Sinkron : jurnal dan penelitian teknik informatika
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Implementation of YOLOv11 for Food Detection to Support Nutritional Information in Stunting Prevention
No full textStunting remains a persistent public health challenge in Indonesia, mainly due to chronic malnutrition and limited parental literacy regarding balanced diets. To address this issue, this study developed an integrated nutrition education system using YOLOv11 and Generative AI, structured based on the ADDIE framework. This system aims to bridge the literacy gap by automating food identification and transforming technical nutritional data into easy-to-understand insights for stunting prevention. The study used a dataset of 2,413 images, which was expanded to 4,687 through augmentation. Technical evaluation showed strong performance with a Mean Average Precision ([email protected]) of 97%, ensuring reliable detection of important protein sources such as eggs. In addition to accuracy, the system applies a heuristic nutritional assessment algorithm visualized through a ‘Traffic Light’ system to reduce the cognitive load on users. Qualitative evaluation with posyandu cadres showed a significant increase in nutritional understanding, with 90% of users able to explain appropriate dietary interventions based on AI recommendations. These results conclude that the integration of computer vision with structured educational design effectively transforms mobile devices into real-time decision support systems for stunting prevention initiatives at the community level
User Satisfaction in Moo Opinion App: Machine Learning for Cooperative Segmentation
No full textThis study addresses the critical need to understand digital application user satisfaction within the agricultural cooperative sector, specifically for the Moo Opinion application at the Village Unit Dairy Cooperative (KUD). The study's primary novelty lies in the implementation of an integrated, sequential Machine Learning framework—combining Random Forest (RF), Principal Component Analysis (PCA), and K-Means Clustering—to provide a granular analysis of user behavior in a specialized dairy ecosystem. The methodology first utilized RF for key feature selection, followed by PCA for dimensionality reduction, and K-Means for precise user segmentation. Primary data was collected from 40 respondents (20 farmers, 20 customers). Key findings reveal that Service Quality (0.42) and Milk Quality (0.36) are the most significant drivers of satisfaction, considerably outweighing economic factors like Milk Price (0.08). PCA identified two core satisfaction dimensions: Quality-Service Synergy (explaining 56.7% variance) and Structural-Economic Factors (explaining 25.7% variance), confirming the dominance of non-economic aspects. K-Means Clustering successfully identified three segments: Highly Satisfied (45%), Moderately Satisfied (38%), and Low Satisfaction (17%), with high cluster validity (Silhouette Coefficient 0.71). A recognized limitation of this study is the small sample size (N=40), which may affect the generalizability of the findings to larger cooperative populations. However, the results offer significant practical implications, highlighting the need for KUD to prioritize digital service quality and product value over pricing strategies to enhance loyalty and prevent churn
Evaluation of MobileNet-Based Deep Features for Yogyakarta Traditional Batik Motif Classification
No full textBatik 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
Analysis of Factors Causing Toddler’s Malnutrition in Medan City Using the Random Forest Method
No full textMalnutrition and severe malnutrition in toddlers remain critical public health concerns that impair physical growth, cognitive development, and long-term productivity. Deficiencies in essential nutrients increase the risks of stunting, weakened immunity, and developmental delays. Although interventions such as supplementation and routine anthropometric monitoring are implemented, comprehensive identification of multidimensional causal factors is still limited, reducing the effectiveness of targeted policies. This study aims to predict toddler nutritional status using a quantitative data mining approach. A dataset consisting of 328 samples and 17 features was collected from health facilities in Medan City, including Puskesmas, the Health Office, and Posyandu. A Random Forest Classifier was developed with missing-value handling, feature engineering, and feature importance analysis to identify dominant predictors of nutritional outcomes. The model achieved an overall accuracy of 92.42 percent and showed strong performance in identifying the “Normal” class, although predictive sensitivity for minority classes such as “Gizi Kurang” and “Gizi Buruk” remained comparatively lower. Feature importance analysis indicated that complete immunization and health insurance ownership were the most influential determinants of nutritional status. This research provides a machine learning–based tool for early nutritional risk prediction and offers data-driven insights to support more precise malnutrition interventions. Future enhancement may include expanding feature diversity and applying advanced interpretability techniques to strengthen model reliability. The findings reinforce the importance of evidence-based nutrition policy strategies that prioritize early prevention and improved child health outcomes
