In recent advancements in agricultural technology, quantum mechanics and deep learning integration have shown promising potential to revolutionize rice yield forecasting methods. This research introduces a novel Hybrid Quantum Deep Learning model that leverages the intricate processing capabilities of quantum computing combined with the robust pattern recognition prowess of deep learning algorithms such as Extreme Gradient Boosting (XGBoost) and Bidirectional Long Short-Term Memory (Bi-LSTM). Bi-LSTM networks are used for temporal feature extraction and quantum circuits for quantum feature processing. Quantum circuits leverage quantum superposition and entanglement to enhance data representation by capturing intricate feature interactions. These enriched quantum features are combined with the temporal features extracted by Bi-LSTM and fed into an XGBoost regressor. By synthesizing quantum feature processing and classical machine learning techniques, our model aims to improve prediction accuracy significantly. Based on measurements of mean square error (MSE), the coefficient of determination ($R^2$), and mean average error (MAE), the results are 1.191621 × 10⁻⁵, 0.999929482, and 0.001392724, respectively. This value is so close to perfect that it helps make essential decisions in global agricultural planning and management.

In the evolving landscape of agricultural technology, recognizing rice diseases through computational models is a critical challenge, predominantly addressed through Convolutional Neural Networks (CNN). However, the localized feature extraction of CNNs often falls short in complex scenarios, necessitating a shift towards models capable of global contextual understanding. Enter the Vision Transformer (ViT), a paradigm-shifting deep learning model that leverages a self-attention mechanism to transcend the limitations of CNNs by capturing image features in a comprehensive global context. This research embarks on an ambitious journey to refine and adapt the ViT Base(B) transfer learning model for the nuanced task of rice disease recognition. Through meticulous reconfiguration, layer augmentation, and hyperparameter tuning, the study tests the model's prowess across both balanced and imbalanced datasets, revealing its remarkable ability to outperform traditional CNN models, including VGG, MobileNet, and EfficientNet. The proposed ViT model not only achieved superior recall (0.9792), precision (0.9815), specificity (0.9938), f1-score (0.9791), and accuracy (0.9792) on challenging datasets but also established a new benchmark in rice disease recognition, underscoring its potential as a transformative tool in the agricultural domain. This work not only showcases the ViT model's superior performance and stability across diverse tasks and datasets but also illuminates its potential to revolutionize rice disease recognition, setting the stage for future explorations in agricultural AI applications.

Saat ini pemasaran dan penjualan hasil produk UMKM masih dilakukan dengan mengikuti berbagai macam pameran produk UMKM yang diadakan oleh pemerintah maupun perusahaan yang memberikan fasilitas pameran, melalui broadcast pesan dan menunggu pengunjung datang ke toko. Metode ini belum optimal dalam penyampaian informasi karena promosi yang dilakukan tidak menjangkau masyarakat yang berada jauh dari lokasi galeri pameran/toko. Media sosial saat ini menjadi alat yang sangat populer dan diminati oleh pelaku UMKM salah satunya adalah facebook yang di dalamnya terdapat fitur marketplace yang dapat menjangkau promosi dan penjualan yang tidak mengenal ruang dan waktu. Metode yang digunakan dalam penggunaan facebook marketplace dengan melakukan pelatihan langsung kepada pelaku UMKM untuk mem-posting produk, meng-share dan melayani pembelian konsumen. Hasil implementasi facebook marketplace memudahkan pelaku UMKM dalam memasarkan produk dan sekaligus melakukan penjualan dengan menggunakan smartphone yang dimiliki sehingga mampu meningkatkan volume penjualan

This paper proposed a hybrid method that had been solved both of these issues. We proposed a combination of Discrete Cosine Transform (DCT) and Haar Wavelet Transform (HWT) due to achieved imperceptibility and robustness comes from PSNR and NC value. Firstly, HWT applied in host image and then selected subband LL. LL subband will be divided into an 8×8 matrix in which each matrix was applied using DCT. Copyright embedded at the DC coefficient. In this paper we choose PSNR to measure the imperceptibility. Meanwhile, to measure the extracted copyright we used correlation coefficient. The results of this simulation method getting high PSNR value with an average of 43.2392 dB. Experiment results of extraction has tested using image watermarking attacks are JPEG compression, low pass filtering, mid filtering, unsharp filter, gaussian noise, cropingp, blurring, salt and pepper with the average value of the correlation coefficient of 0.8676. Our results proved that HWT-DCT has fulfilled the purpose of image watermarking, namely copyright protection.

Face spoofing detection is critical for biometric security systems to prevent unauthorized access. This study proposes a deep learning-based approach integrating FaceNet and DenseNet201 to enhance face spoofing detection performance. FaceNet generates identity-based embeddings, ensuring robust facial feature representation, while DenseNet201 extracts complementary texture-based features. These features are fused using the Concatenate function to form a more comprehensive representation for im-proved classification. The proposed method is evaluated on two widely used face spoofing datasets, NUAA Photograph Imposter and LCC-FASD, achieving 100% accuracy on NUAA and 99% on LCC-FASD. Ablation studies reveal that data augmentation does not always enhance performance, particularly on high-complexity datasets such as LCC-FASD, where augmentation increases the False Rejection Rate (FRR). Conversely, DenseNet201 benefits more from augmentation, while the proposed method performs best without augmentation. Comparative analysis with previous studies further confirms the superiority of the proposed approach in reducing error rates, particularly Half Total Error Rate (HTER), False Acceptance Rate (FAR), and FRR. These findings indicate that combining identity-based embeddings and texture-based feature extraction significantly improves spoofing detection and enhances model robustness across different attack scenarios. This study advances biometric security by introducing an efficient feature fusion strategy that strengthens deep learning-based spoof detection. Future research may explore further optimization strategies and evaluate the approach on more diverse datasets to enhance generalization.

Students who are graduated from Informatics Engineering have wide employment opportunities in the information technology work field, such as database administrator, data scientist, UI designer, IT project manager, network engineer, system analyst, software engineer and UX designer. Each job in Information Technology field has different skill requirement for the interest of work field. Therefore, IT skill classification is needed to find out the suitable career recommendation for Informatics Engineering students. Data from IT professionals which are obtained from LinkedIn account of IT professionals will be processed as reference for students. Data are processed using K-Means Clustering algorithm to find out how is feasible IT professionals data are used as a reference. Then, Collaborative Filtering method by the K-NN algorithm is used to determine classification based on the proximity between student skills and information technology job field. The output is recommendation of information technology job field which are generated from calculate of IT student skills. Result has been tested by testing one of user that has been labeled software engineer produce a recommendation output as a software engineer.