYNDROME BASED ON FACIAL IMAGES USING A HYBRID CONVOLUTIONAL NEURAL NETWORK CINANTYA PARAMITA; Gifari Hilal Hilmi Nashif
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.v11i1.7196

Abstract

Down Syndrome is a multisystem genetic disorder serving as a primary cause of intellectual disability, where delayed diagnosis often obstructs access to crucial developmental interventions. Conventional clinical diagnosis, which relies on the subjective observation of dysmorphic features, is frequently constrained by the scarcity of medical experts, necessitating the development of automated systems based on facial images to support objective and accessible early detection. This study aims to evaluate and compare the performance of single deep learning models with an innovative hybrid architecture to enhance the accuracy of screening systems. The research methodology employs a dataset of 2,666 facial images of toddlers, processed using Convolutional Neural Networks (CNN) through a transfer learning approach. Comprehensive experiments compared InceptionV3 and EfficientNetB3 architectures both as standalone models and within a hybrid ensemble while assessing the efficacy of feature extraction versus fine-tuning strategies. The results demonstrate that fine-tuning significantly outperforms feature extraction, yielding a 10-12% performance increase due to more specific feature adaptation. The hybrid ensemble model utilizing fine-tuning emerged as the superior approach, achieving a peak validation accuracy of 92.32% and an ghF1-Score of 92.33%. This model proved robust against pose and expression variations while effectively minimizing false negatives. Consequently, integrating computational strengths through a hybrid architecture produces rich feature representations, establishing this method as a reliable and precise solution for medical screening.
PERBANDINGAN KINERJA SVM DAN BERT DALAM ANALISIS SENTIMEN KOMENTAR TIKTOK QRIS DI JEPANG DAN CHINA: COMPARISON OF SVM AND BERT PERFORMANCE IN SENTIMENT ANALYSIS OF TIKTOK COMMENTS ON QRIS IN JAPAN AND CHINA Safara Agastya; Nurul Winarsih; Filmada Saputra; Danny Ratmana; Anggun Suasana
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.v11i1.7199

Abstract

The launch of the Indonesian Standard Quick Response Code (QRIS), scheduled for August 17, 2025, in Japan and China, requires an evaluation of public response to support the development of cross-border digital payment policies. This research aims to analyze Indonesian public sentiment toward QRIS usage in Japan and China through TikTok comments, compare the performance of the Support Vector Machine (SVM) model against the Bidirectional Encoder Representations from Transformers (BERT) approach, and provide methodological recommendations for fintech sentiment analysis in Indonesia. The study employs a quantitative approach, utilizing 1,429 TikTok user comments collected through web scraping. The data underwent cleaning, preprocessing, and labeling using TextBlob. The data were then split using a 5-fold cross-validation scheme and implemented on an SVM model with TF-IDF representation and the SMOTE technique, as well as a BERT model with data augmentation and the application of class weights. The evaluation results show that BERT achieved an accuracy of 84% with evaluation metric values ranging from 0.84 to 0.85, while SVM achieved an accuracy of 80% with consistently stable evaluation metric values of 0.80. The research confirms that deep learning approaches based on pretrained language models are optimal for social media sentiment analysis in fintech. BERT can support policymakers in monitoring public sentiment in real-time for international digital payment service development.  
COMPARATIVE STUDY OF MACHINE LEARNING MODELS FOR CLASSIFYING SENTIMENT IN GOOGLE GEMINI APP REVIEWS Muhammad Dzaky Alifayoezra; Ali Ibrahim; Yadi Utama; Endang Lestari Ruskan; Dwi Rosa Indah
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.v11i1.7201

Abstract

The rising number of reviews for the Google Gemini app on the Google Play Store reflects diverse user opinions regarding the performance of this AI-based application. To identify sentiment patterns, this research conducted a comparative study of three classification algorithms—Support Vector Machine (SVM), Naive Bayes, and Random Forest—using 14,479 raw reviews collected through scraping. These reviews then went through several preprocessing steps, including case folding, text cleaning, tokenization, normalization, stopword removal, and stemming. After being labeled based on ratings, the dataset formed a highly imbalanced class distribution, consisting of 11,252 positive reviews and 1,571 negative reviews, and was subsequently split using the Hold-Out method with an 80% training and 20% testing ratio. Evaluation using the Confusion Matrix along with accuracy, precision, recall, and F1-score metrics showed that SVM achieved the best performance, producing 91% accuracy, 93% precision, 97% recall, and a 95% F1-score, outperforming Random Forest and Naïve Bayes, which each reached 90% accuracy. Overall, these results highlight SVM as the most effective algorithm for classifying sentiment in Google Gemini reviews, while the predominance of positive feedback suggests a relatively high level of user satisfaction, although model performance on the minority (negative) class remains a challenge due to data imbalance.
ANALISIS ALGORITMA MACHINE LEARNING DENGAN TEKNIK SMOTE UNTUK PENINGKATAN SENSITIVITAS MODEL DETEKSI SINDROM OVARIUM POLIKISTIK (PCOS) Mamay Maida; M. Arief Soeleman; Hestiana Putri Novitasari; Sifa Ayu Rosita Sari
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.v11i1.7203

