Sinkron : jurnal dan penelitian teknik informatika
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Blockchain and SVM Integration for Distributed DDoS Attack Detection
No full textRapid developments in information technology have increased dependence on network services, but have also triggered an increase in cyber threats such as Distributed Denial of Service (DDoS). These attacks can paralyze systems by flooding servers with simultaneous fake traffic. Conventional rule-based detection methods are now less effective in dealing with dynamic attack patterns, requiring an adaptive approach based on machine learning. This research develops a Support Vector Machine (SVM) model enhanced with Blockchain technology to improve accuracy and data security in detecting DDoS attacks. The dataset used is CICDDoS2023 from the Canadian Institute for Cybersecurity, which contains various variants of modern DDoS attacks. The research stages include data pre-processing, training the SVM model using the RBF kernel, and integrating Blockchain with training data hash recording through a smart contract using Remix Ethereum to ensure data integrity. Performance evaluation was carried out using accuracy, precision, recall, and F1-score metrics based on the confusion matrix results. The integration of SVM and Blockchain showed an increase in security and detection accuracy compared to conventional SVM models. This approach not only improves the reliability of the DDoS attack detection system, but also creates a transparent and tamper-proof data validation mechanism. The research results are expected to contribute to the development of adaptive, decentralized network security systems with a high level of confidence in attack detection results
Causal Analysis of Stunting Determinants Using the Peter-Clark and Greedy Equivalence Search Algorithms
No full textChild stunting remains a major public health challenge, reflecting the long-term effects of inadequate nutrition, limited maternal education, and restricted access to health services. However, most existing studies rely on correlational analysis, leaving the underlying causal mechanisms insufficiently understood. This gap limits the development of effective interventions, as policymakers require evidence on how determinants interact causally. To address this issue, this study applies two causal discovery algorithms Greedy Equivalence Search (GES) and Peter Clark (PC) to identify causal relationships among eight key determinants of stunting using secondary data from the West Bangka District Health Office (2024). The variables include anthropometric indicators, maternal characteristics, and environmental conditions. Causal assumptions such as causal sufficiency, acyclicity, and faithfulness were imposed to ensure identifiability of the resulting graphs. Model performance was evaluated using Directed Density (DD) and Causal Density (CD) metrics. GES generated a parsimonious causal structure highlighting maternal education, posyandu visits, and exclusive breastfeeding as dominant causal candidates affecting height-for-age (TB/U) and weight-for-age (BB/U). In contrast, the PC algorithm produced a more complete and dense structure, achieving DD = 1.0 and CD = 0.12, compared with GES (DD = 0.80; CD = 0.10). These results indicate that PC is more exploratory in mapping complex causal interactions, while GES offers a simpler and more conservative model. Collectively, the findings demonstrate that combining score-based and constraint-based discovery approaches yields complementary insights into the mechanisms driving stunting
Comparison of IndoBERT and SVM Performance in Sentiment Analysis of Digital Education Platforms
No full textSentiment analysis on user-generated reviews is essential for understanding the quality and effectiveness of digital education platforms. This study compares the performance of Support Vector Machine (SVM) and IndoBERT in classifying sentiments from Ruangguru user reviews. The original dataset contains 111,838 reviews, from which a stratified sample of 10,000 entries was selected for experimentation to maintain class proportion. Text preprocessing applied standard/light normalization (case folding and light cleaning, handling URLs/users/hashtags and repetition) without stopword removal to preserve polarity cues. Auto labels are validated on 139 manually annotated samples (accuracy 0.763, Cohen’s κ 0.644), indicating reliable yet imperfect alignment. To ensure a fair, leakage-safe comparison, we use a fixed 20% standard test split for all models; within the remaining data, 10% is used for validation, and IndoBERT checkpoints are selected based on validation macro-F1 (early stopping). The SVM baseline combines word- and character-level TF-IDF with class-balanced LinearSVC and grid search, achieving accuracy 0.888 and macro-F1 0.543, strong on positives but limited for the neutral class. IndoBERT yields more balanced performance: the class-weighted variant attains the best macro-F1 0.601 (accuracy 0.857), while the baseline reaches the highest IndoBERT accuracy (0.867) with macro-F1 0.596. These results show that Transformer models provide a more balanced trade-off under severe imbalance, whereas SVM remains a competitive accuracy-oriented baseline. In practice, platforms should prioritize macro-F1, use optimized IndoBERT when minority opinions matter, and invest in expanded manual labeling and advanced imbalance handling to improve neutral detection further
Comparison of XGBoost and Naive Bayes Models in Type 2 Diabetes Prediction with RFE Feature Selection
