Sinkron : jurnal dan penelitian teknik informatika
Not a member yet
1261 research outputs found
Sort by
Implementation of YOLOv12 and PaddleOCR for Indonesian Bank Statement Table Extraction
No full textThe increasing reliance on digital financial documents has highlighted the need for automated methods to extract structured information from bank statements. Traditional optical character recognition (OCR) systems often fail to capture complex tabular structures, leading to incomplete or error-prone transaction records. To address this challenge, this research proposes a two-stage detection and recognition pipeline that combines YOLOv12 for table and structural element detection with PaddleOCR for text extraction, followed by automated Excel conversion. The objective of this study is to improve accuracy in localizing tables, detecting rows and columns, and generating structured financial data that can be directly utilized for downstream applications. The methods involve training a YOLOv12-n model in two stages: Stage 1 focuses on detecting entire table regions, while Stage 2 focuses on identifying row and column structures within the detected tables. A lightweight AdamW optimizer with conservative augmentation strategies was applied to preserve the geometric integrity of document layouts. Results show that Stage 1 achieved precision of 0.998, recall of 1.0, and mAP50-95 of 0.989, while Stage 2 achieved precision of 0.992, recall of 0.964, and mAP50-95 of 0.899, demonstrating strong localization and structural recognition. The conclusions confirm that the proposed two-stage pipeline is effective for financial document processing, with potential applications in digital banking, auditing, and automated record management. Future research may focus on expanding datasets and addressing domain-specific variability
Comparative Study of Baseline and CBAM-Enhanced ResNet50 and MobileNetV2 for Indonesian Rupiah Banknote Classification
No full textThis study investigates the performance of Convolutional Neural Network (CNN) architectures enhanced with Convolutional Block Attention Module (CBAM) for Indonesian banknote classification. Although attention mechanisms have shown strong potential in improving fine-grained visual recognition, their effectiveness for the classification of banknotes with fine textures and similar color patterns remains underexplored, forming a key research gap addressed in this work. Four architectures, ResNet50, ResNet50+CBAM, MobileNetV2, and MobileNetV2+CBAM, were evaluated using K-Fold cross-validation on a dataset of 1,281 images representing seven banknote denominations. Experimental results show that ResNet50 achieves strong baseline performance with a weighted Train accuracy of 99.14% and a Val accuracy of 96.72%, while the integration of CBAM further improves feature discrimination, with ResNet50+CBAM obtaining the highest average accuracy across all folds with a weighted Train accuracy of 100% and a Val accuracy of 99.45%. MobileNetV2 showed lower performance due to its lightweight capacity with a Train accuracy of 91.88% and a decrease in Val accuracy of 85.71%. However, the addition of CBAM provided measurable improvements and greater stability with a Train accuracy of 99.61% and Val accuracy of 92.82%. Overall, CBAM improved CNN’s ability to focus on spatial information and salient channels, resulting in more reliable classification. ResNet50+CBAM emerged as the best-performing model, offering the best balance between accuracy and consistency. These findings support the development of reliable computer vision systems for financial technology applications, including automatic banknote recognition, counterfeit detection, and secure transaction verification
A Comparative Study of MobileNetV2 and ResNet50 for Multi-Class AI- Generated and Real Image Classification
No full textThis 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
Evaluation of Machine Learning Algorithm for Automatic Assessment of School Students' English Essay
No full textThe manual assessment of essays in English language learning often faces challenges related to objectivity and efficiency, especially on a large scale. With advancements in artificial intelligence technology, machine learning-based approaches have begun to be adopted to automate this process through Automated Essay Scoring (AES) systems. However, most existing AES models tend to rely solely on the final scores from the dataset without considering the structural quality of the writing, such as coherence between paragraphs. This study aims to evaluate the effectiveness of machine learning algorithms in assessing school students' essays by adding coherence features as predictor variables in a regression model. This approach uses linguistic feature representation techniques to explicitly build coherence indicators. The proposed model achieved a QWK improvement from 0.69 to 0.89 using SMOTE and coherence features. Meanwhile, human evaluation results showed that the pair of Rater 1 and Rater 2 achieved a QWK of 0.82, the pair of Rater 1 and Rater 3 scored 0.79, and the pair of Rater 2 and Rater 3 scored 0.81. These values indicate a high level of agreement among raters, suggesting that the assessment instrument used is stable. The main contribution of this study is introducing the coherence feature as an explicit predictor in the AES model, filling the gap not provided by standard datasets and proving that coherence improves model accuracy. This research provides practical benefits such as speeding up the evaluation process, reducing teachers' workload, and improving the objectivity and consistency of assessment in language education and evaluation
ELECTRE-Based Decision Support Model for LPG Base Location Optimization
No full textThe kerosene to LPG (Liquefied Petroleum Gas) 3 kg conversion program since 2007 has successfully improved household energy efficiency, but equitable access to bases in remote areas is still an obstacle. In Tabanan Regency, Bali, eight villages do not have access to 3 kg LPG bases, making it difficult for the community to obtain LPG at the Highest Retail Price (HET) and timely supply. This research develops a decision-making model using the ELECTRE method to recommend optimal base locations based on a case study of four villages: Pupuan Sawah, Dalang, Mundeh, and Belatungan. The model integrates 15 criteria including population density, infrastructure accessibility, existing base distance, and the presence of public facilities with a multi-stakeholder approach. The model is expected to be a tool for LPG agents and policy makers in determining the optimal base location and supporting equitable distribution of subsidized energy
