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All Journal Jurnal Ilmu Komputer dan Agri-Informatika Jurnal Informatika Upgris JURNAL TEKNIK INFORMATIKA DAN SISTEM INFORMASI Jurnal ULTIMATICS Krea-TIF: Jurnal Teknik Informatika Journal of Information Systems and Informatics Jurnal Riset dan Aplikasi Mahasiswa Informatika (JRAMI) Jurnal Sains Terapan : Wahana Informasi dan Alih Teknologi Pertanian PROSISKO : Jurnal Pengembangan Riset dan observasi Rekayasa Sistem Komputer Journal of Scientech Research and Development Jurnal Teknik Mesin, Industri, Elektro dan Informatika Prosiding Seminar Nasional Universitas Ma Chung Scientica: Jurnal Ilmiah Sains dan Teknologi AI dan SPK : Jurnal Artificial Intelligent dan Sistem Penunjang Keputusan Jurnal Rekayasa Sistem Informasi dan Teknologi International Journal of Multilingual Education and Applied Linguistics International Journal of Computer Technology and Science International Journal of Information Engineering and Science International Journal of Electrical Engineering, Mathematics and Computer Science
Endang Purnama Giri
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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): November : International Journal of Multilingual Education and Applied Linguist
Publisher : Asosiasi Periset Bahasa Sastra Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61132/ijmeal.v1i4.130

Abstract

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 application called Rambot, enabling students to verify their compliance with SOPs more easily. This technology aims to improve the accuracy and consistency of hair length monitoring.
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): November : International Journal of Multilingual Education and Applied Linguist
Publisher : Asosiasi Periset Bahasa Sastra Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61132/ijmeal.v1i4.148

Abstract

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 development uses technologies such as Python and OpenCV to ensure efficient processing of image data, with the CNN model trained using a dataset of 22,564 images. Test results show excellent accuracy, reaching 99.27% for organic waste and 98.72% for inorganic waste.
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): November : International Journal of Multilingual Education and Applied Linguist
Publisher : Asosiasi Periset Bahasa Sastra Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61132/ijmeal.v1i4.156

Abstract

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 preparation of low-resolution facial images, applying the ESRGAN model to enhance image resolution, and evaluating the results using metrics such as PSNR and SSIM. The findings reveal that ESRGAN significantly improves the visual details of facial images, thereby supporting better facial identification processes. These results have important implications for leveraging deep learning technology to facilitate image analysis in forensic contexts. However, challenges such as extreme noise presence require further development of methods to achieve more optimal outcomes.
indonesia Pengenalan Pola Ekspresi Wajah Untuk Pengolahan Citra Menggunakan Metode Convolutional Neural Network Nurjihan, Saniyyah Wafa; Nurbadillah, Nurbadillah; Faturrahman, Nafis; Wiguna, Indra Maki; Lasardi, Ekky Mulia; Giri, Endang Purnama; Mindara, Gema Parasti
JATISI (Jurnal Teknik Informatika dan Sistem Informasi) Vol 11 No 4 (2024): JATISI (Jurnal Teknik Informatika dan Sistem Informasi)
Publisher : Lembaga Penelitian dan Pengabdian pada Masyarakat (LPPM) STMIK Global Informatika MDP

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35957/jatisi.v11i4.9585

Abstract

This research focuses on the development of a facial expression recognition system based on image processing that is capable of identifying emotions with high accuracy. Facial recognition is a widely used technology for authentication and security, but it has potential applications in understanding emotional expressions. By utilizing Convolutional Neural Networks (CNN), the system is designed to detect and classify expressions such as happiness, sadness, anger, and neutrality in real time. The research stages include data collection of faces with various expressions, preprocessing, and training of the CNN model. Performance evaluation demonstrates that CNN outperforms traditional methods such as Viola-Jones and Support Vector Machine (SVM) under various lighting and angle conditions, achieving an average accuracy of 92%. These results prove the model's reliability in detecting emotional expressions with high precision. Further development is proposed to enhance performance, such as expanding the dataset variety and employing more advanced image processing techniques. Consequently, this system has the potential to make a positive impact on human-computer interaction.
Aplikasi Website dengan Flask dan Open CV untuk Filtering Warna Bagi Penderita Buta Warna Mia Putri Yeza; Marsya Halya Alfrida; Anka Luffi Ramdani; Fauzi Adi Saputra; Capriandika Putra Susanto; Endang Purnama Giri; Gema Parasti Mindara
Jurnal Teknik Mesin, Industri, Elektro dan Informatika Vol. 3 No. 4 (2024): Desember : JURNAL TEKNIK MESIN, INDUSTRI, ELEKTRO DAN INFORMATIKA
Publisher : Pusat Riset dan Inovasi Nasional