No full textType 2 diabetes mellitus is a chronic disease with an increasing prevalence rate that can cause serious complications if not detected early. The application of machine learning algorithms can aid prediction, but selecting the right model and features greatly determines the accuracy of the results. This study aims to compare the performance of the Extreme Gradient Boosting (XGBoost) and Naive Bayes algorithms in predicting type 2 diabetes with and without Recursive Feature Elimination (RFE) feature selection. The data used were from the UCI Machine Learning Repository, comprising 768 samples and eight clinical features. The research process included data preprocessing, dividing the data into 614 training data and 154 testing data, applying RFE to select the most influential features, model training, and evaluation using accuracy, precision, recall, F1-score, and AUC. The results show that Naive Bayes without RFE achieves 70.77% accuracy, 0.57377 precision, 0.648148 recall, F1-score 0.608696, and 0.772778 AUC, while Naive Bayes with RFE increases the accuracy to 74.02% and the AUC to 0.793333. Meanwhile, XGBoost with RFE provided the best results with an accuracy of 74.67%, precision of 0.653061, recall of 0.592593, F1-score of 0.621359, and the highest AUC of 0.804259. Besides, applying RFE also improves the computational efficiency. These findings indicate that applying RFE significantly improves classification and computation time performance. The practical implication is that this model could aid early detection of diabetes in clinical settings. Further research can be conducted by optimizing parameters and using more diverse datasets
Machine Learning Analysis of Jakarta Bay Water Quality: Comparing Models
No full textJakarta Bay experiences persistent anthropogenic pressures that produce spatially heterogeneous water-quality conditions. This study develops a regulation-aligned, explainable classification framework using a 2024 in-situ dataset collected at 53 stations across two sampling periods (March and August). After preprocessing—including unit harmonization, outlier screening, missing-value imputation, and treatment of below-detection-limit measurements—the dataset yielded 104 complete samples classified into Good (n=46), Lightly Polluted (n=28), and Moderately Polluted (n=34) categories based on KEPMEN LH No. 51/2004. Three ensemble algorithms (LightGBM, CatBoost, and Random Forest) were evaluated using stratified cross-validation to maintain class balance and prevent spatial leakage. CatBoost achieved the best overall performance (Accuracy = 0.8338; F1 = 0.8257), followed by Random Forest, while LightGBM showed the highest variability across folds. Class-level metrics indicate that CatBoost produced the most balanced predictions, particularly for the borderline Lightly Polluted class. SHAP analysis identified turbidity/TSS, nutrients, dissolved oxygen, salinity, and spatial gradients as dominant predictors, enabling transparent interpretation of model decisions. The resulting framework provides a reproducible and operationally deployable approach for rapid screening, hotspot detection, and decision support in Jakarta Bay’s water-quality management
Enhanced Performance Evaluation of VGG16 and ResNet50 for Deepfake Detection Using Local Ternary Pattern
No full textDeepfake video generation has become increasingly sophisticated, posing challenges for detection methods that rely solely on convolutional neural networks (CNNs without explicit texture enhancement). Many existing approaches have limited robustness in capturing subtle texture inconsistencies caused by manipulation, compression, and noise. This study investigates the integration of Local Ternary Pattern (LTP)–based texture enhancement with transfer learning models for deepfake video detection. Specifically, VGG16 and ResNet50 architectures are evaluated using the Celeb-DF (v2) dataset. LTP is employed to extract fine-grained texture features due to its higher robustness to illumination variations and noise compared to conventional descriptors such as Local Binary Pattern (LBP). Video frames are processed individually and used to train CNN classifiers, followed by evaluation at both frame and video levels. Experimental results show that ResNet50 outperforms VGG16, achieving a test accuracy of 93% with a validation loss of 0.2228, while VGG16 reaches an accuracy of 88% with a validation loss of 0.2636. Further testing on 20 withheld videos demonstrates that ResNet50 correctly classifies all samples, whereas VGG16 misclassifies two real videos, indicating lower robustness to real-video misclassification. These results demonstrate that LTP-based texture enhancement effectively supports CNN-based deepfake detection and that deeper architectures benefit more from enriched texture representations. This study provides empirical insights into improving robustness and reliability in deepfake video classification
Improving Machine-Learning Malware Detection Through IQR-Based Feature Reduction
No full textMalware detection is a significant challenge in cybersecurity due to the complex and evolving nature of threats. This study evaluates the effectiveness of machine learning algorithms, specifically XGBoost and LightGBM, in detecting malware. The approach includes data cleaning, normalization, feature selection, and the use of the Interquartile Range (IQR) technique to select relevant features. The initial dataset contained 21,752 files, evenly split between malicious and benign files. After data cleaning, the number of samples decreased to 19,256 files, with numerous features that were reduced after applying IQR. Results show that XGBoost outperforms other algorithms, achieving 99.20% accuracy, an improvement over the 98.99% accuracy without IQR. The IQR technique enhances data quality by filtering out features with significant differences between malware and benign files, improving model performance. Additionally, reducing the feature set helps prevent overfitting and strengthens the model's generalization ability. The study concludes that machine learning, particularly with algorithms like XGBoost and LightGBM, can effectively improve malware detection. By using IQR in feature selection, model performance is enhanced, leading to reduced false positives and increased detection efficiency. The research highlights the importance of feature selection techniques like IQR in boosting the predictive power of machine learning models, making them more efficient in identifying malware. Future work will explore additional feature selection methods to further improve malware detection accuracy
