The increasing adoption of machine learning in production systems has intensified the need for structured, automated, and reproducible pipelines, commonly modeled as directed acyclic graphs. However, unlike most existing workflow scheduling studies that focus on distributed or multi-node environments, this work addresses the lack of controlled, workload-aware analysis for orchestration strategies specifically in single-node systems. A controlled experimental methodology is applied by executing an identical machine learning pipeline under two orchestration modes: sequential execution using a local orchestrator and parallel execution using a workflow orchestration engine. Two scenarios are evaluated by explicitly controlling task execution duration to represent light and heavy computational workloads. The results show that under light workloads, parallel execution increases the average makespan significantly, yielding a speedup of only 0.71, which indicates performance degradation due to dominant orchestration overhead. In contrast, under heavy workloads, parallel execution reduces the average makespan from 1013 seconds to 532 seconds, achieving a speedup of 1.90. System-level monitoring reveals higher central processing unit utilization during parallel execution, while evaluation metrics, including root mean square error and coefficient of determination, remain stable across all experimental runs. This study contributes empirical evidence of a workload threshold beyond which orchestration overhead becomes negligible and pipeline parallelism becomes beneficial. These findings demonstrate that the performance benefits of parallelism are strictly workload-dependent, highlighting the importance of selecting orchestration strategies based on computational workload characteristics.

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.

Stunting 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 (mAP@0.5) 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.

Smoke detection plays a critical role in preventing fire-related hazards, particularly in intelligent monitoring and early warning systems. Conventional smoke sensors often exhibit limited responsiveness in dynamic environmental conditions, prompting the adoption of IoT-based sensor data combined with machine learning techniques. This study presents a comparative evaluation of four supervised classification algorithms, K-Nearest Neighbors (KNN), Decision Tree, Random Forest, and Gradient Boosting, using the Smoke Detection Dataset from Kaggle. The methodology integrates SMOTE to address class imbalance and Z-score normalization for feature standardization. Hyperparameter tuning was performed using GridSearchCV with 5-fold cross-validation, and model performance was assessed based on accuracy and execution time. Experimental results show that KNN achieved the highest accuracy (98.33%) with the lowest execution time (0.0327 s), whereas Decision Tree recorded the lowest accuracy (84.17%) but remained computationally fast (0.0406 s). Random Forest and Gradient Boosting demonstrated strong predictive capability (97.22% and 96.94%, respectively), but at higher computational costs (1.4338 s and 8.3819 s, respectively). Almost all models achieved perfect scores (1.00) for precision, recall, and F1-score following SMOTE-based balancing, except KNN which obtained slightly lower values (0.99). The findings indicate a trade-off between predictive performance and computational efficiency, suggesting that lightweight models such as KNN are better suited for real-time IoT-based smoke detection. In contrast, ensemble models may be more appropriate for backend analysis. This research contributes an integrated evaluation framework that combines data rebalancing, multi-model benchmarking, and time-based performance analysis, providing practical insights for the development of responsive and scalable early smoke detection systems.

The rapid advancement of information technology has encouraged small and medium-sized enterprises to shift from manual operational procedures to structured digital systems. However, many small printing businesses continue to face delays, data inconsistencies, and limited real-time monitoring due to conventional order management practices. These challenges highlight the need for a more responsive and efficient ordering system capable of improving transaction accuracy and service delivery speed. This study addresses the issue by developing a web-based ordering system using an iterative Agile Scrum approach, followed by a comprehensive performance evaluation through simulated concurrent user testing. The results show a substantial improvement in system responsiveness, with user data retrieval time decreasing from 11,228 ms to 2,148 ms (an 80.9% improvement) and order processing time reduced from 16,954 ms to 4,697 ms (a 72.3% improvement), resulting in an overall average efficiency gain of 76.6%. The integration of Redis caching significantly enhances system performance, stability, and load distribution, addressing the current gap in Redis implementation for small-scale printing environments. This study demonstrates that adopting a hybrid data-handling architecture can provide a scalable, reliable, and high-performance solution for digital ordering processes, enabling small enterprises to improve operational efficiency and customer satisfaction.