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55606/jtmei.v3i4.4536

Abstract

Color blindness is a hereditary vision disorder that impairs the ability to distinguish certain colors, significantly affecting daily activities and quality of life. This study aims to develop a web-based application using Flask and OpenCV to assist individuals with color blindness in identifying colors accurately. The application incorporates image processing technology to enhance color contrast and simulate real-time color perception adjustments. Employing the Waterfall model of the Software Development Life Cycle (SDLC), the study encompasses requirements analysis, system design, implementation, testing, and maintenance. Key features include Camify, for real-time color adjustments via device cameras, and Pickerify, for detecting colors in uploaded or live images. Testing reveals the application's effectiveness in providing improved color perception for users with various types of color blindness (e.g., Deuteranopia, Protanopia, Tritanopia). Despite minor limitations under extreme lighting conditions, the intuitive user interface and robust functionality make the application accessible to diverse user groups. Future enhancements include integrating AI for personalized filters and expanding compatibility with emerging technologies.
Development of a Real-Time Traffic Density Detection Website Using YOLOv8-Based Digital Image Processing with OpenCV Juliansyah, Rizki; Ar Rachman, Muhammad Aqil Musthafa; Amin, Muhammad Al; Tyanafisya, Aisya; Hanifah, Nurrizkyta Aulia; Giri, Endang Purnama; Mindara, Gema Parasti
Journal of Information System and Informatics Vol 6 No 4 (2024): December
Publisher : Universitas Bina Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51519/journalisi.v6i4.912

Abstract

This study introduces a real-time traffic density monitoring system utilizing YOLOv8-based digital image processing to improve traffic management efficiency. By leveraging YOLOv8’s enhanced speed and precision, the system detects and classifies five types of vehicles and displays traffic data through a web interface developed with OpenCV and Flask. Key implementation features include real-time video streaming and accurate detection metrics, with the system achieving 96% Precision, 84% Recall, and an F1 Score of 90% during field testing in Bogor. This indicates the system’s potential for minimizing manual traffic monitoring and aiding traffic authorities in making data-driven decisions. The research also discusses the system’s integration into urban traffic management and its scalability for diverse environments.
Pendeteksi Penggunaan Sabuk Pengaman Real Time Untuk Pengemudi Menggunakan Metode YOLOV5 Keysha Maulina Halimi; Tiara Ariyanto Putri; Muhammad Rahmat Maryadi; Rayhan Ananda Hafiz Pradipta; Hassan Nasrallah Matouq; Endang Purnama Giri; Gema Parasti Mindara
AI dan SPK : Jurnal Artificial Intelligent dan Sistem Penunjang Keputusan Vol. 2 No. 2 (2024): Jurnal AI dan SPK : Jurnal Artificial Inteligent dan Sistem Penunjang Keputusan
Publisher : CV. Shofanah Media Berkah

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

Kecelakaan lalu lintas merupakan salah satu masalah yang sangat merugikan dan membutuhkan penanganan yang serius. Kecelakaan mobil menempati peringkat dua teratas kendaraan yang sering mengalami kecelakaan lalu lintas. Salah satu upaya yang dapat digunakan untuk meminimalisir akibat dari kecelakaan berkendara adalah menggunakan sabuk pengaman. Mengenakan sabuk pengaman mencegah tubuh penumpang bertabrakan dengan struktur rangka mobil, benda lain di dalam mobil, atau penumpang lain di dalam mobil yang sama. Meskipun penggunaan sabuk pengaman saat berkendara memiliki dampak yang besar, masih banyak pengendara yang masih menyepelekan pentingnya penggunaan sabuk pengaman dalam keselamatan berkendara di jalan raya. Pada penelitian ini, pendeteksian penggunaan sabuk pengaman secara realtime untuk pengemudi mobil di jalan raya telah dilakukan dengan menggunakan metode deep learning YOLOv5. Tujuan dari penelitian ini adalah untuk mengembangkan dan mengimplementasikan sistem pendeteksian penggunaan sabuk pengaman secara real-time bagi pengemudi mobil di jalan raya menggunakan model YOLOv5 sebagai salah satu usaha untuk meminimalisir risiko terjadinya kecelakaan lalu lintas.
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): December : International Journal of Electrical Engineering, Mathematics and Com
Publisher : Asosiasi Riset Teknik Elektro dan Infomatika Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62951/ijeemcs.v1i4.108

Abstract

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 mosquitoes virtually in real-time through hand gesture recognition that is read by the camera and translated into in-game actions. RAD allows rapid development iterations and improvements based on user feedback, which is essential for improving system responsiveness and accuracy. This study focuses on detection precision, system responsiveness, and the impact of lighting on game performance, as measured using frames per second (FPS) and user gameplay results. The test results show that optimal lighting meets high detection accuracy, while low lighting conditions have a negative impact on accuracy and responsiveness. The results of this study provide insights for further development of gesture-based applications, especially regarding the importance of optimizing technical parameters and RAD methodology in improving user experience.
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): December : International Journal of Electrical Engineering, Mathematics and Com
Publisher : Asosiasi Riset Teknik Elektro dan Infomatika Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62951/ijeemcs.v1i4.123