CataractAsist: Convolutional Neural Network-Based Early Detection System for Cataracts
No full textCataract disease is one of the leading causes of blindness worldwide, especially in developing countries with limited access to healthcare facilities. To address this challenge, this study aims to develop an automated cataract detection system using the Convolutional Neural Network (CNN) method. This system is designed to classify eye images into three classes, namely Normal, Mature, and Immature, by utilizing the "Senile Cataract" dataset from the Kaggle platform. The research methods include image pre-processing, feature extraction using the VGG16 model through transfer learning, model training with augmentation techniques, and performance evaluation using accuracy, precision, recall, f1-score, and confusion matrix metrics. The test results show that the model is capable of achieving 95% accuracy, with the highest f1-score in the Normal class at 0.96. Confusion matrix analysis shows excellent prediction rates for all classes, although there are slight classification errors between the Immature and Mature classes. In conclusion, this CNN-based cataract detection system is proven to be effective and accurate, and has great potential to be applied in web-based healthcare services as an automatic early diagnosis tool for eye diseases
Hybrid Machine Learning Predictive Model for Resource Allocation Optimization and Project Risk Management
No full textIT project management faces critical challenges related to inaccurate resource allocation estimation and project risk assessment, which complicates decision-making and threatens project performance. Although machine learning techniques have been widely adopted in this domain, existing studies predominantly rely on single models or simple ensemble strategies, limiting their ability to capture heterogeneous interactions among organizational, technical, and risk-related factors. This study proposes a hybrid machine learning–based decision support framework that integrates feature-level representation learning and probabilistic decision fusion. Gradient Boosting is reconceptualized as a feature selection and nonlinear interaction modeling mechanism, while Artificial Neural Networks generate latent feature embeddings representing complex project characteristics. These representations are fused through a Naive Bayes classifier to produce calibrated probabilistic predictions, supported by a weighted fusion strategy with F1-score–based threshold optimization to improve stability under imbalanced risk conditions. Experimental evaluation is conducted using 5,997 synthetic IT project records from PT Anugerah Nusa Teknologi. Model performance is evaluated using accuracy, precision, recall, F1-score, and ROC-AUC metrics. Compared to standalone Gradient Boosting, Artificial Neural Network, and Naive Bayes models, the proposed hybrid framework consistently demonstrates superior predictive performance, achieving an accuracy of 0.85, an F1-score of 0.8485, and a ROC-AUC of 0.9050. Theoretically, this study contributes to project management research by demonstrating that IT project outcomes are more effectively modeled through multi-perspective learning rather than isolated predictors. Practically, the proposed framework provides actionable decision support to assist project managers in optimizing resource allocation and prioritizing risk mitigation under uncertainty
Enhanced Stacked GRU Model for Monthly Rice Production Forecasting in Bali Province
No full textRice production has a seasonal pattern that depends on the planting cycle and environmental conditions, requiring forecasting methods that can accurately model temporal dynamics. This study aims to predict monthly rice production in Bali Province using the Stacked Gated Recurrent Unit (GRU) architecture. Monthly rice production data from 2018 to 2024 from the Central Statistics Agency (BPS) was used as the main source. The preprocessing stage includes data cleaning, Min-Max normalization, and feature engineering in the form of creating sin_month and cos_month features to capture seasonal patterns, as well as a 3-month rolling mean to extract short-term trends. The proposed stacked design with dual-layer GRU combined with seasonal features improves temporal pattern extraction compared to single-layer GRU baselines. The model was tested using three configurations, and Scheme 3 provided the best performance with an MAE value of 1610.21, an RMSE of 2055.90, and a MAPE of 14.29%, which is considered good accuracy. The model was able to follow seasonal production trends, including an increase at the beginning of the year and a decrease during the planting period. Long-term predictions for the next 12 months and quarterly forecasts per district/city also showed patterns consistent with historical data. The results of the study indicate that Stacked GRU is effective in forecasting seasonal rice production and can be used as a basis for decision support in food security planning in Bali