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All Journal CESS (Journal of Computer Engineering, System and Science) Khazanah Informatika: Jurnal Ilmu Komputer dan Informatika Jurnal Ilmiah Matrik MATRIK : Jurnal Manajemen, Teknik Informatika, dan Rekayasa Komputer Jurnal Nasional Pendidikan Teknik Informatika (JANAPATI) Jutisi: Jurnal Ilmiah Teknik Informatika dan Sistem Informasi Journal of Information Systems and Informatics Zonasi: Jurnal Sistem Informasi Jurnal Informasi dan Teknologi Jurnal Pengembangan Sistem Informasi dan Informatika Jurnal Indonesia : Manajemen Informatika dan Komunikasi Jurnal Informatika Teknologi dan Sains (Jinteks) Journal of Information System and Artificial Intelligence Kesatria : Jurnal Penerapan Sistem Informasi (Komputer dan Manajemen) Jejak digital: Jurnal Ilmiah Multidisiplin Aksi Kita: Jurnal Pengabdian Kepada Masyarakat International Journal of Advances in Artificial Intelligence and Machine Learning Jurnal Jaringan Komputer dan Keamanan (JJKK) Jurnal Sinergi Sistem Informasi Pengabdian Masyarakat
Maria Ulfa
Universitas Bina Darma
Humanities Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Environmental Science Languange, Linguistic, Communication & Media Social Sciences Other

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AI-Powered Face Mask Detection Utilizing MobileNetV2 for Health Monitoring Misinem Misinem; Eka Puji Agustini; Maria Ulfa
International Journal of Advances in Artificial Intelligence and Machine Learning Vol. 1 No. 1 (2024): International Journal of Advances in Artificial Intelligence and Machine Learni
Publisher : CV Media Inti Teknologi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58723/ijaaiml.v1i1.286

Abstract

The COVID-19 pandemic has highlighted the critical need for face masks to prevent virus transmission. Ensuring consistent mask usage in crowded public spaces remains a challenge, especially with manual monitoring methods that are inefficient and prone to error. To address this, this research introduces a real-time face mask detection system leveraging MobileNet-V2, a lightweight and efficient deep learning model known for its high performance in image classification tasks. The system utilizes a dataset from Kaggle comprising 11,792 images, divided into training (10,000), validation (800), and testing (992) sets. MobileNet-V2 was fine-tuned for this task, using its inverted residual layers to extract features and enhance performance effectively. Data augmentation techniques were applied to improve the model’s ability to generalize across diverse scenarios. The MobileNet-V2 model achieved an impressive 98.69% accuracy on the testing dataset, demonstrating exceptional reliability in identifying individuals wearing masks versus those without. Standard evaluation metrics, including precision, recall, and a confusion matrix, confirmed its robustness. This system’s ability to operate in real-time makes it ideal for public health surveillance in environments such as airports, shopping malls, and public transport. The proposed face mask detection system is both accurate and scalable, offering an efficient solution for enforcing mask-wearing protocols in public spaces. The system’s integration of advanced deep learning techniques ensures its reliability in real-time monitoring, contributing to better public health management. Future work will focus on further optimizing the model and expanding its application to other health-related monitoring tasks, enhancing its value for public health surveillance.
SISTEM INFORMASI PENDAFTARAN SISWA BARU BERBASIS WEB MENGGUNAKAN METODE WEB ENGINEERING Mazida Khaeriyya Suryono; Maria Ulfa; Evi Yulianingsih; Muhammad Ariandi; Dinny Komalasari
Jurnal Informatika Teknologi dan Sains (Jinteks) Vol 7 No 4 (2025): EDISI 26
Publisher : Program Studi Informatika Universitas Teknologi Sumbawa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51401/jinteks.v7i4.6753

Abstract

Perkembangan teknologi informasi mendorong digitalisasi proses administrasi, termasuk pendaftaran siswa baru. MI Islamiyah Perguruan masih menggunakan sistem pendaftaran berbasis kertas yang rentan terhadap kesalahan, kehilangan data, dan ketidakefisienan. Penelitian ini bertujuan untuk merancang dan membangun sistem informasi pendaftaran siswa baru berbasis web menggunakan metode Web Engineering. Metode pengembangan terdiri dari lima tahap: customer communication, planning, modeling, construction, dan delivery & feedback. Hasil penelitian berupa sistem yang memfasilitasi pendaftaran online, upload dokumen, verifikasi admin, dan pencetakan bukti pendaftaran. Sistem ini telah diuji menggunakan blackbox testing dan berjalan sesuai kebutuhan. Simpulan menunjukkan bahwa sistem yang dibangun mampu meningkatkan efisiensi dan akurasi proses pendaftaran. Disarankan untuk menambahkan fitur arsip data tahunan dan integrasi payment gateway pada pengembangan selanjutnya.
Co-Authors Aan Restu Mukti Abdurrahman Abdurrahman AHMAD MUTATKIN BAKTI Ahmad Syazili Aldi Wildan Aulia Benny tinota aryansyah Berliana Irmadiani DINNY KOMALASARI Dio Ramadani Edi Supratman Edi Surya Negara Eka Puji Agustini Evi Yulianingsih Fajar Romadhan Fatmasari Fatmasari Fatoni Febi Franata Febriyanti Panjaitan Fitri Purwaningtias Fitri Purwaningtias Hasnawiyah, Hasnawiyah Hazira Maharani Helda Yudiastuti Irman Effendy Irwansyah Isna Wijayani Kiko Agustinus Putra Kurniawan Kurniawan M. Amirudin Amir Mazida Khaeriyya Suryono Megawaty Mgs Gustriawan Candra Misinem Muhammad Nasir Nana Misrina Putra, Alfinda Retno Ricko Mustika Jaya Ricky Nopria Suryadi Ridho Adilaksana Rizki Wijayanto Septa Hardini Sopiah, Nyimas Suryayusra Suryayusra Suyanto Suyanto Syahril Rizal Taqrim Ibadi Wahyu Wijaya Wardah Miftahul Jannah Wydyanto Wydyanto