Sinkron : jurnal dan penelitian teknik informatika
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Earthquake Detection IoT Prototype with Early Warning System Based on Vibration Sensor
No full textThis research developed a prototype of an earthquake detector using a vibration sensor, integrated with a real-time early warning system through a buzzer and LCD display. The system is designed to detect vibrations that potentially indicate an earthquake in real time and to promptly provide alerts to the surrounding environment. The research followed a Research and Development (R&D) method using the waterfall model, which includes requirements analysis, system design, implementation, testing, and maintenance. The hardware components include Arduino Uno, a SW-420 vibration sensor, a buzzer, and an LCD, while the software utilizes Arduino IDE for coding. The test results show that the system successfully detects vibrations and displays real-time notifications, proving its effectiveness in early earthquake warning scenarios. This system is expected to support disaster mitigation efforts by offering a simple and affordable solution that can be implemented in vulnerable areas
Comparison of WSM and Weight Product Methods with WSM-Score and Vector Approaches
No full textFertilizers are essential in modern agriculture as they supply vital nutrients to plants, enhancing growth and yield. However, selecting the most appropriate fertilizer involves multiple criteria and a diverse range of available options. This study conducts a comparative analysis of two Multi-Criteria Decision-Making (MCDM) methods: the Weighted Sum Model (WSM) and the Weight Product (WP) method, supplemented by WSM-Score and vector-based approaches. The evaluation is based on four criteria price, quality, ease of availability, and fertilizer form across seven alternatives: Urea, Compost, TSP, KCL, Gandasil, NPK, and ZA. Using normalized weights from expert judgment, both methods were used to rank the alternatives. A key contribution of this study is the integration of WSM-Score and vector approaches, which enhance traditional MCDM by improving score comparability (WSM-Score) and enabling geometric interpretation of alternative positioning (vector). Results show that Compost (A2) ranks highest across all methods, indicating convergence despite differences in computational logic. WSM offers ease of interpretation, while WP better accounts for proportional differences but is more sensitive to low-performing criteria. The findings suggest that method selection should be context-dependent. Although the ranking results are consistent, the absence of empirical validation through expert comparison or field data limits the generalizability of the conclusions. Further research should include such validation to strengthen the reliability of MCDM-based decision support systems in agricultural applications
Comparative Performance of Yolov8 and Ssd-mobilenet Algorithms for Road Damage Detection in Mobile Applications
No full textRoad damage is a serious issue that can impede traffic and increase the risk of accidents in any area. Fast and accurate detection and classification of road damage are crucial for efficient maintenance and repair. Considering the ease of access, the implementation of this detection can be done using a mobile application. This study aims to compare the performance of two object detection algorithms, YOLOv8 and SSD-MobileNet, in detecting and classifying road damage in mobile application. Evaluation is conducted using accuracy, speed, and memory utilization, and classification of road damage into six categories namely block cracks, alligator cracks, transverse cracks, edge cracks, patches, and potholes using a confusion matrix. The results show that YOLOv8 has an overall accuracy of 86.4%, a speed of 0.5 ms, and consumes 0.41 GB of RAM. SSD-MobileNet shows an overall accuracy of 91.1%, speed 0.7 ms, and consumes 0.14 GB of RAM. The comparison indicates that YOLOv8 excels in detection speed, while SSD-MobileNet is more higher accuracy and efficient in memory. This study is limited to a performance measurement approach for YOLOv8 and SSD-MobileNet algorithms in a mobile-based road defect detection context. Its contribution lies in the trade-off between accuracy, speed, and the memory required to implement the models in limited devices. In future research is recommended to explore model with pruning to reduce memory usage
Lightweight YOLO Models for Real-Time Multi-Vehicle Detection
No full textThis study presents a comparative evaluation of three lightweight YOLO architectures: YOLOv5n, YOLOv8n, and YOLOv11n, for multi-class vehicle detection using CCTV imagery captured under dense traffic conditions in Semarang, Indonesia. The models were tested on their ability to detect four types of vehicles, including motorcycle, car, bus, and truck. To enhance generalization across different lighting conditions, image qualities, and environmental noise, six data augmentation techniques were applied during training. These included Blur, Brightness Adjustment, Color Jitter, Noise Injection, Scaling, and Zoom In. Among these, the Blur technique yielded the most significant improvement in detection accuracy. YOLOv8n with Blur augmentation achieved the best performance with a precision of 0.875, recall of 0.655, [email protected] of 0.756, and [email protected]:0.95 of 0.467. Class-wise analysis showed that buses and trucks were easier to detect due to their larger size and distinct features, while motorcycles were the most difficult due to their smaller dimensions and visual similarity to other objects. Training curves demonstrated consistent decreases in loss values and progressive improvements in evaluation metrics across 60 epochs. These findings emphasize the importance of selecting appropriate model architecture and augmentation strategies to improve object detection performance, particularly in real-time and resource-limited applications. YOLOv8n with Blur augmentation proved to be the most effective configuration in this study