SVM-Based Pediatric Disease Classification Model from the Balinese Lontar Usada Rare Manuscript
No full textLontar Usada Rare is a traditional Balinese manuscript containing pediatric medical knowledge based on local wisdom, yet its narrative format limits accessibility and utilization in modern contexts, while its physical fragility threatens long-term preservation. This study aims to develop a pediatric disease classification model using a Support Vector Machine (SVM) combined with Term Frequency–Inverse Document Frequency (TF-IDF) weighting to support the digitalization of Balinese traditional medicine. A total of 422 data samples were collected through expert interviews and manuscript analysis, covering symptoms, disease types, herbal ingredients, and treatment procedures. The research stages included text preprocessing (cleansing, tokenizing, stopword removal, stemming), manual labeling into 35 disease classes, and model evaluation using five train–test split ratios (80:20 to 60:40) with variations of the complexity parameter C (0.5, 1, 10, 100, 1000). The best performance was achieved using C=10 with an 80:20 ratio, resulting in 87.06% accuracy, 91.55% precision, 87.06% recall, and an F1-score of 87.96%. Confusion matrix analysis showed strong classification performance for most classes, although minority classes with overlapping symptoms exhibited misclassification. Overall, the TF-IDF and linear SVM combination effectively classifies pediatric disease symptoms from Lontar Usada Rare and contributes to the preservation and digital transformation of Balinese traditional medical knowledge for potential modern healthcare applications.
Blockchain Disaster-Relief DApps with SVM and Data Anchors for Fraud-Prevention
No full textVoucherAid and DataAnchor are prototype DApps for disaster-relief voucher processing that integrate on-chain rule enforcement, cryptographic data anchoring through fixed-size hash commitments, and an off-chain SVM-based analytics gateway. VoucherAid issues non-transferable vouchers, restricts redemption to certified merchants, and emits auditable events, while DataAnchor records time-stamped digests to support provenance verification without exposing sensitive content. A 200-record dataset was generated from on-chain logs and enriched with behavioral–temporal features derived from redemption activity. Experiments conducted in a single-node Ganache environment using a 70:30 split show that the SVM achieves 0.75 accuracy with perfect precision but limited recall for fraud (1.00 precision, 0.32 recall, 0.48 F1), indicating that the model cannot serve as a reliable stand-alone detector and is more appropriate as a conservative decision-support tool under human oversight. The prototype demonstrates that separating on-chain enforcement from off-chain analytics can enhance auditability and support model evolution without contract redeployment. However, the findings remain constrained by the small, partially synthetic dataset, the single-node evaluation environment, and programmatic labeling. Future work will expand datasets, incorporate richer temporal and graph-based features, adjust thresholds and class weights, and evaluate the system on multi-node networks to improve fraud recall while maintaining usability and inclusion
Adoption of Artificial Intelligence in Vocational High Schools: A Systematic Review of Teachers’ Perspectives
No full textThe purpose of this study is to examine Vocational High School teachers' perceptions of the adoption of artificial intelligence (AI) in vocational education. Using a systematic literature review (SLR) approach with the PRISMA 2020 protocol, 1,346 articles were identified from six main databases: ACM, IEEE Xplore, ScienceDirect, Google Scholar, and Semantic Scholar. Of the 1,346 articles identified, 263 were excluded at an early stage because they did not meet basic criteria, such as authorship, publication language, and document type. A total of 1,083 articles were screened, yielding 208 reports for in-depth screening. After the screening and feasibility assessment, 29 studies were included in the final analysis. The analysis showed that 72% of studies reported positive results, 24% reported moderate results, and 3% reported exploratory results. The dominant factors influencing teachers' perceptions included infrastructure readiness, digital competence, institutional support, the relevance of the vocational curriculum, and ethical and privacy issues. These findings emphasize the need for a holistic strategy for implementing AI in vocational schools that encompasses teacher training, education policy, and ethical considerations
Comparative Analysis of XGBoost, KNN, and SVM Algorithms for Heart Disease Prediction Using SMOTE-Tomek Balancing
