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Menampilkan 1–10 dari 11 artikel
Enhancing Low-Resolution Facial Images for Forensic Identification Using ESRGAN
Helena Dewi Hapsari
; Arya Dimas Wicaksana
; Hafiz Fadli Faylasuf
; Asa Yuaziva
; Rivanka Marsha Adzani
; Endang Purnama Giri
; Gema Parasti Mindara
International Journal of Multilingual Education and Applied Linguistics
Vol 1
, No 4
(2024)
This research is motivated by the challenges in facial identification for forensic investigations due to poor image quality, especially from low-resolution CCTV recordings. Images with noise, low lighting, and suboptimal angles often hinder accurate facial recognition. This study aims to examine the effectiveness of the Enhanced Super-Resolution Generative Adversarial Network (ESRGAN) method in enhancing the quality of forensic facial images. The methodology consists of three main stages: data p...
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Design GiggleGate as Desktop Virtual Assistant with Face and Speech Recognition Authentication System
Jasmine Aulia Mumtaz
; Kinaya Khairunnisa Komariansyah
; Wildan Holik
; Reza Pratama
; Muhammad Galuh Gumelar
; Endang Purnama Giri
; Gema Parasti Mindara
International Journal of Computer Technology and Science
Vol 1
, No 4
(2024)
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 res...
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Real-Time Facial Emotion Detection Application with Image Processing Based on Convolutional Neural Network (CNN)
Hakim, Ghaeril Juniawan Parel
; Simangunsong, Gandi Abetnego
; Rangga Wasita Ningrat
; Jonathan Cristiano Rabika
; Muhammad Rafi' Rusafni
; Endang Purnama Giri
; Gema Parasti Mindara
International Journal of Electrical Engineering, Mathematics and Computer Science
Vol 1
, No 4
(2024)
Facial Emotion Recognition (FER) is a key technology for identifying emotions based on facial expressions, with applications in human-computer interaction, mental health monitoring, and customer analysis. This study presents the development of a real-time emotion recognition system using Convolutional Neural Networks (CNNs) and OpenCV, addressing challenges such as varying lighting and facial occlusions. The system, trained on the FER2013 dataset, achieved 85% accuracy in emotion classification,...
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Parking Slot Scanning for Maximum Efficiency Using Python
Faras, Algyon
; Andisa, Gany
; Nashwandra, Nakula Bintang
; Nadhifah, Jauza
; Widhiwipati, David Reza
; Giri, Endang Purnama
; Mindara, Gema Parasti
International Journal of Information Engineering and Science
Vol 2
, No 1
(2024)
The growing number of vehicles in major cities has posed significant challenges in parking lot management. Motorists often have difficulty finding empty parking slots quickly, which not only wastes time but also aggravates traffic congestion and increases air pollution. This research develops a Python-based smart parking system by utilizing the OpenCV library to detect the status of parking slots in real-time. The system uses a camera as the main sensor and processes the image using techniques s...
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Automatic Passenger Counting System on Public Buses Using CNN YOLOv8 Model for Passenger Capacity Optimization
Ari Dian Prastyo
; Sharfina Andzani Minhalina
; Surya Agung
; Denty Nirwana Bintang
; Muhammad Yordi Septian
; Endang Purnama Giri
; Gema Parasti Mindara
International Journal of Information Engineering and Science
Vol 1
, No 4
(2024)
This study presents the development and evaluation of an automatic passenger counting system for public buses using the YOLOv8 algorithm based on Convolutional Neural Networks (CNN). Accurate passenger counting plays a crucial role in optimizing public transportation operations, as it enables effective capacity management, reduces operational costs, and improves overall passenger comfort. Conventional manual counting methods are often inefficient, time-consuming, and prone to human error, partic...