Comparative Performance Analysis of Decision Tree And SVM Algorithms in Detecting Multiple System Atrophy Based on Clinical Features
No full textMultiple System Atrophy (MSA) is a progressive neurodegenerative disorder that presents significant challenges in early and accurate diagnosis. Advances in machine learning algorithms offer promising solutions for improving diagnostic support in medical fields, particularly in complex disorders such as MSA. This study compares the performance of two widely used classification algorithms Decision Tree (DT) and Support Vector Machine (SVM) in detecting MSA using clinical datasets consisting of 300 patient records. Supervised learning techniques with cross-validation were employed, and key performance metrics including accuracy, precision, recall, and F1-score were evaluated. SVM achieved an accuracy of 88.1% and F1-score of 87.1%, outperforming Decision Tree, which recorded 85.4% accuracy and an F1-score of 83.9%. The novelty of this study lies in its direct comparative benchmark using standardized clinical features for MSA detection, offering practical insights into model selection for neurodegenerative disease screening. The SVM model’s superior performance indicates its suitability for reliable early detection of MSA from clinical data. This research contributes to the development of machine learning-based decision support tools in neurology
Comparative Performance Benchmarking of WebSocket Libraries on Node.js and Golang
No full textThe demand for responsive real-time web applications continues to grow, making the selection of backend technology and WebSocket libraries a crucial factor in determining performance. Node.js and Golang are popular platforms for real-time applications. However, the WebSocket library within them offers a trade-off between features and efficiency, the impact of which has not been comprehensively measured. This research aims to fill this gap by conducting a quantitative performance analysis to compare the efficiency and scalability of four WebSocket libraries: ws and socket.io on Node.js, and gorilla/websocket and coder/websocket on Golang. This research uses a benchmarking experimental method with client load simulations that gradually increase from 100 to 1000 concurrent clients. The experiment was conducted through two scenarios, namely the Echo Test and Broadcast Test. In the Echo Test, the performance metrics measured were Connection Time, Round Trip Time (RTT), and Throughput. Meanwhile, in the Broadcast Test, the performance metric measured was Broadcast Latency. The results from the Echo Test show a significant performance disparity. At a peak load of 1000 clients, socket.io achieved a throughput of only 27,152 messages/second, whereas the lightweight libraries (ws, gorilla/websocket, and coder/websocket) all achieved over 44,000 messages/second. In the Broadcast Test with a high load, the latency difference between the four libraries became insignificant. Therefore, for applications prioritizing raw performance in point-to-point communication, certain WebSocket libraries such as ws, gorilla/websocket, and coder/websocket are more suitable for future development
Enhancing Entity Extraction in E-Government Complaint Data using LDA-Assisted NER
No full textWith the rapid development of information technology, governments are increasingly challenged to provide digital channels that enhance public participation in governance. LaporGub, an official platform managed by the Central Java Provincial Government, accommodates citizens' aspirations and complaints, but faces challenges in processing large amounts of unstructured text. Manual analysis is time-consuming and error-prone, resulting in delayed responses and decreased service quality. Conventional Named Entity Recognition (NER) models struggle to handle informal Indonesian-language text, while transformer-based approaches require substantial computing resources that are not widely available in local government environments. Therefore, this study aims to develop a lightweight NER approach by integrating Latent Dirichlet Allocation (LDA) as a semantic pre-annotation tool to improve the accuracy of entity extraction in Indonesian e-government complaint data. To achieve this goal, a dataset of 53,858 complaint reports from the LaporGub platform (2022–2025) was processed using LDA topic modeling (k=10) to provide semantic context during annotation. Next, the enriched dataset was used to train a spaCy-based NER model targeting three entity types: LOCATION, ORGANIZATION, and PERSON, with a training-validation-test split ratio of 70:15:15 using stratified sampling. The evaluation showed that the proposed NER+LDA model achieved a precision of 90.03%, a recall of 81.86%, and an F1-score of 85.75%, representing improvements of +5.78, +2.55, and +4.04, respectively, compared to the baseline NER model (F1-score: 81.71%). Furthermore, the most significant improvements occurred in the detection of ORGANIZATION and PERSON entities. These findings confirm that the integration of LDA as a pre-annotation strategy effectively improves NER performance on informal complaint texts in Indonesia, thus offering a practical and resource-efficient alternative to transformer-based methods for e-government applications
