Purpose: Face recognition has become an increasingly intriguing field in artificial intelligence research. In this study,   This study aims to explore the application of CNNs, implemented through TensorFlow, to develop a robust model for enhancing facial recognition accuracy in student attendance systems. The focus of this research is the development of a model capable of recognizing student faces under various lighting conditions and poses in an academic environment, using a multi-class dataset of student images collected from internship attendance records at the Computer Science Department.
Methods: The dataset, comprising facial images from 19 students, served as the foundation for training and validating the CNN model. The dataset, sourced from the computer science department's internship attendance records, included a total of 231 images for training and 59 images for validation. The preprocessing phase included facial area detection and categorization, resulting in a well-organized dataset for training and validation. The CNN architecture, consisting of seven layers, was meticulously designed to achieve optimal performance.
Result: The model exhibited exceptional accuracy, reaching 93% on the validation dataset after 300 training epochs. Precision, recall, and F1-score metrics were employed for a detailed evaluation across diverse classes, highlighting the model's proficiency in accurately categorizing facial images. Comparative analyses with a VGG-16-based model showcased the superiority of the proposed CNN architecture. Moreover, the implementation of a web service demonstrated the practical applicability of the model, providing accurate predictions with a remarkable response time of less than 0.3 seconds.
Novelty: This comprehensive study not only advances face recognition technology but also presents a model applicable to real-world scenarios, particularly in student attendance systems.

<p>Pada SMP Negeri 26 Semarang, pengolahan data perpustakaannya masih menggunakan cara manual, sehingga pengurus perpustakaan merasa kesulitan untuk mengolah data perpustakaan yang seharusnya dapat dikerjakan dalam waktu yang singkat dan minim terjadinya kesalahan. Pada proses pendataan buku, anggota, peminjaman, pengembalian, kas dan pembuatan rekap laporan, SMP Negeri 26 Semarang masih menggunakan buku catatan sehingga mengalami beberapa masalah seperti terjadi <em>input</em> data ganda, pemberian kode buku yang kurang konsisten, buku yang digunakan untuk mencatat dapat rusak atau hilang, data tidak tersimpan dengan rapi. Dalam pembuatan sistem ini, metode pengumpulan data dilakukan dengan cara <em>observasi</em> (pengamatan) dan <em>interview</em> (wawancara). Metode pengembangan sistem menggunakan metode <em>Extreme Programming</em> (XP) dan pembuatan program dengan <em>Sublime Text</em>, <em>XAMPP</em>, <em>Chrome Browser</em>. Tujuan dalam pembuatan sistem perpustakaan ini diharapkan dapat menghasilkan sebuah program yang nantiya dapat membantu pihak sekolah dalam mengelola data-data perpustakaan</p><p><em>On 26 Semarang Public Junior High School, library data processing still using manual procedure, so the library management feels trouble for processing data library which should be done in a short time and have minimum mistake. In the process of collecting books data, user data, loan data, data return, treasury data, 26 Semarang Public Junior High School still using notebook so they experiencing problems like multiple data input, not consistent when processing books code, notebook damaged, and data is not stored neatly. In making this system, method of collecting data done by observation and interview. Using Extreme Programming (XP) in this system development, and using Sublime Text, XAMPP, Chrome Browser for creting this system. the purpose of making this system is expected to produce a program that later on can help the school in processing data library</em></p>