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Sinkron : Jurnal dan Penelitian Teknik Informatika
Published by Politeknik Ganesha Medan
ISSN : 2541044X     EISSN : 25412019     DOI : 10.33395/sinkron.v8i3.12656
Core Subject : Science,
Computer Science & IT
Scope of SinkrOns Scientific Discussion 1. Machine Learning 2. Cryptography 3. Steganography 4. Digital Image Processing 5. Networking 6. Security 7. Algorithm and Programming 8. Computer Vision 9. Troubleshooting 10. Internet and E-Commerce 11. Artificial Intelligence 12. Data Mining 13. Artificial Neural Network 14. Fuzzy Logic 15. Robotic
Arjuna Subject : Ilmu Komputer (semua kategori) - Visi Komputer dan Pengenalan Pola
Articles 1,259 Documents
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Comparative Evaluation of YOLOv8 and YOLOv11 for Student Behavior Detection in Classroom CCTV Environments Sofhia, Maya
Sinkron : jurnal dan penelitian teknik informatika Vol. 10 No. 1 (2026): Article Research January 2026
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/sinkron.v10i1.15648

Abstract

Monitoring student behavior during classroom learning is important for supporting learning quality and teacher performance. This study presents a pilot comparison between YOLOv8 and YOLOv11 for detecting student classroom behaviors from CCTV images. Six elementary behaviors are consistently defined and used throughout the work: lookup, raise-hand, read, stand, turn-head, and write. The available SCB dataset contains 4,934 labeled images, but this study deliberately uses a front-facing subset of 100 images that best represent clear posture and behavior. After augmentation, the dataset grows to 220 images, split into 180 training, 30 validation, and 10 testing images. Both models are trained for 25 epochs on a T4 GPU with comparable configurations. At the detector level, YOLOv11 achieves higher mean average precision (mAP) of 42.9% compared to 28.9% for YOLOv8. At the behavior level, overall classification accuracy on the test set is 43.3% for YOLOv8 and 37.5% for YOLOv11. These results indicate a trade-off: YOLOv11 provides stronger bounding-box detection performance, while YOLOv8 produces slightly more stable behavior-level predictions on this very small and imbalanced dataset. The study emphasizes that these findings are exploratory baselines rather than definitive benchmarks, because the dataset is small and no statistical significance testing is performed. Future work must use a larger portion of the SCB dataset, more balanced class distributions, repeated experiments, and statistical analysis to obtain more robust conclusion.
Enhancing Feature-Efficient Network Intrusion Detection Using Gradient Boosting and Chi-Square Selection on NSL-KDD Soares, Gilardinho Javiere Oscoraldo Pedrosa; Fauzi Adi Rafrastara; Ramadhan Rakhmat Sani
Sinkron : jurnal dan penelitian teknik informatika Vol. 10 No. 1 (2026): Article Research January 2026
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/sinkron.v10i1.15650

Abstract

This study examines the growing complexity of cyber threats that increasingly challenge the effectiveness of traditional Network Intrusion Detection Systems (NIDS). Modern attacks, particularly zero-day intrusions, require detection approaches capable of handling high-dimensional network traffic data. However, existing studies rarely examine the trade-off between feature efficiency and generalization performance in boosting-based NIDS under controlled feature-reduction strategies. Moreover, the role of statistical feature selection in mitigating overfitting in classical boosting models remains underexplored. This study evaluates the performance of NIDS by combining boosting ensemble algorithms, namely AdaBoost, Gradient Boosting, and XGBoost, with filter-based feature selection methods, including Information Gain, Chi-Square, and ReliefF. The NSL-KDD dataset is used as the primary benchmark, with Min–Max normalization applied during preprocessing to ensure numerical feature consistency. Model development is conducted using Orange Data Mining, and performance is assessed through 10-fold cross-validation. Experimental results show that Gradient Boosting achieves the highest baseline accuracy among the evaluated models. Further performance improvements are obtained through feature selection, with the Chi-Square method yielding the best result at 81.2% accuracy using 19 selected features. Information Gain also enhances performance, achieving 80.8% accuracy with 13 features, while ReliefF provides comparatively lower gains. These findings demonstrate that effective feature reduction improves generalization performance, reduces computational complexity, and mitigates overfitting. Overall, the proposed combination of Gradient Boosting and statistical feature selection provides a feature-efficient, generalizable intrusion detection strategy for modern network environments.
Optimizing URL-Based Phishing Detection Using XGBoost and Relief Feature Selection Tyas, Wahyu Suryaning; Rafrastara, Fauzi Adi; Ghozi, Wildanil
Sinkron : jurnal dan penelitian teknik informatika Vol. 10 No. 1 (2026): Article Research January 2026
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/sinkron.v10i1.15651

