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All Journal Jupiter Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) MANAJERIAL Jurnal Ilmiah Matrik MATRIK : Jurnal Manajemen, Teknik Informatika, dan Rekayasa Komputer Jurnal Teknologi Informasi dan Terapan (J-TIT) JOURNAL OF INFORMATION SYSTEM RESEARCH (JOSH) Jurnal Teknologi Informatika dan Komputer Journal of Computer and Information Systems Ampera Jurnal Pengabdian kepada Masyarakat Jurnal Nasional Pengabdian Masyarakat Jurnal Ilmiah Betrik : Besemah Teknologi Informasi dan Komputer International Journal of Advances in Artificial Intelligence and Machine Learning Jurnal Perangkat Lunak
Eka Puji Agustini
Univeritas Bina Darma
Agriculture, Biological Sciences & Forestry Humanities Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Environmental Science Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Library & Information Science Public Health 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.
Real-Time Outlier Detection in Fast-Moving Data Streams Eka Puji Agustini; Mohd Zaki Zakaria
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.v1i1.287

Abstract

Anomaly detection is a critical task in various fields such as finance, healthcare, network monitoring, and sensor data analysis, where identifying unusual patterns or outliers in data streams is essential for timely decision-making. Two commonly used techniques for anomaly detection are the Moving Average (MA) and Exponential Moving Average (EMA) methods. Despite their widespread use, selecting the appropriate method depends on the nature of the data and the requirements of the system. This paper presents a comparative analysis of MA and EMA for anomaly detection, focusing on critical factors such as speed of detection, stability, precision and recall, false positive rate, and computational efficiency. This research addresses the problem of determining which method, MA or EMA, is better suited for specific types of data, particularly in streaming environments with varying trends and anomalies. The results of our comparison indicate that EMA performs better in dynamic environments where rapid identification of anomalies is critical, such as financial markets or network traffic analysis. It quickly detects sudden deviations but may flag minor fluctuations as false positives due to its sensitivity. MA, on the other hand, is more stable and computationally efficient, with a lower false positive rate, making it more suitable for applications where long-term trend monitoring is required, and stability is prioritized over speed. This research highlights the strengths and weaknesses of both methods, demonstrating that the choice between MA and EMA should be based on the specific needs of the anomaly detection system. For real-time, high-speed environments, EMA offers a more responsive solution, while MA provides better stability and efficiency in long-term monitoring. A hybrid approach combining both methods could offer a more robust solution, adapting to different types of data and detection requirements.
ANALISIS KUALITAS WEBSITE PEMERINTAH DAERAH MENGGUNAKAN METODE WEB QUAL 4.0 DAN IMPORTANCE PERFORMANCE ANALYSIS Febriyanti Panjaitan; Rahjan Saputra; Susan Dian Purnamasari; Ch. Desi Kusmindari; Eka Puji Agustin Puji Agustini
JURNAL PERANGKAT LUNAK Vol 5 No 2 (2023): Jurnal Perangkat Lunak
Publisher : Indragiri Islamic University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32520/jupel.v5i2.2539

Abstract

Dalam era digital saat ini, teknologi informasi menjadi semakin penting bagi pemerintah untuk memenuhi kebutuhan masyarakat. Salah satu bentuk pelayanan publik melalui teknologi informasi adalah melalui website pemerintah daerah. Namun, masih ada beberapa website pemerintah daerah yang belum memenuhi standar kualitas yang baik dalam hal informasi yang disajikan dan kemudahan akses pengguna. Oleh karena itu, penelitian ini bertujuan untuk menganalisis kualitas website pemerintah daerah di kabupaten Ogan Komering Ilir (OKI) menggunakan metode Web Qual 4.0 dan Importance Performance Analysis (IPA). Metode ini telah digunakan dalam beberapa penelitian sebelumnya. Dalam penelitian ini, hasil analisis menunjukkan bahwa kepuasan pengguna dalam menggunakan website pemerintah cukup puas untuk 81% dari semua indikator, sedangkan harapan pengguna dengan hasil analisis kesesuaian sebesar 80% dan rata-rata hasil kesenjangan -0.81. Rekomendasi perbaikan dapat diberikan terutama pada atribut IQ3 (Menyediakan informasi yang up to date) dan atribut SIQ3 (Rasa aman dalam menyampaikan data pribadi) pada kuadran I untuk meningkatkan kualitas pelayanan publik melalui website
Co-Authors A.Zahrun Suyudi Ahkam Gagah Wahyudi Alek Wijaya Anggun Pailina Bella Rosalia Ch. Desi Kusmindari Eko Nardo Febriyanti Panjaitan Gege Ardiansyah Hadinata, Novri Hellen Puspita Sari Hesti Sukmawati Ilman Zuhriyadi Irman Effendy Irwansyah Ibrahim Kurniawan Kurniawan Maria Ulfa Marlindawati Marlindawati Megawaty Merry Agustina Misinem Mohd Zaki Zakaria Muhammad Nasir Nabila Kaulan Karimah Nisa Aprina Maris Nyimas Sopiah Rahjan Saputra Ravina Armiani Siti Sauda Susan Dian Purnamasari Susan Dian Purnamasari, Susan Dian Tomi Kapri Tri Oktarina USWATUN HASANAH Vivi Sahfitri Yesi Novaria Kunang