With the development of facial recognition technology, people can now be uniquely identified by their faces. Face recognition provides an easy and safe solution to monitor people’s presence in different contexts when it comes to attendance management. An overview of a face recognition system created for attendance purposes is given in this abstract, together with information on its main characteristics, workings, and advantages. Facial image recognition, which is one of the concentrations in the field of computer vision, is one of the biometric technologies that has been widely applied in security systems. In general, in its implementation, facial image recognition is carried out using an Android webcam to capture a person's facial image. It works by analyzing facial patterns and shapes to create a unique image that forms facial data, which can be used to identify users. then the facial image is compared with the previous facial image that has been saved. After that, the facial capture is saved into a database that can be managed by the admin as attendance history data.

Sistem Rekomendasi Resep Berdasarkan Citra Bahan Makanan Menggunakan Metode YOLOv8 dan Simple Additive Weighting, Ibnu Batutah, NIM E41201615, Tahun 2024, 90 hlm, Teknologi Informasi, Politeknik Negeri Jember, Aji Seto Arifianto, S.ST., M.T (Pembimbing). Memasak makanan sendiri bukanlah hal mudah bagi sebagian orang dengan kurangnya pengalaman, keterbatasan bahan, dan minimnya alat untuk memasak. Resep makasan selama ini bisa dijumpai di buku masakan, literasi di internet, maupun di platform sosial media. Kendalanya informasi yang bersifat satu arah menyebabkan masyarakat harus mengikuti resep dengan bahan dan alat yang belum tentu tersedia, sehingga masyarakat perlu melakukan improvisasi dan penyesuaian pada resep yang mereka pilih, atau bahkan mencari resep lain yang lebih sesuai dengan kondisi yang ada. Penelitian ini mengembangkan aplikasi rekomendasi resep dan computer vision berbasis android menggunakan metode YOLOv8 dan Simple Additive Weighting untuk membantu masyarakat dalam mencari resep masakan yang sesuai dengan keaadan mereka saat ini dengan tujuan dapat mengurangi potensi terjadinya sampah makanan yang masih layak konsumsi. Pengunaan metode YOLOv8 dikarenakan kemampuan YOLO untuk berjalanan dengan menggunakan sumber daya yang minimal sehingga cocok untuk aplikasi mobile. Hasil User Acceptance Test (UAT) telah mencapai 88.038% yang menunjukkan bahwa aplikasi dinilai telah mampu mendeteksi objek dan memberikan resep yang sesuai dengan keadaan pengguna. Hasil pengujian metode YOLOv8 menunjukkan rata-rata mAP50 0.915 dan akurasi sebesar 88.78% ketika diujikan dalam berbagai kondisi yakni kondisi ruangan tertutup dengan cahaya lampu (28 – 45 lux), kondisi ruangan dengan cahaya matahari (80 - 130 lux) dan kondisi objek blur.

Electronic voting enhances election efficiency and accessibility but faces security challenges, particularly phishing attacks. Visual cryptography offers a solution by securely sharing sensitive information while maintaining confidentiality. This research integrates visual cryptography into electronic voting systems to reduce phishing attacks and protect voter identities and voters, thus reinforcing democratic principles and advancing voting technology. The system uses visual cryptography to create shares from voter facial images. These shares must be combined by authorized parties to retrieve the original information, ensuring security through multi-party cooperation.

Abstract : The realm of computer vision, precise and reliable object tracking is crucial for applications ranging from surveillance to autonomous driving. Traditional object tracking techniques often struggle with challenges such as occlusion, rapid object motion, and dynamic backgrounds. Box-MOT addresses these issues by combining bounding box regression with advanced tracking algorithms to improve detection and tracking performance. Experimental results demonstrate that Box-MOT significantly outperforms conventional tracking methods in terms of accuracy, robustness, and computational efficiency.

The increasing prevalence of eating disorders among Generation Z necessitates advanced diagnostic tools to identify and address these issues effectively. This research focuses on the development of an expert system that leverages the Certainty Factor method to diagnose unusual behavior across multiple categories of eating disorders specifically within the Ge n Z demographic. The system aims to enhance early detection and provide accurate diagnoses by incorporating a wide range of symptoms and behavioral indicators. The Certainty Factor method allows for handling uncertainty and partial truths, making the syste m robust and reliable. Preliminary results indicate that the expert system significantly improves diagnostic accuracy compared to traditional methods, offering a valuable tool for healthcare providers in managing and treating eating disorders among young i ndividuals.

Abstract: As children is known as one of the most important aspects of society, children’s education is something serious to be taken of. ARECO leverages Augmented Reality (AR) technology to create an immersive educational platform aimed at enhancing children's understanding of animal and plant patterns and behaviours across different ecosystems. This innovative system integrates interactive AR experiences with educational content, enabling children to engage with and explore natural environments in a virtual setting. By simulating various ecosystems and their inhabitants, ARECO provides an engaging and effective learning tool that fosters curiosity and knowledge about biodiversity and environmental science. Abstrak: Anak dikenal sebagai salah satu aspek terpenting dalam masyarakat, sehingga pendidikan anak merupakan suatu hal yang serius untuk diperhatikan. ARECO memanfaatkan teknologi Augmented Reality (AR) untuk menciptakan platform pendidikan mendalam yang bertujuan untuk meningkatkan pemahaman anak-anak tentang pola dan perilaku hewan dan tumbuhan di berbagai ekosistem. Sistem inovatif ini mengintegrasikan pengalaman AR interaktif dengan konten pendidikan, memungkinkan anak-anak untuk terlibat dan menjelajahi lingkungan alam dalam lingkungan virtual. Dengan melakukan simulasi berbagai ekosistem dan penghuninya, ARECO menyediakan alat pembelajaran yang menarik dan efektif yang menumbuhkan rasa ingin tahu dan pengetahuan tentang keanekaragaman hayati dan ilmu lingkungan.

Non-native English-speaking students encounter substantial challenges in honing their English pronunciation skills, necessitating innovative solutions to bridge this gap. The "VOICESENSE" project endeavors to revolutionize English pronunciation learning for non-native high school students by harnessing innovative technology and constructivist learning principles. Inspired by the need for personalized feedback and real-time assessment, VoiceSense offers a platform for users to record, evaluate, and enhance their pronunciation skills independently. By tracking progress and motivating continuous practice, VoiceSense aims to empower students to achieve proficiency and confidence in English communication.

Analisis sentimen terhadap Program Merdeka Belajar Kampus Merdeka sangat penting karena media sosial, khususnya Twitter, telah menjadi platform utama bagi masyarakat untuk mengekspresikan opini dan pandangan mereka. Dengan adanya analisis sentimen, pemerintah dapat lebih responsif terhadap kebutuhan dan harapan masyarakat, sehingga dapat meningkatkan efektivitas dan penerimaan program di kalangan masyarakat luas. Dalam penelitian ini, data yang digunakan terdiri dari 1468 tweet yang dikumpulkan dari Twitter, dengan rincian 309 tweet mengandung sentimen negative, 379 tweet mengandung sentimen positif, dan 780 tweet mengandung sentiment netral. Hasil analisis menggunakan metode Naïve Bayes menunjukkan bahwa model yang dibangun memiliki akurasi sebesar 67.35%, dengan presisi sebesar 64.09 dan recall sebesar 67.35%. Hasil ini menunjukkan bahwa model tersebut cukup baik dalam mengklasifikasikan sentimen tweet. Meskipun ada ruang untuk perbaikan, hasil ini menunjukkan bahwa metode Naïve Bayes dapat digunakan secara efektif untuk analisis sentimen di media sosial.