Abstract

Polycystic Ovary Syndrome (PCOS) is an endocrine disorder affecting approximately 6–21% of women of reproductive age but is often difficult to detect in its early stages. This study develops a PCOS detection model using machine learning and the Synthetic Minority Oversampling Technique (SMOTE) to address class imbalance between positive and negative classes. Four algorithms were tested: Logistic Regression (LR), K-Nearest Neighbors (KNN), Decision Tree (DT), and Random Forest (RF). The dataset used was obtained from Kaggle and underwent preprocessing, including data cleaning, encoding, and feature selection based on correlation. After balancing the data using SMOTE, standardization was applied to ensure feature scale consistency.Model evaluation was carried out using a confusion matrix as the basis for calculating Accuracy, Precision, Recall, F1-score, Specificity, and Negative Predictive Value (NPV), with Recall as the primary focus. The results show that LR achieved the highest Recall (0.92), while RF demonstrated the best performance balance with Accuracy (0.92), F1-score (0.87), Specificity (0.95), and NPV (0.93). KNN and DT obtained the same Recall value (0.83), although their Precision and F1-score were slightly lower. In addition, a before–after analysis was conducted to evaluate the effect of SMOTE, and the McNemar test was used to assess the statistical significance of performance differences between models. Feature Importance analysis revealed that follicle count, menstrual cycle length, and fast-food consumption patterns are the most influential factors contributing to PCOS risk. These findings indicate that the application of SMOTE significantly enhances model sensitivity and has strong potential to be developed as a decision support system in reproductive health.
YOLOV8S AS THE OPTIMAL MODEL FOR AUTOMATED PNEUMONIA DETECTION: A COMPARATIVE STUDY CINANTYA PARAMITA; Nila Farihah; Anamarija Jurcev Savicevic
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.v11i1.7213

Abstract

Pneumonia remains a leading cause of global mortality, accounting for nearly 15% of deaths among children under five worldwide, thus requiring early and accurate detection. This research examines three YOLOv8 variants: YOLOv8n, YOLOv8s, and YOLOv8m to determine their effectiveness in classifying Chest X-Ray (CXR) images into normal, bacterial pneumonia, and viral pneumonia categories. The dataset was divided into training 80%, validation 10%, and testing 10%, with each image resized to 640 × 640 pixels. The models were checked using three measures: Precision, Recall, and mAP@50. The experimental evaluation revealed that all models produced high accuracy, each achieving mAP@50 scores above 0.90. Among the tested variants, YOLOv8s delivered the most optimal results, obtaining mAP@50 of 0.917, precision of 0.873, and recall of 0.889. YOLOv8m showed consistent performance and strong detection capability for normal lung images, while YOLOv8n achieved the highest recall value (0.894) with efficient computational demands. The superior outcome of YOLOv8s is attributed to its well-balanced architectural design, enabling enhanced detection while minimizing overfitting. Overall, the findings indicate that the moderately complex YOLOv8s model provides the most effective combination of speed, a
DETEKSI PENYAKIT GLAUKOMA MENGGUNAKAN DETEKSI OD DAN SEGMENTASI BV DENGAN ALGORITMA SVM Yenny Rahmawati; Ilham Fanani; Luthfia Nurma Hapsari; Ahmad Muharya
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.v11i1.7217

Abstract

Glaucoma is a neurodegenerative disease that causes damage to the optic nerve head and visual field defects. Although the neural aspect is often the main focus, vascular factors play a crucial role but are often overlooked in conventional diagnosis. This study proposes a glaucoma detection approach based on optic disc (OD) detection and blood vessel (BV) segmentation as a more targeted GLCM texture feature extraction stage. The dataset used consists of 550 glaucoma images and 200 non-glaucoma images. The extracted features were then classified using a Support Vector Machine (SVM) with an RBF kernel. The results show that the model achieves an accuracy of 76% with a weighted average F1-score of 0.68. An in-depth evaluation of each class shows significant performance in the glaucoma class with a recall value of 0.99, indicating that the post-segmentation OD and BV texture features are highly informative in capturing structural damage patterns. However, in the non-glaucoma class, a low recall value of 0.12 with a precision of 0.80 was found, indicating a tendency for false positives. The high recall value in the glaucoma class shows that this method is very effective and reliable for early detection (screening) purposes, where minimizing the risk of false negatives is a top priority in clinical diagnosis.

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