Abstract

Phishing is a significant cybersecurity threat in which attackers exploit manipulated URLs to deceive users and obtain confidential information. As phishing attacks continue to grow in complexity, automated machine learning based detection methods have become essential to strengthen digital security. This study proposes a URL based phishing detection model using boosting algorithms while analyzing the role of feature selection in improving classification performance and computational efficiency. The experiments were conducted on a dataset consisting of 10000 instances with 50 features and balanced class labels. After data preparation, 48 features were retained as input variables, and min max normalization was applied to ensure uniform feature scaling. Three boosting algorithms namely Gradient Boosting, XGBoost, and AdaBoost were evaluated using accuracy, precision, recall, and F1 score. Among these methods, XGBoost achieved the highest accuracy of 98.8 percent, demonstrating its effectiveness in learning complex URL patterns. Subsequently, three feature selection techniques namely Information Gain, Chi Square, and ReliefF were applied and evaluated using 10 fold cross validation. The results indicate that ReliefF provides the most effective feature reduction by selecting 37 features while maintaining the same classification accuracy. Unlike previous studies that mainly focus on classifier comparison, this study demonstrates that integrating XGBoost with ReliefF enables significant feature dimensionality reduction without compromising predictive accuracy. This finding highlights an efficient trade off between detection performance and computational complexity. Overall, the proposed framework offers a robust, efficient, and scalable solution for fast and adaptive phishing detection in modern cybersecurity environments.
Forecasting Hotel Demand with Time Series Prediction Model Using Random Forest Regression Pramita, Dewa Ayu Kadek; Saraswati, Ni Wayan Sumartini; Sandana, I Putu Dedy; Dewi, Dewa Ayu Putu Rasmika; Krismentari, Ni Kadek Bumi
Sinkron : jurnal dan penelitian teknik informatika Vol. 10 No. 1 (2026): Article Research January 2026
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/sinkron.v10i1.15655

Abstract

The tourism sector, as one of the main contributors to national foreign exchange, relies heavily on the growth of the hospitality industry. Improvements in this sector are expected to enhance service quality and strengthen the overall image of tourism. However, the hospitality industry is highly dynamic, with fluctuating room demand influenced by both internal and external factors, creating challenges for accurate demand forecasting. This study develops a hotel demand prediction model using internal variables (occupancy rate, reservations, cancellations, and lead time) and external variables (events and visitor numbers). The Random Forest Regression method was employed, with predictive performance evaluated through a proxy demand index. The dataset was obtained from Adiwana Unagi Suites, Ubud, Bali, covering historical time series data from November 2021 to July 2025 with a total of 18.674 transactions. Evaluation metrics included Mean Absolute Error, Mean Square Error, Root Mean Square Error, and R-squared, applied to each hotel room type. The results demonstrate strong predictive performance, with R-squared values of 99.83% for test data, 99.95% for training data, and 88.24% for three-month prediction data, accompanied by low error values across all metrics. The lower performance in the three-month forecast may be due to the proxy demand index not fully representing actual demand. Overall, the findings highlight the potential of machine learning approaches, particularly Random Forest Regression, to support decision-making in hotel management. The model can serve as a reference for room pricing, allocation, and operational strategies, enabling stakeholders to adapt effectively to fluctuating market demand.
Security Evaluation of Indonesian LLMs for Digital Business Using STAR Prompt Injection Agnes Irene Silitonga; Irwandi, Hafiz; Silitonga, Agnes Irene; Rudy Chandra; Simamora, Windi Saputri
Sinkron : jurnal dan penelitian teknik informatika Vol. 10 No. 1 (2026): Article Research January 2026
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/sinkron.v10i1.15662

Abstract

The adoption of Large Language Models (LLMs) in digital business systems in Indonesia is rapidly increasing; however, systematic security evaluation against Indonesian language prompt injection remains limited. This study introduces the Indonesian Prompt Injection Dataset, consisting of 50 attack scenarios constructed using the STAR framework, which combines structured instruction variations with sociotechnical context to expose potential model vulnerabilities. The dataset was used to evaluate three commercial LLM platforms ChatGPT using a GPT-4 class lightweight variant (OpenAI), Gemini 2.5 Flash (Google), and Claude Sonnet 4.5 (Anthropic) through controlled experiments targeting instruction manipulation in Indonesian. The results reveal distinct robustness profiles across models. Gemini 2.5 Flash exhibits moderate observed resilience, with 76% of scenarios classified as medium risk and 12% as high risk. ChatGPT demonstrates higher observed robustness under the tested scenarios, with 88% of cases classified as low risk and no high-risk outcomes. Claude Sonnet 4.5 shows intermediate observed resilience, with 72% low-risk and 28% medium-risk scenarios. High-risk cases primarily involve direct role override, urgency- or emotion-based prompts, and anti-censorship instructions, while structural ambiguities and multi-intent manipulations tend to result in medium risk, and mildly persuasive prompts fall under low risk. These findings suggest that while contemporary LLM defense mechanisms are effective against explicit attacks, contextual and emotionally framed manipulations continue to pose residual security challenges. This study contributes the first Indonesian-language prompt injection dataset and demonstrates the STAR framework as a practical and standardized approach for evaluating LLM security in digital business applications.
An OWL-Based Ontology Model of Food Production and Distribution in Indonesian Purwanto, Joko; Muin, Muhammad Abdul; Nugroho, Adlan; Muhammad, Kukuh
Sinkron : jurnal dan penelitian teknik informatika Vol. 10 No. 1 (2026): Article Research January 2026
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/sinkron.v10i1.15664

