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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 IKRA-ITH Informatika : Jurnal Komputer dan Informatika 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) Journal of Mathematics Instruction, Social Research and Opinion Explore
Salman Salman
Universitas Teknologi Mataram
Humanities Computer Science & IT Education Engineering Languange, Linguistic, Communication & Media Mathematics Social Sciences

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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.
Optimizing Transdisciplinary Epistemic Knowledge Through STEAM+X-Integrated Epistemic Learning Patterns in Mathematical Comparison Muhamad Galang Isnawan; R. Didi Kuswara; Salman Salman; Lidaini Lidaini; Naif Mastoor Alsulami
Journal of Mathematics Instruction, Social Research and Opinion Vol. 5 No. 3 (2026): September
Publisher : MASI Mandiri Edukasi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58421/misro.v5i3.1318

Abstract

Low transdisciplinary epistemic knowledge is one of the problems in mathematical comparison learning. Therefore, this study aims to optimize this competency through the development of a teaching module based on epistemic learning patterns integrated with STEAM+X. This study used a design-based research design. The instruments used were a practical problem questionnaire, a transdisciplinary epistemic knowledge test, and a teaching module. The participants were 36 people, consisting of 15 students (aged 13–15 years) and 21 educational stakeholders (teaching experience 0–25 years). Qualitative data were analyzed using thematic analysis, while quantitative data were analyzed using the Wilcoxon Signed Rank Test. The results revealed that identity and orientation crises, lack of spatial experience, pedagogical mismatches, and ecosystem limitations were the contributing factors to low student competency. The learning principles expected to be optimized were student-centered learning, technology integration, and learning ecosystem support. ELP-STEAM+X-AI was then implemented because it aligned with the previous principles. The study concluded that the learning was able to optimize transdisciplinary epistemic knowledge (Z = -2,96; p = 0,003; and r = 0,76 with large impact) because students were facilitated in constructing mathematical comparison concepts through various STEAM+X-based epistemic activities. This finding recommends that other subjects adopt ELP-STEAM+X-AI to optimize student competency.
Penerapan Two-Factor Authentication Untuk Meningkatkan Keamanan Sistem Penjualan Akun Game Online Salman Salman; Maspaeni Maspaeni; Supardianto Supardianto
IKRA-ITH Informatika : Jurnal Komputer dan Informatika Vol. 10 No. 2 (2026): IKRAITH-INFORMATIKA Vol 10 No 2 Juli 2026
Publisher : Fakultas Teknik Universitas Persada Indonesia YAI

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37817/ikraith-informatika.v10i2.6845

Abstract

The advancement of information technology has increased digital transaction activities, including online game account sales. However, transactions conducted manually through social media have high security risks such as account theft and fraud. This study aims to implement Two-Factor Authentication (2FA) in a web-based game account sales information system to enhance user access security. The system development method used is the waterfall method, which includes analysis, design, implementation, and testing stages. The system is developed using PHP and MySQL and integrated with an authentication mechanism based on One-Time Password (OTP). The results of Black Box testing show that the system functions according to requirements and improves login security. The implementation of 2FA minimizes unauthorized access risks, making the system more secure and reliable
Co-Authors ahmad yani Ardiyallah Akbar Efendi, Muhamad Masjun erniwati, surni Haikal Wahyudi Hidayatullah, Beni Ari Imran, Bahtiar Isnawan, Muhamad Galang Kirana, Bq. Ajeng Liesna Putri Dende Kirana Kuswara, R. Didi Lalu Delsi Samsumar, M.Eng. Lidaini Lidaini Maspaeni Maspaeni Moh. Subli Muhammad Masjun Efendi Muhammad Rijal Alfian Naif Mastoor Alsulami Ramdan, Hendri Rudi Muslim Sriasih, Sriasih Subki, Ahmad Supardianto Supardianto Zaeniah Zaeniah Zaeniah Zaenudin Zaenudin