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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 1,196 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.  

Page 120 of 120 | Total Record : 1196

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