Abstract

Food security in Indonesia is influenced by the dynamics of production, distribution, and availability between regions. However, many existing information systems still rely on conventional data structures without semantic integration, which limits interoperability and hinders interregional analysis. To address this gap, this study developed an ontology model based on the Web Ontology Language (OWL) that formally represents the relationships between food production, commodity characteristics, distribution flows, food insecurity conditions, and geographical context. The ontology was built using Protégé through stages of literature review, official data collection from BPS, FAO, and the Ministry of Agriculture, conceptual model design, implementation, and evaluation. Conceptual validation was conducted through Focus Group Discussions (FGD) with food supply chain experts to ensure the suitability of the ontology structure and the actual conditions of the national food system. The technical evaluation involved consistency testing using the Pellet reasoner and Competency Question (CQ) testing through SPARQL queries to assess the ontology's ability to respond to essential information needs. The resulting ontology consists of five core classes (FoodProduction, FoodItem, FoodDistribution, FoodSecurityStatus, and GeographicRegion) which collectively represent the semantic structure of Indonesia's food supply chain. The evaluation results show that the ontology is structurally consistent and capable of producing outputs that are in line with CQ, including the retrieval of production-distribution information and the initial identification of commodity surpluses and deficits based on instance data. These findings indicate that the developed ontology provides a coherent semantic foundation for modeling food systems and has strong potential to support the development of knowledge-based food security management applications.
Evaluation of MobileNet-Based Deep Features for Yogyakarta Traditional Batik Motif Classification Muhdhor, Umar; Yohannes
Sinkron : jurnal dan penelitian teknik informatika Vol. 10 No. 1 (2026): Article Research January 2026
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/sinkron.v10i1.15668

Abstract

Batik is an Indonesian intangible cultural heritage that embodies profound philosophical, aesthetic, and cultural values. Yogyakarta batik motifs, such as Parang, Kawung, and Truntum, reflect Javanese wisdom and identity through distinctive geometric and floral patterns. In the digital era, artificial intelligence based image processing provides a promising approach to support the preservation and automatic recognition of traditional batik motifs. The objective of this study is to evaluate the effectiveness of MobileNet-based feature extraction combined with Support Vector Machine (SVM) classification for Yogyakarta batik motif recognition. The proposed method employs MobileNet as a convolutional feature extractor and SVM as a decision model to separate motif classes in the feature space. Experiments were conducted on 685 batik images consisting of three motif classes, with class imbalance handled using Synthetic Minority Over-sampling Technique (SMOTE). Model performance was evaluated using weighted accuracy, precision, recall, and F1-score under five-fold cross validation. The results show that MobileNetV3Large achieved the best performance with a weighted accuracy of 98.36%, followed by MobileNetV3Small and MobileNetV4Small. Statistical significance tests using the Friedman test and Wilcoxon signed-rank analysis confirm that the performance differences among the evaluated models are statistically significant. These findings indicate that MobileNetV3 architectures provide robust and discriminative feature representations for batik motif classification on limited yet structured datasets. This study contributes a validated MobileNet–SVM framework for batik recognition and supports ongoing efforts in the digital preservation of Indonesia’s cultural heritage. Future work will explore larger motif sets and cross-dataset evaluation to further improve generalization performance.
Feature-Level Fusion of DenseNet121 and EfficientNetV2 with XGBoost for Multi-Class Retinal Classification Laksana, Jovansa Putra; Yohannes
Sinkron : jurnal dan penelitian teknik informatika Vol. 10 No. 1 (2026): Article Research January 2026
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/sinkron.v10i1.15670