No full textHeart disease remains one of the leading causes of death worldwide, making early detection crucial for improving patient outcomes. This study aims to evaluate and compare the performance of several machine learning algorithms in detecting heart disease using the 2015 BRFSS dataset, which includes responses from 253,680 individuals. The three algorithms examined are Extreme Gradient Boosting (XGBoost), K-Nearest Neighbors (KNN), and Support Vector Machine (SVM). The data preprocessing steps involved feature encoding, class imbalance handling using the Synthetic Minority Over-sampling Technique combined with Tomek Links (SMOTE-Tomek), and hyperparameter tuning through RandomizedSearchCV. The models were assessed on a hold-out validation set using several metrics, including accuracy, Receiver Operating Characteristic-Area Under the Curve (ROC-AUC), F1-score, precision, and recall. The results demonstrated that XGBoost achieved the highest performance, with an accuracy of 94%, a ROC-AUC score of 0.98, and an F1-score of 0.94. In comparison, KNN achieved an accuracy of 87% (ROC-AUC 0.95), while SVM attained an accuracy of 79% (ROC-AUC 0.86). These findings suggest that XGBoost is a robust model for large-scale heart disease classification and holds potential for implementation in clinical decision support systems
Comparing XGBoost and LightGBM for Optimizing Health Content Categories
No full textIndonesia’s social media platforms contain large amounts of unverified health information. Research on Indonesian health-text mining still rarely focuses on disease-based classification, leaving a gap compared with studies that only address sentiment or general topic categorization. This study proposes a multi-class classification approach that uses IndoBERT embeddings combined with gradient-boosting classifiers (XGBoost and LightGBM) to categorize tweets into diabetes, hypertension, and heart disease. The dataset comprises 4,075 tweets collected from platform X (Twitter). Preprocessing involves text cleaning, anonymization, normalization, and the extraction of 768-dimensional IndoBERT embeddings. Experiments are conducted in Google Colab (Intel Xeon CPU, 13 GB RAM, optional NVIDIA T4 GPU) using stratified five-fold cross-validation.The best results are obtained by the IndoBERT × LightGBM pipeline, which achieves an accuracy of 0.8526 and a macro-averaged F1-score of 0.8527, outperforming the IndoBERT × XGBoost model (accuracy 0.8325 and macro F1-score 0.8326). Feature-importance analysis shows that contextual terms related to blood sugar, the heart, and blood pressure strongly influence the predictions. Overall, the proposed method provides an effective baseline for monitoring health-related text and supporting disease-oriented analytics in Indonesian-language social media
Explainable Machine Learning-Based Decision Tree Model for Early Detection of Hypertension Risk
No full textHypertension is one of the leading causes of cardiovascular disease and is often referred to as a “silent killer” because it typically remains asymptomatic until serious complications, such as stroke or kidney failure, occur. Early detection of hypertension risk is therefore essential to enable timely intervention and prevention. This study aims to develop an explainable machine learning–based Decision Tree model for early detection of hypertension risk using clinical and lifestyle data. The balanced dataset includes variables such as age, body mass index (BMI), blood pressure, family history, smoking habits, stress levels, and sleep duration. The dataset used in this study was obtained from the “Hypertension Risk Prediction Dataset” available on the Kaggle platform, consisting of 1,985 patient records and 11 main features covering variables such as age, body mass index (BMI), systolic and diastolic blood pressure, family history, smoking habits, stress level, physical activity, and sleep duration. The dataset is balanced between the hypertension and normal categories, enhancing the reliability of the classification results. The model was constructed using a Decision Tree Classifier implemented in Scikit-learn and validated through cross-validation to minimize overfitting. Model performance was assessed using accuracy, precision, recall, F1-score, and AUC-ROC metrics. The results indicate that the model achieved an accuracy of 96% and an AUC of 0.9645, demonstrating excellent classification performance. The motivation behind this research lies in the growing need for interpretable artificial intelligence models in healthcare, where transparency and explainability are critical for clinical trust and ethical decision-making. Unlike black-box models, the Decision Tree approach allows clinicians to trace each prediction path, understand contributing variables, and apply insights in real-world medical settings. The primary advantage of this model lies in its transparency, as each prediction can be interpreted through explicit decision rules. Overall, this explainable and high-performing model shows strong potential as a clinical decision support tool for early hypertension screening and prevention programs