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.

Dieng Kabupaten Banjarnegara memiliki iklim yang cocok untuk pertanian sayuran. Khususnya di Desa Ratamba Kecamatan Pejawaran Kabupaten Banjarnegara memiliki komoditas pertanian kentang dan wortel. Untuk saat ini hasil panen dipasarkan secara konvensional dengan mengumpulkan hasil panen pada pengepul dan mengirimkan ke pasar untuk selanjutnya dikirim ke beberapa daerah di sekitar kabupaten Banjarnegara. Pemasaran dengan metode konvensional sudah dilakukan dalam jangka waktu yang cukup lama. Akan tetapi ada celah kekurangan yang terjadi di rantai distribusi dalam proses bisnis pertanian tersebut. Salah satunya adalah petani tidak dapat menentukan harga sayuran hasil panennya karena distribusi sayuran harus melalui pengepul atau tengkulak terlebih dahulu. Sehingga yang menentukan harga di pasar adalah pengepul, bukan oleh petani secara langsung. Selain itu, petani juga tidak dapat memperluas jangkauan pemasaran hasil pertaniannya. Disebabkan distribusi barang tidak dilakukan oleh petani akan tetapi oleh dilakukan oleh pengepul juga. Oleh karena alasan tersebut maka pada pengabdian pada masyarakat ini dikembangkan website yang dapat diakses oleh petani di Desa Ratamba untuk memasarkan dan menjual hasil pertanian mereka secara langsung. Dengan adanya media online yang digunakan oleh petani diharapkan dapat meningkatkan pendapatan dan kesejahteraan para petani. Dengan demikian diharapkan petani dapat terus mempertahankan dan meingkatkan kualitas hasil pertanian mereka

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.

The increasing number of traffic accidents caused by motorcyclists not wearing helmets has led to an increase in the number of studies related to road safety surveillance. The research system used is an automatic system to detect whether the motorcyclist is wearing a helmet or not. Many studies use image processing systems, deep learning and computer vision. In this research, Cascade Classifier and Adaptive Boosting have been implemented for the process of identifying motorcycle riders with helmets and without helmets. The number of datasets used is 500 datasets with labels on the image of the driver with a helmet and the image of the driver without a helmet. Based on the test results, an accuracy of 90% has been obtained

Di tahun ini sudah terjadi perubahan atau revolusi dalam sistem industri yaitu revolusi industri 4.0, di mana industri sudah mengimplementasikan sebuah mesin-mesin yang serba cepat dan praktis untuk membantu dalam hal produktivitas. Dengan semakin pesatnya teknologi, banyak alat-alat yang di ciptakan untuk perkembangan teknologi di dunia. Pemanfaatan teknologi di gunakan untuk sebagai penggerak di pesatnya sistem berbasis AI ini dapat di manfaatkan untuk di berbagai bidang, salah satunya adalah penerapan sistem deep learning dan salah satu teknik yang terkenal dan sudah banyak digunakan untuk klasifikasi citra adalah menggunakan metode Jaringan Syaraf Tiruan (JST) yang mampu mengenali wajah manusia dan suatu gambar citra lalu mengklasifikasinya atau bisa di sebut image classification dan image recognition. Untuk perancangan sistem deteksi helm pada penelitian ini akan menggunakan algoritma CNN(Convolutional Neural Network). Convolutional Neural Network dan untuk model arsitektur yang di gunakan adalah MobileNetV2 dengan akurasi 80%, yang dapat di kategorikan baik dalam sebuah akurasi.

The increasing of digital technology today has helped many people to fulfill their needs. But the election system, still conventionally using paper in its implementation. Elections in general still use a centralized system, where there is an organization that manages it. Some of the problems that may occur in traditional electoral systems are that there are organizations that have full control over the database and system, so the possibility of hacking the database is quite a big opportunity.Blockchain innovation is one arrangement that can be utilized in light of the fact that it has a decentralized framework and the whole database is duplicated by all clients. Blockchain itself has been used by Bitcoin and Ethereum cryptocurrency which is known as a decentralized system. By using the blockchain in database recording on an e-voting system can reduce one source of fraud that is database manipulation. This study discusses the recording of voting data using blockchain technology. The implementation of Smart Contracts contained in the Ethereum Blockchain will be implemented to create this voting system