Abstract

Accurate and efficient classification of retinal fundus images plays a critical role in supporting the early diagnosis of ocular diseases. However, models relying on a single deep learning backbone often struggle to capture the multi-scale and heterogeneous characteristics of retinal lesions, leading to unstable performance across visually similar disease classes. To address this limitation, this study proposes a novelty feature-level fusion framework that integrates complementary representations from DenseNet121 and EfficientNetV2-s, followed by classification using XGBoost. The fusion pipeline extracts 1024-dimensional features from DenseNet121 and 1280-dimensional features from EfficientNetV2-s, which are concatenated into a unified 2304-dimensional feature vector. Experiments were conducted on a dataset of 10,247 retinal fundus images spanning six categories: Central Serous Chorioretinopathy, Diabetic Retinopathy, Macular Scar, Retinitis Pigmentosa, Retinal Detachment, and Healthy. The proposed fusion model achieved an accuracy of 91.60%, outperforming DenseNet121 XGBoost (91.31%) and EfficientNetV2-s XGBoost (89.70%). Moreover, the fusion strategy demonstrated improved class-level stability, particularly for visually similar retinal disorders where single-backbone models exhibited higher misclassification rates. This study contributes a lightweight yet effective multi-backbone feature-level fusion approach that enhances discriminative representation and classification stability without increasing model complexity. In addition, the use of XGBoost introduces a tree-based decision mechanism that is inherently more interpretable than conventional fully connected layers, offering potential advantages for clinical analysis. Overall, the results highlight the effectiveness of multi-backbone feature fusion as a reliable strategy for automated retinal disease classification.
IoT-Based Stress Monitoring Using CNN for HRV-GSR Analysis Segono, Yohandres; Hutagalung, Elia Yose Mayal; Simbolon, Harly Gumanti; Us, Nurul Iman; Ridwan, Achmad
Sinkron : jurnal dan penelitian teknik informatika Vol. 10 No. 1 (2026): Article Research January 2026
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/sinkron.v10i1.15671

Abstract

Stress has become a major global health concern affecting both physical and mental well-being. Conventional stress assessment methods rely on subjective self-reports that cannot capture real-time physiological changes. Existing systems are often limited to controlled laboratory environments or depend on traditional machine learning approaches requiring extensive manual feature engineering. This study aims to develop an Internet of Things–based stress monitoring system using deep learning to enable objective, continuous, and practical real-world stress detection.  The system incorporates wearable sensors using an ESP32-DevKit V1 microcontroller, a MAX30102 photoplethysmography sensor, and a Grove-GSR module for real-time acquisition of Heart Rate Variability and Galvanic Skin Response signals. A dual-branch Convolutional Neural Network architecture processes preprocessed HRV and GSR time-series data to automatically learn discriminative features without manual feature engineering. Data were collected from 30 participants, resulting in 8,100 labeled samples across four stress levels. The proposed CNN model achieved 91.3% classification accuracy, outperforming baseline machine learning models such as Support Vector Machine (78.4%), Random Forest (81.7%), and XGBoost (84.3%). Real-time system evaluation demonstrated an average latency of 1.47 seconds and battery endurance exceeding 13 hours, confirming the feasibility of continuous wearable stress monitoring. The integration of IoT infrastructure with deep learning provides an effective framework for physiological stress assessment, offering potential applications in preventive healthcare, workplace health management, and personalized mental-wellness monitoring.
A Comparative Study of MobileNetV2 and ResNet50 for Multi-Class AI- Generated and Real Image Classification Pramudita, I Gusti Ngurah Agus Ega Patria; Sudipa, I Gede Iwan; Fittryani, Yuri Prima; Iswara, Ida Bagus Ary Indra; Aristamy, I Gusti Ayu Agung Mas
Sinkron : jurnal dan penelitian teknik informatika Vol. 10 No. 1 (2026): Article Research January 2026
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/sinkron.v10i1.15682

Abstract

This study aims to classify AI-generated and real images using Convolutional Neural Network (CNN) architecture by comparing the performance of MobileNetV2 and ResNet50. Previous studies on AI-generated image detection have primarily focused on binary classification without explicitly analyzing object-level context in multi-class scenarios, leaving a gap in understanding model performance across diverse visual categories. The dataset consists of 23,941 images divided into two main classes of real and fake and five subclasses of human, animal, art, view, and vehicle. The training process employs data augmentation and a K-Fold Cross Validation strategy on the training and validation set to maintain balanced class proportions, while a separate unseen test set is used exclusively for final performance evaluation. Model evaluation is performed based on accuracy, precision, recall, and F1-score metrics on test data. The results showed that MobileNetV2 achieved the best accuracy of 89% at the 10th epoch, but experienced a decline in performance at the 30th and 50th epochs, indicating overfitting. In contrast, ResNet50 showed the most stable performance with the highest accuracy of 93% at the 30th epoch and consistently high precision, recall, and F1-score values. Thus, ResNet50 was found to be the most effective architecture for classification of AI-generated and real images on multi-class datasets, while MobileNetV2 remains relevant for implementation on devices with computational limitations.

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