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All Journal IAES International Journal of Artificial Intelligence (IJ-AI) CESS (Journal of Computer Engineering, System and Science) Jurnal Pilar Nusa Mandiri JURNAL REKAYASA TEKNOLOGI INFORMASI JURNAL TEKNIK INFORMATIKA DAN SISTEM INFORMASI EXPLORE Jurnal Manajemen Informatika dan Sistem Informasi INFOKUM Prosiding Seminar Nasional Sisfotek (Sistem Informasi dan Teknologi Informasi) Explore
Salman Salman
Universitas Teknologi Mataram
Computer Science & IT Engineering

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Analisis Manipulasi Splicing pada Citra Digital menggunakan Metode Discrete Cosine Transform (DCT) dan Scale Invariant Feature Transform (SIFT) Efendi, Muhamad Masjun; Salman, Salman
CESS (Journal of Computer Engineering, System and Science) Vol 9, No 1 (2024): January 2024
Publisher : Universitas Negeri Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24114/cess.v9i1.53156

Abstract

Pemalsuan dalam citra digital seringkali terjadi di era teknologi saat ini. Bantuan software pengolahan citra memudahkan dan mempercepat proses manipulasi, mendorong orang untuk melakukan perubahan sebelum citra dipublikasikan di internet atau media sosial. Meski kegiatan ini umum dilakukan, seringkali merugikan orang lain dan merupakan bentuk penipuan publik terhadap keaslian citra. Salah satu metode manipulasi yang kerap kali digunakan adalah splicing, splicing adalah menambah objek dalam citra, contohnya meletakkan suatu objek pada citra target yang seolah-olah objek tersebut berada disana. Penelitian ini bertujuan untuk mendeteksi manipulasi jenis splicing dengan menggunakan metode Discrete Cosine Transform (DCT) dan Scale Invariant Feature Transform (SIFT). Metode DCT mentransformasikan blok piksel citra menjadi koefisien, sedangkan SIFT digunakan untuk menemukan frekuensi pada citra grayscale dengan mendeteksi keypoint yang sama. Metode ini mampu mendeteksi objek citra yang dimanipulasi dengan baik dan akurat. Dari hasil pengujian yang dilakukan, nilai akurasi deteksi image splicing pada citra dari internet dan koleksi citra hasil koleksi pribadi mencapai 100%. Harapannya, hasil penelitian ini dapat bermanfaat bagi masyarakat dalam membedakan citra yang asli dengan yang sudah dimanipulasi melalui teknik splicing.
STUDENT ATTENDANCE BASED ON FACE RECOGNITION USING THE CONVOLUTIONAL NEURAL NETWORK METHOD Salman, Salman; Ramdan, Hendri
Jurnal Pilar Nusa Mandiri Vol. 21 No. 1 (2025): Pilar Nusa Mandiri : Journal of Computing and Information System Publishing Pe
Publisher : LPPM Universitas Nusa Mandiri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33480/pilar.v21i1.6157

Abstract

Mataram University of Technology (UTM) still relies on a manual attendance process, such as signing paper-based attendance lists, which are prone to fraud and difficult to manage on a large scale. This study develops a face recognition-based attendance system using Convolutional Neural Network (CNN), which can automatically recognize visual patterns and unique facial features. CNN has advantages in extracting significant facial features, allowing it to recognize faces under various lighting conditions and viewing angles. The dataset used consists of 5,820 facial images from 97 students, with 60 augmented images per student. The results indicate that this system can be implemented in a lecture environment, achieving a validation accuracy of 98.5% at the 150th epoch. However, the model has some limitations, such as a relatively small dataset size and challenges in recognizing faces under extreme lighting conditions or unusual angles, which can affect accuracy in real-world applications. Additionally, although this system has the potential for real-time implementation, further optimization is required to ensure fast and accurate responses on a large scale. To overcome these limitations, future research can explore the use of direct camera input to enhance efficiency and user experience. Furthermore, improving dataset quality by incorporating variations in lighting and image angles, as well as exploring alternative deep learning architectures such as Vision Transformers (ViT) or Swin Transformer, can enhance model performance and generalization. By implementing these improvements, the facial recognition-based attendance system can be more optimal in enhancing accuracy and ease of use in academic environments.
Co-Authors ahmad yani Ardiyallah Akbar Efendi, Muhamad Masjun erniwati, surni Haikal Wahyudi Hidayatullah, Beni Ari Imran, Bahtiar Kirana, Bq. Ajeng Liesna Putri Dende Kirana Lalu Delsi Samsumar, M.Eng. Moh. Subli Muhammad Masjun Efendi Muhammad Rijal Alfian Ramdan, Hendri Rudi Muslim Sriasih, Sriasih Subki, Ahmad Zaeniah Zaeniah Zaeniah Zaenudin Zaenudin