Abstract

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, demonstrating high performance in detecting happiness, sadness, and surprise. The results highlight the system's effectiveness in real-time applications, offering potential for use in mental health and customer behavior analysis.
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): October: International Journal of Computer Technology and Science
Publisher : Asosiasi Riset Teknik Elektro dan Infomatika Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62951/ijcts.v1i4.113

Abstract

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 research employs a development methodology to create and implement the system, which integrates face recognition via OpenCV and speech recognition via a Python library. The findings indicate that the integration of these technologies enhances security and user experience by offering dual-factor authentication. The system is expected to contribute to more secure and accessible virtual assistant applications, offering both a practical and efficient solution for users. The implications of this study suggest that the combination of face and speech recognition can provide an effective means to protect user privacy and improve the overall functionality of desktop assistants.
Co-Authors Abdurrahman, Hasan Achmad Syahmi Rasendriya Aditya Wicaksono Agus Buono Ahmad Ridha Ahmad Ridha Alkautsar, Muhammad Farhan Alya Putri Salsabila Anatasya Wenita Putri Andisa, Gany Anggito Rangkuti Bagas Muzaqi Anka Luffi Ramdani Aprilianti, Dhila Ar Rachman, Muhammad Aqil Musthafa Ari Dian Prastyo Aria Wrdana Ariya Pratama Adjie Nugroho Arya Dimas Wicaksana Asa Yuaziva Athallah, Ananda Salma Aulia Anggraeni Auzi Asfarian Azhar Nadhif Annaufal Bagaskoro Dwi Adhie Nugroho Bima Julian Mahardika Budy Santoso Capriandika Putra Susanto Daffala Viro Hidayat Darmansah, Fadhlan Zaki Denty Nirwana Bintang Dini Nurul Azizah Ester Olivia Silalahi Faras, Algyon Fathonah, Lathifunnisa Faturrahman, Nafis Fauzi Adi Saputra Fauzi Ikhsan Suswanto Fikri Saputra Firman Ardiansyah Fitrah Satrya Fajar Kusumah Fredicia Fredicia Galih Ario Prayudo Gema Parasti Mindara Hafiz Fadli Faylasuf Hakim, Ghaeril Juniawan Parel Hanifah, Nurrizkyta Aulia Hassan Nasrallah Matouq Helena Dewi Hapsari Hendriyan, Amanda Pricillia Ibnu Aqil Mahendar Ibrahim, Arhammirza Inna Novianty Inyasdi Kahvi, Muhamad Restu Iqna Raidan Abdurrahman Jasmine Aulia Mumtaz Jonathan Cristiano Rabika Jonser Steven Rajali Manik Jovita Nabilah Azizi Juliansyah, Rizki Ka-sasi, R.I. Damai Kanaya Sabila Azzahra Keysha Maulina Halimi Khairunisa, Aulia Kinaya Khairunnisa Komariansyah Kurniawan, Fadly Lasardi, Ekky Mulia Luthfi Dika Chandra Manurung, Maryetha Marcelita, Faldiena Marsya Halya Alfrida Ma’arief, Denasyah Mia Putri Yeza Mindara, Gema Parasti Mochammad Alwan Al Ataya Muchlisinia, Newi Muhamad Ali Imron Muhammad Al Amin Muhammad Asyhar Agmalaro Muhammad Bilal Fauzan Muhammad Farhan Fahrezy Muhammad Galuh Gumelar Muhammad Ilham Nurfajri Muhammad Naufal Ardhani Muhammad Naufal Sutardi Muhammad Rafi Alexander Prayoga Muhammad Rafi' Rusafni Muhammad Rahmat Maryadi Muhammad Yordi Septian Muhammad, Fadhel Muthia Nurul Sa'adah Nabil Malik Al Hapid Nabil Raihan Alfarizi Nadhifah, Jauza Nashwandra, Nakula Bintang Nasywa Shafa Salsabila Nelvi, Annisa Amanda Nova Sukmawati Novianty, Inna Nur Iman Nugraha Nur Indah Chasanah Nur Rahma Ditta Zahra Nurbadillah, Nurbadillah Nurjihan, Saniyyah Wafa Pratama, Dharma Pratiwi, Iswi Nur Qonita, Vellisya Afifa Rabbani, Rafif Rafli Damara Raisa Mutia Thahir Rajhaga Jevanya Meliala Ramadhan, Dean Apriana Rangga Wasita Ningrat Rayhan Ananda Hafiz Pradipta Reza Pratama Rheynesta Hannover Riani, Lutfi Rio Ferddinansya Riupassa, Muhammad Hafidz Sidqi Rivanka Marsha Adzani Rizky Fadlurohman Saputra, Ananda Pratama Setiady Ibrahim Anwar Sharfina Andzani Minhalina Simangunsong, Gandi Abetnego Sri Yusrina Stefanny, Arlyn Sugi Guritman Sugiana, Lili Rahmawati Surya Agung Syah Bintang Tiara Ariyanto Putri Tyanafisya, Aisya Valenza, Ihsan Lana Wahyu Mustika Aji Widhiwipati, David Reza Wiguna, Indra Maki Wildan Holik Zafira, Cut Yasmin Zahra, Afnan