Article Per Year (5 Year)
p-Index From 2021 - 2026
0.61
P-Index
This Author published in this journals
All Journal INTI Nusa Mandiri Journal of System and Computer Engineering Al-Buhuts (e-journal)
Imran Taufik
Unknown Affiliation
Computer Science & IT Decision Sciences, Operations Research & Management Economics, Econometrics & Finance

Published : 3 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 3 Documents
Search

 1 
IMPLEMENTASI MODEL DeiT UNTUK MEMBEDAKAN GAMBAR BUATAN AI DAN MANUSIA PADA ILUSTRASI ANIMASI 2D Erwin, Ibnu Taimiyah; Abdul Latief Arda; Imran Taufik; Muhammad Erwin Rosyadi. S; Hilyatul Auliyah Erwin
INTI Nusa Mandiri Vol. 19 No. 2 (2025): INTI Periode Februari 2025
Publisher : Lembaga Penelitian dan Pengabdian Pada Masyarakat

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33480/inti.v19i2.6306

Abstract

The development of artificial intelligence (AI) has influenced various fields, including art and visual design. AI Generative Art, which mimics human styles, has sparked debates on originality, artistic value, as well as legal and ethical challenges. Therefore, methods are needed to distinguish between AI-generated and human-made images, particularly in 2D animation illustrations. This study proposes the use of Data-efficient Image Transformers (DeiT) for image classification. Two models tested are DeiT Base and DeiT Tiny, using a dataset of 6,000 images equally divided between AI and human categories. The dataset is split into training (70%), validation (15%), and testing (15%). Experimental results show that DeiT Base achieves over 95% accuracy with fast convergence and optimal loss function stability. Meanwhile, DeiT Tiny attains around 93% accuracy, being more computationally efficient despite requiring more epochs for stability. Compared to previous models using a larger dataset (11,000 images per category) but achieving only 80% accuracy, DeiT performs better in both accuracy and computational efficiency, even with a smaller dataset. In conclusion, DeiT is effective for classifying 2D animation images. DeiT Base excels in accuracy and convergence speed, while DeiT Tiny is more resource-efficient, making it an ideal choice for environments with computational constraints.
Pengaruh Digitalisasi Pembelajaran, Kompetensi Profesional dan Komitmen Kerja dimoderasi oleh Budaya Organisasi terhadap Prestasi Siswa pada SMP Negeri 4 Makassar Misbahuddin, Misbahuddin; Nashriah Akil; Imran Taufik; Ihsan Guntur
Al-Buhuts Vol. 21 No. 1 (2025): Al-Buhuts
Publisher : Institute Agama Islam Negeri (IAIN) Sultan Amai Gorontalo

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30603/ab.v21i1.5217

Abstract

This study aims to analyze the influence of learning digitalization, teachers' professional competence, and work commitment on student achievement with organizational culture as a moderation variable. This study was conducted at SMP Negeri 4 Makassar by involving a randomly selected sample of students. This study uses a quantitative method with path analysis to test the hypothesis proposed. The results show that the digitization of learning has a significant effect on student achievement, especially when supported by a strong organizational culture. Teachers' professional competence has also been shown to significantly improve student achievement, with influence amplified by a supportive organizational culture. Teachers' work commitment has a significant effect on student achievement, and a positive organizational culture reinforces this influence. These findings emphasize the importance of the role of organizational culture in maximizing the positive influence of learning digitalization, teacher professional competence, and work commitment to student achievement. The recommendations of this study include strengthening organizational culture in schools, improving teachers' professional competence through continuous training, and integrating technology in the learning process to improve overall student achievement.
Coordinate Identity Features for Unsupervised Detection of Attendance Location Fraud Nur Trismi Auliah; Wardi Wardi; Hazriani Hazriani; Syafruddin Syarif; Yuyun Yuyun; Imran Taufik
Journal of System and Computer Engineering Vol 7 No 4 (2026): JSCE: October 2026
Publisher : Universitas Pancasakti

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61628/jsce.v7i4.2972

Abstract

Web-based attendance systems cannot read the mock location flag of a user device, thus, the coordinates produced by spoofing applications pass geofence validation unnoticed. This study asks whether detection can be moved from the device to the historical pattern, and which unsupervised detector suits the data that result. Eleven composite spatio-temporal-behavioural features were engineered from the attendance log of a corporate human resource information system in Indonesia, three of which measured how often an employee repeated the same coordinate at six-decimal precision. Isolation Forest, a one-class support vector machine, and a long short-term memory autoencoder were compared under twelve-fold walk-forward cross-validation on 249,818 records from 212 employees, evaluated against pseudo-labels derived from seven documented attendance rules. The stored models were then applied to 18,711 later records, and the leading indicator was confirmed with ten employees through a blinded matched case-control interview. The one-class support vector machine led on every accuracy metric, reaching a precision-recall area of 0.5367 and a Matthews correlation coefficient of 0.2541, and its ranking held across three calibration scenarios. Removing the coordinate repetition features cost 74.2 percent of the discriminative margin, while the field confirmation separated flagged from comparison employees completely.
Co-Authors Abdul Latief Arda Erwin, Ibnu Taimiyah G. Gunawan Hazriani Hazriani Hilyatul Auliyah Erwin Ihsan Guntur Muhammad Erwin Rosyadi. S Nashriah Akil Nur Trismi Auliah Syafruddin Syarif Wardi Wardi Yuyun Yuyun