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Sinkron : Jurnal dan Penelitian Teknik Informatika
Published by Politeknik Ganesha Medan
ISSN : 2541044X     EISSN : 25412019     DOI : 10.33395/sinkron.v8i3.12656
Core Subject : Science,
Computer Science & IT
Scope of SinkrOns Scientific Discussion 1. Machine Learning 2. Cryptography 3. Steganography 4. Digital Image Processing 5. Networking 6. Security 7. Algorithm and Programming 8. Computer Vision 9. Troubleshooting 10. Internet and E-Commerce 11. Artificial Intelligence 12. Data Mining 13. Artificial Neural Network 14. Fuzzy Logic 15. Robotic
Arjuna Subject : Ilmu Komputer (semua kategori) - Visi Komputer dan Pengenalan Pola
Articles 1,261 Documents
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Application of LSTM-Based Deep Learning for Stock Return Prediction of DCII Mulyani, Sri; Ilham, Wanda
Sinkron : jurnal dan penelitian teknik informatika Vol. 9 No. 4 (2025): Articles Research October 2025
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/sinkron.v9i4.15353

Abstract

Stock return prediction is one of the areas that has received great attention in modern finance because it can help investors make more informed decisions and reduce the risk of market uncertainty. This study applies a deep learning approach based on Long Short-Term Memory (LSTM) to predict the return of DCII (PT DCI Indonesia Tbk) shares as a representation of highly volatile stocks on the Indonesia Stock Exchange. The purpose of this study is to evaluate the performance of twelve LSTM variants—including LSTM-Base, LSTM-Wide, LSTM-Stack2, LSTM-Stack3, LSTM with Dropout, BiLSTM, BiLSTM with Attention, and LSTM with Attention Mechanism—by comparing their performance on daily (H=1) and weekly (H=7) prediction horizons using historical data from id.investing.com. The initial data undergo preprocessing involving local format cleaning, calculation of technical indicators (MA, EMA, MACD, RSI, ATR, Bollinger Bands, etc.), MinMax normalization, and sequencing (windowing) with 30, 60, and 120-day lookbacks. The training process uses a uniform configuration with Adam optimization and early stopping to prevent overfitting, while the evaluation employs MAE, RMSE, MAPE, and R² metrics. The results show that LSTM-Stack3 (LB=60, H=1) provides the best performance with MAE = 0.020, RMSE = 0.031, MAPE = 5.0%, and R² = 0.91, followed by LSTM-Stack2-DO as the second-best configuration. Meanwhile, the LSTM-LB120-H7—the only model evaluated with a seven-day horizon—achieves the lowest performance due to higher long-term uncertainty. These findings confirm that stacked LSTM architectures are more effective for short-term return forecasting, whereas longer horizons require hybrid or enhanced approaches for stable performance..
Hybrid CNN and KNN Approach for Coffee Bean Quality Identification Army, Widya Lelisa; Anita, Sri; Ramadhina, Retno
Sinkron : jurnal dan penelitian teknik informatika Vol. 9 No. 4 (2025): Articles Research October 2025
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/sinkron.v9i4.15366

Abstract

This study discusses the integration of Convolutional Neural Network (CNN) and K-Nearest Neighbors (KNN) for the identification of coffee bean quality as an effort to increase the competitiveness of local commodities. CNN is used as a feature extractor to produce an information-rich representation of coffee bean images, while KNN acts as a classifier to classify quality into two classes, namely Good and Defective. The dataset is divided into training, validation, and test data, with a total of 1,190 images obtained from the manual annotation process. The research stages include (1) pre-processing of data in the form of cropping based on bounding boxes, resize to 224×224 pixels, normalization, and data augmentation; (2) feature extraction using pretrained CNN (ResNet18) by eliminating the final classification layer to obtain a 512-dimensional feature vector; and (3) classification using KNN with variations in k values (3, 5, and 7) as well as Euclidean distance metrics. The results of the experiment showed that the CNN+Softmax baseline resulted in an accuracy of 86%, while the CNN+KNN method provided better performance. The k=5 configuration was proven to be optimal with an accuracy of 93.4%, precision, recall, and a balanced F1-score in both classes. The confusion matrix shows that most samples can be classified correctly with a low error rate. These findings are in line with previous research that emphasized the effectiveness of CNN in the extraction of visual features and the advantages of KNN on limited datasets. Thus, this approach can be a practical solution to support an automatic, accurate, and consistent coffee bean quality identification system to increase the competitiveness of local coffee commodities in the global market.
Lightweight Deep Learning Models for Facial Expression Recognition in Inclusive Education Ilmi, Miftahul; Doni Syofiawan
Sinkron : jurnal dan penelitian teknik informatika Vol. 9 No. 4 (2025): Articles Research October 2025
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/sinkron.v9i4.15370

