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All Journal INTI Nusa Mandiri Al-Buhuts (e-journal) Ceddi Journal of Information System and Technology (JST)
Imran Taufik
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Computer Science & IT Decision Sciences, Operations Research & Management Economics, Econometrics & Finance

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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.
Internet of Things (IoT) Based Air Pollution Detector for Baby Rooms Najmawatih; Imran Taufik; Supriadi; Anders Christensen
Ceddi Journal of Information System and Technology (JST) Vol. 4 No. 1 (2025): April
Publisher : Yayasan Cendekiawan Digital Indonesia (CEDDI)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56134/jst.v4i1.111

Abstract

Indoor air pollution is a leading cause of respiratory illnesses in infants and children, potentially resulting in severe health outcomes, including death. Common sources include dust, cigarette smoke, cleaning chemicals, and hazardous gases such as carbon monoxide (CO) and nitrogen dioxide (NO₂), particularly in enclosed, air-conditioned (AC) environments. Due to the difficulty of detecting pollutants like fine particulate matter (PM2.5) and CO, an effective, real-time monitoring solution is crucial. This study aims to design and develop an Internet of Things (IoT)-based device capable of monitoring PM2.5, CO, temperature, and humidity, specifically in infant rooms. The system integrates an ESP32 microcontroller with DSM501a, MQ-7, and DHT22 sensors and features automated alerts via a Telegram bot when pollutant levels exceed predefined thresholds. The device was evaluated through a comparative 24/7 testing method over seven days against commercially available standard instruments. Results show a relative error of 25% for PM2.5, 30% for CO, and significantly lower errors for temperature (2%) and humidity (0%). Sensor data is processed and transmitted to the Thingspeak server for real-time graphical monitoring. The Telegram alert feature demonstrated an average response time of 1.84 seconds across 20 tests. The findings suggest that the proposed device offers a viable, accessible, and responsive solution for indoor pollutant detection, contributing to improved air quality monitoring and early warning systems to protect vulnerable populations, especially infants.
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.
Co-Authors Abdul Latief Arda Anders Christensen Erwin, Ibnu Taimiyah Hilyatul Auliyah Erwin Ihsan Guntur Misbahuddin Misbahuddin Muhammad Erwin Rosyadi. S Najmawatih Nashriah Akil Supriadi