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Traspoter Application Development: Website-Based Automatic Garbage Classification Using CNN Method
Bima Julian Mahardika
; Budy Santoso
; Aulia Anggraeni
; Muhamad Ali Imron
; Anatasya Wenita Putri
; Endang Purnama Giri
; Gema Parasti Mindara
International Journal of Multilingual Education and Applied Linguistics
Vol 1
, No 4
(2024)
This research focuses on the development of automatic waste classification by applying the Convolutional Neural Network (CNN) method in a web-based application. This system is designed to help the waste management process through automatic sorting between organic and inorganic waste, so that it can support recycling efforts and reduce environmental impacts. In its application, this application utilizes the CNN algorithm to analyze images and recognize the type of waste with good accuracy. The de...
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Development of Hand Gesture Detection Application for Slap Mosquito Game Based on Image Processing
Rajhaga Jevanya Meliala
; Nur Indah Chasanah
; Jonser Steven Rajali Manik
; Anggito Rangkuti Bagas Muzaqi
; Syah Bintang
; Endang Purnama Giri
; Gema Parasti Mindara
International Journal of Electrical Engineering, Mathematics and Computer Science
Vol 1
, No 4
(2024)
The development of technology with digital image processing is often utilized to solve various problems in image processing, such as facial recognition, object detection, and interaction between users. In this study, we developed an interactive hand gesture-based game titled "Slap Mosquito" that utilizes image processing techniques to control the game through hand gestures. Using Rapid Application Development (RAD), Python, OpenCV, and Pygame methodologies, this game allows users to slap mosquit...
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Utilization of Image Processing to Detect Hair Length According to SOP at IPB Vocational School Using Region-Based Segmentation
Alya Putri Salsabila
; Achmad Syahmi Rasendriya
; Muthia Nurul Sa'adah
; Wahyu Mustika Aji
; Rizky Fadlurohman
; Endang Purnama Giri
; Gema Parasti Mindara
International Journal of Multilingual Education and Applied Linguistics
Vol 1
, No 4
(2024)
This study utilizes image processing technology to detect student hair length in accordance with the Standard Operating Procedures (SOP) at IPB Vocational School. Manual supervision is often inefficient and prone to subjectivity, leading to the development of an automated detection system using a region-based segmentation approach. This method identifies the forehead area as a reference point, where hair is considered long if it exceeds specified limits. The system is implemented in a web-based...
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Utilization of Digital Drawing Program With Hand Tracking Using the Mediapipe Framework
Jovita Nabilah Azizi
; Ester Olivia Silalahi
; Rafli Damara
; Muhammad Farhan Fahrezy
; Fikri Saputra
; Gema Parasti Mindara
; Endang Purnama Giri
International Journal of Multilingual Education and Applied Linguistics
Vol 1
, No 4
(2024)
This research focuses on the use of hand tracking technology in a drawing program based on the MediaPipe framework. The aim of this study is to develop a digital drawing system that can track hand movements in real-time without additional input devices like a mouse or stylus. This technology utilizes computer vision algorithms to detect and track the user's hand movements, which are then translated into strokes on the screen. The study employs a descriptive-qualitative method with a software exp...
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Identification of Traditional Herbal Leaves and Their Benefits Using K-Nearest Neighbors (KNN)
Nur Rahma Ditta Zahra
; Kanaya Sabila Azzahra
; Nur Iman Nugraha
; Muhammad Ilham Nurfajri
; Nabil Malik Al Hapid
; Endang Purnama Giri
; Gema Parasti Mindara
International Journal of Multilingual Education and Applied Linguistics
Vol 1
, No 4
(2024)
Abstract. This study presents a web-based system for identifying traditional herbal leaves using K-Nearest Neighbors (KNN) and image processing techniques focused on analyzing leaf shape and color. The dataset used consists of images of various types of herbal leaves, providing a basis for classification and medicinal benefit information retrieval. The system was tested with multiple leaf samples to assess accuracy, speed, and effectiveness in identifying leaf types based on visual characteristi...
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