Abstract

Facial expression recognition is an essential component in the development of artificial intelligence-based learning systems, particularly in the context of inclusive education that involves students with special needs. This study aims to evaluate the performance of several lightweight deep learning architectures in detecting facial expressions with high accuracy while maintaining computational efficiency. Facial image data were obtained from both public datasets and newly collected samples, which were preprocessed through face cropping, normalization, and data augmentation. The dataset was split into 70% training, 15% validation, and 15% testing. Four lightweight deep learning architectures: MobileNetV2, MobileNetV3 (Small and Large), and EfficientNetB0, were employed as the primary models using transfer learning and fine-tuning approaches. Evaluation was conducted using accuracy, loss, precision, recall, and F1-score metrics, complemented by visualization through confusion matrices. The results indicate that MobileNetV2 achieved the best performance with a test accuracy of 92%, precision of 93%, recall of 91%, and F1-score of 92%, while maintaining a relatively lightweight parameter size of 2.26 million. EfficientNetB0 ranked second with 83% accuracy, followed by MobileNetV3-Large (77%), whereas MobileNetV3-Small demonstrated the lowest performance (45%). Confusion matrix analysis revealed recurring misclassification patterns for certain expressions, such as Happy often misclassified as Sad, and Neutral overlapping with Angry. This study confirms that MobileNetV2 is the most optimal architecture for implementing facial expression recognition systems in inclusive education environments, as it balances high accuracy with computational efficiency. These findings provide a solid foundation for developing intelligent applications that support adaptive interaction in the learning process..
Comparative Performance Evaluation of MobileNetV3 and ResNet50 for Forest Fire Image Classification Hidayat, Muhammad Rizky Amirullah; Hindarto, Djarot; Sani, Asrul
Sinkron : jurnal dan penelitian teknik informatika Vol. 9 No. 4 (2025): Articles Research October 2025
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/sinkron.v9i4.15415

Abstract

Indonesia is one of the countries with a high incidence of forest and land fires (karhutla), especially during the dry season, thus requiring a fast and efficient early detection system. This study aims to compare the performance of two popular deep learning architectures, namely MobileNetV3 (Large and Small variants) and ResNet50, in forest fire image classification tasks using a transfer learning-based approach. This study emphasizes the comparison between accuracy and computational efficiency in a CPU-only environment, which represents real-world conditions of use in the field without GPU support. The dataset used is a combination of local field images from the Puncak area, Bogor, and a curated public forest fire dataset to ensure the model's generalization ability to diverse geographical conditions. The results of the experiment show that ResNet50 provides the highest accuracy with a training accuracy value of 0.677 and a validation accuracy of 0.647, but requires longer training and inference times. Meanwhile, MobileNetV3-Large and MobileNetV3-Small showed better computational efficiency, with only slightly lower accuracy (0.635 and 0.61) and high training stability. These findings confirm that lightweight models such as MobileNetV3 strike an optimal balance between accuracy, speed, and resource consumption, making them an ideal solution for implementing edge computing-based early detection systems. Overall, this research contributes by providing an empirical comparative analysis that can serve as a reference for selecting deep learning architectures for efficient and adaptive forest fire detection systems that are constrained by hardware limitations.
Integrating Blockchain with Neural Networks for Forest Fire Classification Yudistira, Hernan; Hindarto, Djarot; Sani, Asrul
Sinkron : jurnal dan penelitian teknik informatika Vol. 9 No. 4 (2025): Articles Research October 2025
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/sinkron.v9i4.15421

Abstract

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.
Integration of Machine Learning and Blockchain for Forest Fire Risk Prediction Ramadhani, Nursetiaji; Sholihati, Ira Diana; Hindarto, Djarot
Sinkron : jurnal dan penelitian teknik informatika Vol. 9 No. 4 (2025): Articles Research October 2025
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/sinkron.v9i4.15435

