Publication Search

This research is motivated by the problems in manual attendance systems at schools, which remain vulnerable to fraud, time-consuming, and inefficient. The expected solution is to develop an automated attendance system based on face recognition that can operate in realtime with high accuracy. The research object is vocational high school students, with the applied method implementing the YOLO v10 algorithm for face detection, followed by the face_recognition library for identification. The instruments used include an Imou CCTV camera as the input device, a mid-range laptop as the hardware platform, and Python with SQLite as the software environment for data processing and attendance storage. The results show that the developed system achieved an average face detection accuracy of 96% under normal lighting and 91% under low lighting, with an average processing speed of 27 FPS. The implementation of an anti-duplication feature also ensured data validity by allowing each student to be recorded only once per day. In conclusion, the use of YOLO v10 in face-based attendance proved to be effective, efficient, and capable of reducing fraud. The implication of this study is that the system can be applied in both Islamic boarding schools and general schools as a modernization of attendance systems, with a recommendation for further development through web-based application and cloud database integration.

Conventional attendance systems often face various problems such as inefficiency, inaccuracies in attendance logging, and limitations in recapitulation processes. Manual systems are prone to human error and time-consuming, while fingerprint-based systems may fail when the sensor is affected by dirty, wet, or damaged fingers. This study aims to develop an attendance system based on Artificial Intelligence (AI) by utilizing the face_recognition function in Python and implementing a microservice architecture to improve efficiency and accuracy in attendance recording. The system is developed using the Agile Feature-Driven Development (FDD) method, which focuses on building system features based on prioritized business values. This method is applied within the Software Development Life Cycle (SDLC) to ensure a structured, iterative, and user-oriented development process. Facial recognition is performed by comparing the encoding of the captured face image with the data stored in the database. The results show that the system is capable of recording attendance automatically, accurately, and in real-time. Furthermore, the recapitulation process becomes more efficient as the system manages and presents the data systematically.

Attendance is an essential component of school management that serves as both proof of presence and a basis for evaluating the discipline of teachers, students, and staff. The manual attendance system that is still widely used often leads to several issues, such as delayed data recapitulation, potential attendance manipulation, and low recording accuracy. This study aims to design and develop a web-based attendance application at SD Negeri 19 Pasar Cubadak by integrating geolocation, timestamp, and face recognition features as attendance validation tools. The research method employed is the Research and Development (R&D) approach using the Waterfall software development model, which includes the stages of requirements analysis, system design, implementation, and testing. The application was developed using the Laravel framework with PHP and JavaScript programming languages and a MySQL database. The results show that the web-based attendance application can record attendance in real-time, automatically validate user location and identity, and generate accurate and transparent attendance reports. The User Acceptance Test (UAT) results indicate that the system is user-friendly, improves recapitulation efficiency, and assists the principal in objectively monitoring school members’ discipline. Therefore, this application is expected to serve as a modern solution to enhance the effectiveness of attendance administration in primary education environments.

Attendance is an essential activity in both educational institutions and companies, serving as an indicator of discipline, presence, and individual responsibility. Conventional attendance systems that still rely on manual journals often face several problems, such as vulnerability to manipulation, data loss, and physical damage. Meanwhile, modern methods such as fingerprint, QR code, RFID, and GPS are not entirely ideal since each has its own limitations in terms of cost, accuracy, user convenience, and potential misuse. For instance, fingerprint systems raise hygiene concerns due to shared use, while QR code and GPS methods are prone to fraud and location spoofing. To address these challenges, this study proposes a face-based attendance simulation system by integrating the YOLOv8 algorithm for face detection and Local Binary Pattern Histogram (LBPH) for face recognition. YOLOv8 was chosen for its ability to detect faces in real time with high speed and accuracy, while LBPH is employed for face recognition due to its robustness in handling variations in facial features and its relatively low computational requirements. This makes the system efficient even when implemented on medium-specification devices. The system was tested on 25 participants with a total of 250 attendance attempts. Based on the confusion matrix analysis, the system achieved outstanding performance with 98.4% accuracy, 98.4% precision, 100% recall, and a 99.2% F1-score. Furthermore, the system automatically recorded attendance dates and times with an average latency of 69.185 ms, proving its capability to operate quickly and reliably in real-world scenarios. Nevertheless, several limitations were observed, such as decreased accuracy when the face moved too quickly during image capture, as well as potential performance degradation under extreme lighting conditions. Despite these challenges, the proposed system demonstrates excellent performance and offers a promising solution for efficient, hygienic, and fraud-resistant attendance management applicable to both educational and professional environments.

This study explores the integration of facial recognition and GPS technology to enhance attendance management systems in educational institutions. By employing a two-layer verification process that combines face validation and location validation, the proposed system addresses common challenges of traditional attendance methods, such as proxy attendance and inefficient data management. The system not only improves accuracy and security but also provides valuable insights into attendance patterns through location-based analytics. Despite its advantages, the research highlights the need to address challenges related to data privacy, security, and user acceptance for successful implementation. Overall, this study contributes to the development of modern attendance management solutions, demonstrating the potential for increased efficiency and effectiveness in educational environments.

In recent years, virtual assistants have become an integral part of everyday life, simplifying routine tasks and allowing users to focus on more important matters. This research aiming to design GiggleGate, a virtual desktop assistant integrated with both face and speech recognition technology to enhance authentication security. The objective is to develop an authentication system that not only verifies user identity but also provides a more intuitive experience and seamless interaction. The research employs a development methodology to create and implement the system, which integrates face recognition via OpenCV and speech recognition via a Python library. The findings indicate that the integration of these technologies enhances security and user experience by offering dual-factor authentication. The system is expected to contribute to more secure and accessible virtual assistant applications, offering both a practical and efficient solution for users. The implications of this study suggest that the combination of face and speech recognition can provide an effective means to protect user privacy and improve the overall functionality of desktop assistants.