PDF template data extraction remains a substantial challenge due to semi-structured document formats and variations. While large pre-trained models achieve high accuracy, they require extensive computational resources and labeled datasets, making them impractical for resource-constrained environments. Conversely, rule-based approaches are efficient but rigid. This research addresses this gap by developing an adaptive learning system that integrates rule-based approaches with Conditional Random Fields (CRF) in a hybrid framework, designed for data-scarce scenarios. The system implements parallel extraction strategies with confidence-based selection and Human-in-the-Loop (HITL) feedback for incremental learning. Pattern learning updates rule-based strategies, while CRF models are retrained incrementally. Evaluated on synthetically generated documents across diverse template types, the system achieves 98.61% accuracy with minimal training data and 7% user correction rate, demonstrating high learning efficiency (1.88 corrections per percentage point). The improvement is statistically significant (paired t-test, p < 0.001, Cohen’s d = 8.95). The system operates on CPU-only hardware with 50-100 MB footprint and 0.1-0.5 seconds processing time. This work fills a practical gap in document extraction, providing a middle-ground solution balancing high accuracy, minimal data requirements, low resource consumption, and real-time adaptability—suitable for small organizations and rapid deployment where large models are impractical. The evaluation uses synthetic data to ensure reproducibility and controlled assessment, though real-world validation would strengthen practical applicability.

Child 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.

Forest fires represent one of the most severe environmental disasters, causing extensive ecological, social, and economic damage—particularly in tropical nations like Indonesia. This research introduces a hybrid framework that combines Blockchain and Neural Network technologies to classify forest fire images. The goal is not only to enhance detection precision but also to guarantee the integrity and security of experimental data. Two deep learning architectures, ResNet-50 and VGG-16, were implemented and evaluated to compare their effectiveness in differentiating fire from non-fire imagery. The dataset merges locally collected images from the Puncak area of Bogor, Indonesia, with the public FIRE dataset from Kaggle, thereby increasing model generalization. Model training utilized a transfer learning strategy, and its performance was assessed through four key indicators: accuracy, precision, recall, and F1-score. The findings demonstrate that VGG-16 achieved the most reliable outcomes, obtaining an accuracy of 0.91 and an F1-score of 0.87, outperforming ResNet-50, which reached 0.82 accuracy. All experimental data, including training and inference outputs, were stored using the InterPlanetary File System (IPFS), while each file’s Content Identifier (CID) and metadata were recorded in a blockchain-based smart contract to ensure transparency, verifiability, and data permanence. The study concludes that integrating blockchain with deep learning establishes a trustworthy and tamper-resistant framework for forest fire image classification. Future research may explore lighter CNN models and the fusion of IoT sensor data to enable adaptive and real-time fire detection.

This paper presents a comprehensive comparative analysis of Software Development Life Cycle (SDLC) and Research and Development (R&D) methodologies in system development, with a specific focus on their application to the Integrity Zone Management Information System. Through a systematic literature review and an in-depth case study analysis, this research examines the fundamental differences, strengths, and limitations of each methodology. The study identifies key dimensions for comparison including flexibility, risk management, innovation potential, documentation requirements, and stakeholder engagement. Findings reveal that while SDLC methodologies provide structure and predictability for well-defined requirements, R&D approaches offer greater innovation capacity for exploratory projects. The Integrity Zone Management Information System case demonstrates how hybrid approaches can leverage the strengths of both methodologies and improved stakeholder satisfaction by 94%. This research contributes to the theoretical understanding of system development methodologies and provides practical guidance for selecting appropriate approaches based on project context, objectives, and constraints. The paper concludes with recommendations for practitioners and suggestions for future research in methodological integration and adaptation.

Retrofitting community buildings is a key pathway toward carbon neutrality, yet most existing retrofit planning models lack adaptability to the diverse urban contexts of the Global South, where building typologies are heterogeneous and resources limited. Addressing this gap requires approaches that are both computationally efficient and context-sensitive. This study introduces a hybrid optimization framework that integrates Genetic Algorithm (GA) and Mixed-Integer Linear Programming (MILP) to tackle the multidimensional multiple-choice knapsack problem inherent in retrofit planning. The GA explores high-level system configurations, while MILP ensures precise component-level selection under budget and technical constraints. Compared to conventional single-method approaches, the hybrid GA–MILP achieves near-optimal emission reduction with reduced computation time and greater feasibility, offering a balanced trade-off between performance and scalability. Importantly, the framework demonstrates that medium-cost retrofit strategies provide the most cost-effective path to scalable carbon savings, making it highly relevant for resource-constrained urban environments. By situating retrofit planning within the realities of the Global South, this study advances methodological innovation and provides a robust decision-support tool aligned with sustainable development goals for inclusive and low-carbon urban futures.