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All Journal JOIN (Jurnal Online Informatika) JOIV : International Journal on Informatics Visualization JURNAL MEDIA INFORMATIKA BUDIDARMA JURIKOM (Jurnal Riset Komputer) Abdimas Umtas : Jurnal Pengabdian kepada Masyarakat Jurnal Teknologi Terapan Building of Informatics, Technology and Science
Maisevli Harika
Jurusan Teknik Komputer Dan Informatika Politeknik Negeri Bandung
Religion Agriculture, Biological Sciences & Forestry Arts Computer Science & IT Control & Systems Engineering Education Electrical & Electronics Engineering Engineering Mechanical Engineering Social Sciences

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Multiscale Retinex Application to Analyze Face Recognition Supriyanto, Supriyanto; Harika, Maisevli; Ramadiani, Maya Sri; Ramdania, Diena Rauda
JOIN (Jurnal Online Informatika) Vol. 5 No 2 (2020)
Publisher : Department of Informatics, UIN Sunan Gunung Djati Bandung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15575/join.v5i2.668

Abstract

The main challenge that facial recognition introduces is the difficulty of uneven lighting or dark tendencies. The image is poorly lit, which makes it difficult for the system to perform facial recognition. This study aims to normalize the lighting in the image using the Multiscale Retinex method. This method is applied to a face recognition system based on Principal Component Analysis to determine whether this method effectively improves images with uneven lighting. The results showed that the Multiscale Retinex approach to face recognition's correctness was better, from 40% to 76%. Multiscale Retinex has the advantage of dark facial image types because it produces a brighter image output.
AR Make-up Filter for Social Media using the HSV Color Extraction Harika, Maisevli; Rachmat, Setiadi; Aulia, Nurul Dewi; Dwi, Zulfa Audina; Widartha, Vandha Pradwiyasma
JOIN (Jurnal Online Informatika) Vol 7 No 2 (2022)
Publisher : Department of Informatics, UIN Sunan Gunung Djati Bandung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15575/join.v7i2.994

Abstract

Choosing the appropriate cosmetics is an arduous task. Because cosmetics are tested directly on the skin to ensure each person’s preferences are met. The consumer repeatedly tries a sample and then discards it until he discovers one that meets his tastes. The cosmetics business and consumers are affected by this move. Companies can utilize Augmented Reality (AR) technology as an alternative to mass-producing cosmetic samples. The difficulty of deploying augmented reality is the difficulty of putting cosmetics into camera video streams. Each individual bears the burden of skin color and its effect on light. HSV Color Extraction was the method employed for this study. The application of augmented reality intends to enable consumers to test cosmetics with their chosen color and assist businesses in competing in the industry by promoting items and engaging customers. This work makes it easier to choose cosmetics using augmented reality and social media. AR simulates the usage of the desired color cosmetics, whereas social media allows users to obtain feedback on their color preferences. The outcomes of this study indicate that augmented reality (AR) apps can display filters in bright, dim, and even wholly dark lighting conditions. This research contributes originality that cosmetic firms can utilize to market their products on social media.
Optimizing YOLOv8 for Real-Time CCTV Surveillance: A Trade-off Between Speed and Accuracy Sholahuddin, Muhammad Rizqi; Harika, Maisevli; Awaludin, Iwan; Dewi, Yunita Citra; Dhia Fauzan, Fachri; Sudimulya, Bima Putra; Widarta, Vandha Pradiyasma
JOIN (Jurnal Online Informatika) Vol 8 No 2 (2023)
Publisher : Department of Informatics, UIN Sunan Gunung Djati Bandung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15575/join.v8i2.1196

Abstract

Real-time video surveillance, especially CCTV systems, requires fast and accurate face detection. Object detection models with slow inference times are ineffective in real-time. This study addresses this challenge by improving the inference speed of the YOLOv8 model, a leading object detection framework known for its accuracy and speed. We focus on pruning the model's architecture, particularly the P5 head section, which detects larger objects. According to Bochkovskiy's 2020 research, this modification enhances the model's performance specifically for medium and small objects in CCTV footage. The standard YOLOv8 model and its modified version were compared for inference time, mean Average Precision (mAP), and model weight. The pruned YOLOv8 model cuts inference time by 15.56%, from 4.5 ms to 3.8 ms, and reduces model weight. The advantages mentioned above are offset by a 1.6% decrease in mean average precision. This research advances object detection technology by demonstrating architectural modifications' efficacy. These changes make the model faster and lighter, making it suitable for real-time surveillance. The accuracy trade-off is slight. The implications of these findings are crucial for implementing efficient object detection systems in CCTV surveillance. These findings also lay the groundwork for future research to improve such systems' speed-accuracy trade-off.
Co-Authors Aulia, Nurul Dewi Bakhrun, Akhmad Cahya Ramadhan Carolina Magdalena Lasambouw Dewanto Joyo Pramono Dewi, Yunita Citra Dhia Fauzan, Fachri Diena Rauda Ramdania Djoni Djatnika Dwi, Zulfa Audina Eddy Bambang Soewono Fahmi Faturahman Nul Hakim Farida Agoes Ferry Feirizal Fitri Diani Imaniar Salsabila Fathina Iwan Awaludin Kokoy Siti Komariah Meilinda, Lina Mufqi Uwais Nastiar Salim Muhammad Reyhan Soeharto Muhammad Rizqi Sholahuddin Neneng Nuryati Nurul Dewi Aulia Rachmat, Setiadi Rahmadika, Sandi Ramadiani, Maya Sri Rifaldo Sukma Hidayat Rifqi Yuner Safira Oktarini Salma Meldiyana Setiadi Rachmat Sofy Fitriani Sudimulya, Bima Putra Supriyanto Supriyanto Vandha Pradwiyasma Widartha Widarta, Vandha Pradiyasma Yadhi Aditya Permana Yudi Widhiyasana Zulfa Audina Dwi