Integration Of Pca And K-Means Clustering For Staple Food Segmentation In Support Of National Food Policy
No full textThis study aims to develop cross-provincial staple-food segmentation by integrating Principal Component Analysis (PCA) and K-Means to support policy formation. The dataset comprises 2023 staple-food consumption for 34 Indonesian provinces across six indicators from BPS/SUSENAS. All indicators were standardized using z-score, reduced via PCA, and the resulting component scores were used as inputs to K-Means. Three components (PC1–PC3) explained 73.86% of the variance and captured shifts between sweet/animal-based vs. plant foods, fatty or animal-based grains, and the energy contribution of fat. The optimal number of clusters was determined as k = 3, yielding Silhouette = 0.466 and DBI = 0.733, indicating sufficiently compact and well-separated groups. The results reveal three segments: the first group consists of 11 provinces that are predominantly plant-based with low sugar and low animal-based consumption; the second group includes 13 provinces characterized by high animal-based and high-fat consumption; and the third group comprises 10 provinces with low-fat diets and fresh plant-based consumption. Stability checks on initialization and a leave-one-feature-out procedure confirmed consistent assignments. This fills an empirical gap: to our knowledge, no prior research integrates PCA with K-Means for cross-provincial staple-food segmentation in Indonesia while also reporting internal validation. Practically, the study provides operational segmentation to support food-security interventions moving beyond composite indices toward actionable targeting for production support, supply/price stabilization, and improved nutritional access thereby reframing IKP/FSVA from index-ranking to evidence-based segmentation
MCDM-based Fire Risk Mapping with Geospatial Visualization and Blockchain
No full textForest fires are among the most destructive environmental disasters in Indonesia, causing long-term ecological damage, health problems, and economic disruption. Increasing occurrences driven by climate anomalies, land clearing, and vegetation dryness highlight the need for intelligent and data-driven risk monitoring systems. This study introduces a hybrid analytical framework that integrates Multi-Criteria Decision-Making (MCDM) with blockchain-based data management and geospatial visualization to identify forest fire risk levels. The proposed model combines the Analytic Hierarchy Process (AHP), Weighted Sum Model (WSM), and Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) to evaluate multiple parameters, including temperature, humidity, rainfall, and the Normalized Difference Vegetation Index (NDVI). Environmental data were securely obtained from a private Ethereum blockchain using Ganache, Truffle, and MetaMask to ensure transparency, integrity, and immutability. Results were visualized through an interactive Leaflet.js interface, allowing real-time geospatial monitoring linked to blockchain transaction hashes. The AHP analysis revealed that temperature (0.36) and humidity (0.27) contributed 63% of the total decision weight, while TOPSIS identified high-risk zones consistent with historical records. Validation against BNPB data achieved 90.7% accuracy, confirming the model’s reliability. The integration of MCDM, GIS, and blockchain provides a transparent, decentralized, and verifiable approach for national-scale fire-risk management, enhancing the accuracy and credibility of environmental decision-making systems
Food and Physical Activity Tracking Application with Simple Dietary Pattern Analysis
No full textThis study focuses on the development of a mobile application to track food intake and physical activity while offering simple dietary pattern analysis. The primary goal was to create an intuitive tool enabling users to log meals, record physical activities, and receive actionable feedback on caloric balance. Developed using Agile methodology, the application includes user-friendly interfaces for data entry, a dashboard for visualizing caloric intake and expenditure, and feedback to enhance users’ understanding of dietary habits. Results from a one week user testing phase demonstrated high user satisfaction, with participants appreciating the app’s simplicity and clarity in presenting health-related insights. The app effectively encouraged users to engage with their dietary and activity habits, promoting informed lifestyle decisions. However, limitations such as the lack of detailed macronutrient tracking and integration with wearable devices were identified, which could improve accuracy and broaden the app's appeal. Future improvements are suggested, including the addition of macronutrient analysis, wearable device compatibility, and features like goal-setting and gamification to enhance engagement. These findings indicate that a straightforward, user-friendly health tracking app can significantly increase health awareness and support behavior change, particularly for individuals new to health monitoring. The research highlights the potential of simple digital tools to foster sustainable health improvements while addressing users’ needs effectively