Abstract

This study presents an integrated framework combining machine learning and blockchain technology to enhance the accuracy, transparency, and reliability of forest fire risk prediction in tropical regions. Using geospatial and climatological datasets from Google Earth Engine (GEE), two ensemble algorithms—Random Forest (RF) and Extreme Gradient Boosting (XGBoost)—were trained to model spatial fire susceptibility based on variables such as temperature, humidity, rainfall, wind speed, and vegetation index (NDVI). The RF model effectively identified low-risk areas but was less sensitive to minority high-risk classes, while XGBoost demonstrated superior adaptability in handling class imbalance and achieved more balanced performance across all categories. To ensure data authenticity and traceability, the prediction results were validated and recorded on the Ethereum blockchain using smart contracts. Each prediction output was transformed into a cryptographic hash (SHA-256) to guarantee immutability and verifiability. The integration of machine learning with blockchain establishes a decentralized, tamper-proof, and verifiable prediction system, promoting data integrity and transparency in environmental monitoring. Overall, this research introduces a novel “verifiable prediction pipeline” that advances both artificial intelligence and blockchain applications in environmental informatics, supporting proactive and accountable forest fire mitigation strategies.  
Digital Transformation of Toddler Posyandu Services via an Android-Based Application Harsono, Harsono; Mulyono, Mulyono; Rinayati, Rinayati
Sinkron : jurnal dan penelitian teknik informatika Vol. 10 No. 1 (2026): Article Research January 2026
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/sinkron.v10i1.15360

Abstract

Abstract: Posyandu Balita as a community-based health service holds an essential role in improving maternal and child health in Indonesia. Nevertheless, the dependency on manual documentation frequently causes delays in reporting immunization, incomplete records, and limited access for parents to monitor child growth. This study sought to design and assess an Android-based Posyandu Balita application by applying a Research and Development (R&D) model combined with the System Development Life Cycle (SDLC) approach. The development process covered several phases: needs analysis, system design, application construction, pilot implementation, and evaluation through the Technology Acceptance Model (TAM). The pilot, which involved 10 health cadres and 10 parents, revealed that the application reduced data loss, facilitated more accurate immunization tracking, and encouraged stronger parental involvement. Functional testing indicated that the main features—digital medical records, reminder notifications, and growth chart visualization—worked consistently as intended. Based on TAM analysis, perceived usefulness (PU) and perceived ease of use (PEOU) significantly shaped users’ behavioral intention to utilize the system (PU = 62%, PEOU = 58%). Moreover, the level of parental compliance in child health monitoring increased, where 85% of parents actively accessed the digital platform compared to only 40% before the trial. Overall, the results demonstrate that mobile health applications developed with user-centered approaches can improve the effectiveness and efficiency of community-based services. The Posyandu Balita application is a promising innovation to support Indonesia’s digital health transformation. Further research is required to examine large-scale implementation, integration with national health information systems, and strategies for long-term sustainability. Keywords: Community Health, Toddler Posyandu, Android-based Application, Mobile Health, Technology Acceptance Model, Digital Innovation
Decision Model for Best Contraceptive Technique Recommendation Based on Patient's Ideal Profile Hugo, Veronika Novia; Sudipa, I Gede Iwan; Libraeni, Luh Gede Bevi; Pratistha, Indra; Atmaja, Ketut Jaya
Sinkron : jurnal dan penelitian teknik informatika Vol. 10 No. 1 (2026): Article Research January 2026
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/sinkron.v10i1.15377

Abstract

Choosing the right contraceptive method is essential to support the success of family planning programs. Many patients still choose methods without considering their medical conditions, which can lead to failure or side effects. This study designed a decision-making model based on Profile Matching to recommend contraceptive methods according to the patient’s ideal profile. The dataset was obtained from Faskes Level 1 Udayana Denpasar. Validation was conducted through discussions with midwives as experts, referring to the KLOP KB Wheel as the standard issued by the WHO. The evaluation results show a high level of agreement between the model’s recommendations and expert judgments, indicating that the model provides more objective and easily understood recommendations compared to manual approaches.
Analysis of Factors Causing Toddler’s Malnutrition in Medan City Using the Random Forest Method Simamora, Windi Saputri; Harahap, Siti Sarah; Pratama, Andre
Sinkron : jurnal dan penelitian teknik informatika Vol. 10 No. 1 (2026): Article Research January 2026
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/sinkron.v10i1.15380

Abstract

Malnutrition 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.
Implementation of YOLOv12 and PaddleOCR for Indonesian Bank Statement Table Extraction Kristanto, Samuel Miracle; Tanuwijaya, Evan
Sinkron : jurnal dan penelitian teknik informatika Vol. 10 No. 1 (2026): Article Research January 2026
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/sinkron.v10i1.15383

Abstract

The 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.

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