The research focuses on creating an automated attendance system using face recognition through the Convolutional Neural Network (CNN) approach at IPB University's Vocational School. The current manual attendance methods show limitations, such as potential inaccuracies in recording and the risk of cheating, like attendance proxies. To overcome these challenges, this study applies the CNN approach with Python and OpenCV, enabling automatic face detection and recognition for students. The system accurately logs attendance by identifying faces in real time. Testing indicates that the system records attendance reliably, whether with a single individual or with multiple faces present within a single frame.

This study aims to design and develop an automatic attendance system using Radio Frequency Identification (RFID) technology and an ESP32 CAM. The system is designed to improve efficiency and accuracy in recording student attendance in academic environments. When a student's RFID card is scanned, the system automatically identifies the student and captures an image through the ESP32 CAM as additional proof of attendance. Attendance data and captured images are stored in a database that is connected in real-time via the Internet of Things (IoT). The results of the testing show that this system is able to perform the attendance process quickly and accurately, providing convenience for administrators in managing and monitoring attendance data digitally.

Security systems in the maritime sector are crucial for preventing criminal activities and theft on board ships. The physical key systems commonly used today have vulnerabilities that can be exploited by unauthorized individuals. This research aims to design and test a smart door lock system using facial recognition with the ESP32 Wrover cam and a 4x4 Keypad as an additional security measure. The test results show that the system successfully recognizes faces with a 90% success rate at a distance of 15-100 cm, although there is a decrease in efficiency when more than one face is registered. The system also demonstrated 100% success in

This study evaluates the policy implementation of the face recognition system at Surabaya Gubeng Station as a digital innovation in public service by PT Kereta Api Indonesia (Persero). Using a qualitative approach and the policy evaluation model by William N. Dunn, the study assesses six key indicators: effectiveness, efficiency, adequacy, responsiveness, accuracy, and equity. The findings indicate that the system is effective in accelerating the boarding process, reducing queues, and improving passenger convenience. Efficiency is achieved through reduced staff workload and automated identity verification. However, several challenges remain, including technical disruptions, data input errors, and low digital literacy among users. Personal data protection is also a concern as most users are not fully aware of how their biometric data is managed. Therefore an adaptive governance strategy is needed, involving infrastructure improvement, staff training, public education, and greater transparency in data policy. With these measures, the face recognition system can serve not only as a tool for technical efficiency but also as a form of inclusive and sustainable public service transformation.

Face is one of the unique parts of the human body and can be used for identification purposes. Research on the application of facial recognition biometric technology has been carried out since 1960 and continues to be refined to this day. Humans can easily recognize an object or image, but not for a computer. This is the background behind the creation of a scientific discipline called Computer Vision. One deep learning algorithm that has been extensively researched and used for classifying various images is Convolutional Neural Network (CNN). The COVID-19 requires us to comply with health protocols, one of which is by wearing a mask when doing activities outside the home. The biometric presence system that is commonly used today can pose a risk of transmission because they have to touch the surface of an object that may have been contaminated from someone infected with the COVID-19 virus. Seeing the risks posed and the relevance to the times when people are accustomed to wearing masks, a study was conducted to create a masked face recognition system using the Convolutional Neural Network (CNN) method with VGG16 architecture. The dataset used was in the form of people's faces who were willing to be the object of research. This study produced highest accuracy rate of 85,71% with the application of various types of masks, namely surgical, cloth, and KF94 masks.

Seiring perkembangan zaman dan teknologi yang pesat saat ini, dalamkehidupan sehari-hari manusia tidak pernah lepas dari aktivitasnya untuk berinteraksi dengan peralatan elektronik yang membantu dan mempermudah pekerjaan manusia. Salah satunya adalah brankas otomatis dengan keamanan menggunakan sensor facerecognition dan fingerprint supaya perhiasan dan barang berharga lainnya lebih terjamin keamanannya dengan sistem keamanan ganda pada brankas.   Proyek akhir ini bertujuan untuk mengembangkan sebuah teknologi yang memanfaatkan beberapa macam komponen sensor, dimana komponen sensor tersebut melibatkan sensor getar untuk mengirimkan sms gateway jika brankas berpindah tempat, dan sim800l akan mengirimkan titik kordinat brankas berupa sms gateway. Dan GPS akan melacak keberadaan brankas sesuai titik kordinat tersebut. Buzzer akan berbunyi untuk memberikan tandaatau notifikasi.

Face recognition is an area that is still being researched and improved for various purposes such as attendance, population data collection, security systems and others. Two methods that are often used for face recognition applications are artificial intelligence methods, especially back-propagation neural networks (ANN) and learning vector quantization. Both of these techniques are directed learning techniques that are widely used to identify distinctive patterns, namely grouping patterns into groups of patterns, making them ideal for use in facial recognition applications. In this application, preprocessing of the input image includes the detection process of scaling, grayscale, edged with the sobel and threshold methods, carried out before the image is processed in ANN. Meanwhile, the ANN approach used to identify faces involves the Backpropagation method and the Learning Vector Quantization method. The findings of this analysis are a comparison of the backpropagation neural network method and quantization of the learning vectors of face recognition used to assess variations, limitations, strengths and optimal results of the two techniques for use in facial recognition systems.