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All Journal Lontar Komputer: Jurnal Ilmiah Teknologi Informasi JURNAL SISTEM INFORMASI BISNIS JOIV : International Journal on Informatics Visualization Jurnal Nasional Pendidikan Teknik Informatika (JANAPATI) Informasi Interaktif DIKEMAS (Jurnal Pengabdian Kepada Masyarakat) Jurnal Teknik Ilmu dan Aplikasi Jurnal Pengabdian Kepada Masyarakat
Shoumi, Milyun Ni’ma
Politeknik Negeri Malang
Automotive Engineering Chemical Engineering, Chemistry & Bioengineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Mechanical Engineering Social Sciences Other

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An Experimental Study on Deep Learning Technique Implemented on Low Specification OpenMV Cam H7 Device Asmara, Rosa Andrie; Rosiani, Ulla Delfana; Mentari, Mustika; Syulistyo, Arie Rachmad; Shoumi, Milyun Ni'ma; Astiningrum, Mungki
JOIV : International Journal on Informatics Visualization Vol 8, No 2 (2024)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62527/joiv.8.2.2299

Abstract

This research aims to identify and recognize the OpenMV Camera H7. In this research, all tests were carried out using Deep Machine Learning and applied to several functions, including Face Recognition, Facial Expression Recognition, Detection and Calculation of the Number of Objects, and Object Depth Estimation. Face Expression Recognition was used in the Convolutional Neural Network to recognize five facial expressions: angry, happy, neutral, sad, and surprised. This allowed the use of a primary dataset with a 48MP resolution camera. Some scenarios are prepared to meet environment variability in the implementation, such as indoor and outdoor environments, with different lighting and distance. Most pre-trained models in each identification or recognition used mobileNetV2 since this model allows low computation cost and matches with low hardware specifications. The object detection and counting module compared two methods: the conventional Haar Cascade and the Deep Learning MobileNetV2 model. The training and validation process is not recommended to be carried out on OpenMV devices but on computers with high specifications. This research was trained and validated using selected primary and secondary data, with 1500 image data. The computing time required is around 5 minutes for ten epochs. On average, recognition results on OpenMV devices take around 0.3 - 2 seconds for each frame. The accuracy of the recognition results varies depending on the pre-trained model and the dataset used, but overall, the accuracy levels achieved tend to be very high, exceeding 96.6%.
Pengembangan Aplikasi Dan Pelatihan Sistem Informasi TPQ Madinah Ma’arif 10 An-Nur Kota Malang Wakhidah, Rokhimatul; Affandi, Luqman; Shoumi, Milyun Ni'ma; Kirana, Annisa Puspa; Hormansyah, Dhebys Suryani; Arief, Sofyan Noor
DIKEMAS (Jurnal Pengabdian Kepada Masyarakat) Vol 6 No 1 (2022)
Publisher : Politeknik Negeri Madiun

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Abstract

Advances in information technology have a major impact on the use of technology in various fields, one of which is education. Information technology is used as a means to simplify the teaching and learning process to the management of educational institutions. Almost all educational institutions have utilized information technology, one of which is the application of an integrated information system to handle administrative affairs of educational institutions. TPQ Madinah Ma'arif 10 An-Nur is one of the TPQ that has implemented advances in information technology. This is reflected in the use of value input information systems and the printing of student report cards. Over time, this feature has not met the needs of the management in carrying out TPQ operational activities. TPQ institutions do not yet have a system that handles the presence of students. And there is no system that handles the recording of payments
Co-Authors Affandi, Luqman Annisa Puspa Kirana Arie Rachmad Syulistyo Astiningrum, Mungki Banni Satria Andoko Batubulan, Kadek Suarjuna Budiprasetyo, Gunawan Dhebys Suryani Hormansyah, Dhebys Suryani Dwi Puspitasari Joni Dwi Pribadi Luqman Affandi Mamluatul Hani’ah Mustika Mentari Nurtjahjani, Fullchis Pramana Yoga Saputra Radian Malek Rayrendra Ratsanjani, Muhammad Hasyim Ridwan Rismanto Rifqie Muhammad Rokhimatul Wakhidah Rosa Andrie Asmara Sofyan Noor Arief Sofyan Noor Arief Ulla Delfana Rosiani