Implementing a Payment Gateway in the Mount Slamet Hiking Ticketing System
No full textBackground—Many hiking basecamps in Indonesia still process bookings manually, causing queues, quota uncertainty, and errors in payment verification that hinder operations. Objective— Design and implement a web-based information system (e-ticketing) for Mount Slamet hiking, integrated with the Midtrans payment gateway; validate transactions in near-real-time and issue ticket IDs for gate inspection. Methods—Development followed Agile/Scrum. Requirements were gathered through observation and interviews; the design employed use-case, activity, ERD, and payment-flow models. Implementation used React (UI), Express and Prisma ORM (API), MySQL, and Midtrans Snap, with signature-verified, idempotent webhooks. Trials covered end-to-end black-box testing (booking; transitions among pending, paid, expired, and canceled; ticket-ID issuance; and check-in), cross-browser compatibility (Chrome, Edge, Firefox, Safari on desktop and mobile), and the System Usability Scale (SUS; n = 13). We also monitored propagation time from settlement to order update and behavior in the admin panel (route, quota, and date-closure management). Results—All functional scenarios passed; behavior was consistent across major browsers; mean SUS = 75.0 (> 68) indicates acceptable usability. Webhooks ensured automatic, duplicate-free status updates, with propagation on the order of seconds, so the reservation–payment–e-ticket chain operated end-to-end and was traceable via ticket-ID logs. Conclusion—The proposed e-ticketing system is technically feasible for basecamp operations and provides an architectural blueprint, core data schema, and a replicable Midtrans integration pattern. Future work will refine the public interface, add refund/void features, and conduct production-grade performance and security testing
IoT-Based Stress Monitoring Using CNN for HRV-GSR Analysis
No full textStress 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
IndoBERT-Based Pediatric Disease Classification and Symptom-Based Traditional Medicine Recommendation from Lontar Usada Rare
No full textThis study aims to develop a Balinese traditional text-based pediatric disease classification model using a fine-tuned IndoBERT model on the Lontar Usada Rare dataset. The dataset used consists of 422 entries containing disease symptoms, disease types, medicinal ingredients, and treatment procedures obtained from transliteration of lontar manuscripts and interviews with traditional medicine experts. Pre-processing was done through case folding, cleansing, and normalization, followed by label encoding on 35 disease classes. The IndoBERT model was fine-tuned using the AdamW optimizer with a learning rate of 5e-5, batch size 8, and 15 epochs. Evaluation results showed the model was able to achieve 90.59% accuracy, 94.71% precision, 90.59% recall, and 90.99% F1-score, indicating excellent performance in understanding the linguistic context of traditional medical text. The developed recommendation system integrates model prediction with TF-IDF-based cosine similarity method to provide the most relevant treatment recommendations based on user symptom input. This research makes an important contribution to the digitization and preservation of Balinese traditional medical knowledge through the development of a structured and widely accessible digital knowledge base
Facial Expression Recognition for Monitoring Learning Satisfaction in Smart Learning Environments Using MobileNetV2
No full textThis study develops a lightweight, privacy-aware Facial Expression Recognition (FER) framework to monitor learning satisfaction in Smart Learning Environments (SLEs). Using MobileNetV2 with a two-stage training scheme on the FER2013 dataset and evaluated on 35,000 test samples, the system addresses two main questions: (1) how effectively a customized MobileNetV2 recognizes core student expressions under authentic classroom conditions, and (2) how temporal aggregation and confidence calibration improve the stability of a Learning Satisfaction Index (LSI). The model achieves 0.39 accuracy and 0.34 macro-F1, with strong performance for happy, neutral, and surprise, while challenges remain for fear–surprise and neutral–sad. Temporal smoothing reduces prediction noise and enhances the reliability of LSI signals for instructional decision-making. The findings highlight practical implications for education, particularly in supporting real-time formative assessment and improving teachers’ awareness of student engagement through privacy-preserving, on-device affect monitoring
Experimental Characterization of ESP-Mesh Performance for Resilient Medical IoT Monitoring Systems
No full textThe reliability of medical Internet of Things (IoT) systems is critically dependent on network resilience, particularly in indoor hospital environments where conventional Wi-Fi infrastructures are vulnerable to single points of failure. Although ESP-Mesh has emerged as a promising self-healing communication protocol, its performance characteristics in medical IoT monitoring contexts remain insufficiently explored. This study aims to experimentally characterize the performance of ESP-Mesh networks for resilient medical IoT monitoring systems by analyzing multi-hop latency behavior, signal degradation, and communication stability under indoor medical-like conditions. A multi-parameter monitoring prototype integrating infusion volume, drip rate, and heart rate sensors was deployed as an experimental platform. Network performance was evaluated through controlled measurements of RSSI, end-to-end latency, and self-healing behavior, while MQTT was employed to assess cloud-based transmission efficiency. The results demonstrate that ESP-Mesh maintains stable self-healing communication with an average multi-hop latency of 0.714 s across distances up to 5 m, with latency increasing consistently as RSSI decreases. MQTT cloud transmission achieved a lower average latency of 0.247 s with zero packet loss, confirming its suitability for lightweight medical data delivery. Sensor evaluation revealed high accuracy for infusion volume monitoring (95.42%), while heart rate and drip rate measurements exhibited lower reliability due to signal interference and environmental sensitivity. These findings provide empirical insights into the performance limits and trade-offs of ESP-Mesh networks in medical IoT environments. The study confirms the feasibility of ESP-Mesh as a resilient communication backbone for medical monitoring, while highlighting the necessity of advanced signal processing to achieve clinical-grade sensing reliability