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ANALISIS DAMPAK PENGGUNAAN GENERATIVE AI (CHATGPT) TERHADAP INTEGRITAS AKADEMIK MAHASISWA DI PENDIDIKAN TINGGI Parhusip, Jadiaman; Agustin, Olga Noviola; Ferdinand, Bryan Desmonda; Saputra, Rendy; Rangin, Adriel Eleazar
TRANSFORMASI Vol 21, No 2 (2025): TRANSFORMASI
Publisher : STMIK BINA PATRIA

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56357/jt.v21i2.473

Abstract

The rapid adoption of Generative AI, such as ChatGPT, in higher education has sparked significant debate regarding its impact on academic integrity. This research aims to analyze the ethical and social impacts of using Generative AI on student academic integrity. This study uses the Systematic Literature Review (SLR) method. Literature was searched from databases like Google Scholar and Scopus published between 2022 and 2025. After applying inclusion and exclusion criteria, 10 relevant articles were selected for synthesis. The results identify several key themes: (1) Increased risk of novel forms of plagiarism and academic dishonesty, (2) a potential decline in critical thinking and original writing skills, (3) the urgent need for new institutional policies and ethical guidelines, and (4) the dual role of AI as both a learning aid and a sophisticated cheating tool. This study concludes that while Generative AI offers potential as a learning aid, its unregulated use poses a significant threat to traditional academic integrity.
PENGEMBANGAN APLIKASI ESTIMASI KALORI MAKANAN BERBASIS CITRA DENGAN PENDEKATAN DETEKSI OBJEK MENGGUNAKAN YOLO Supriyadi, Rizqy; Irfan, Muhamad; Hapijar, Rizki Dwi; Abubakar, Fadil; Saputra, Rendy; Supriyanto, Kus; Dwiantara, Raihan Putra; Nainggolan, Esron Rikardo; Brawijaya, Herlambang
Jurnal Informatika dan Teknik Elektro Terapan Vol. 14 No. 1 (2026)
Publisher : Universitas Lampung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23960/jitet.v14i1.8545

Abstract

Penelitian ini mengembangkan aplikasi estimasi kalori makanan berbasis citra untuk membantu pengguna memantau asupan energi secara praktis melalui foto ponsel. Sistem menggunakan deteksi objek YOLOv8n untuk mengenali makanan Indonesia dan memetakan tiap deteksi ke parameter nutrisi guna menghitung massa dan kalori. Dataset pelatihan berisi 3.772 citra pada 9 kelas makanan (dibagi 80% latih, 10% validasi, 10% uji). Model dilatih selama 100 epoch pada resolusi 640 piksel menggunakan optimizer AdamW dan early stopping. Backend FastAPI dalam lingkungan Docker menjalankan inferensi dan perhitungan kalori berdasarkan data nutrisi tiap kelas. Aplikasi mobile Flutter mengirim citra ke endpoint /predict dan menampilkan makanan terdeteksi beserta confidence, estimasi massa, dan total kalori. Hasil uji menunjukkan performa deteksi tinggi dengan mAP@0.5 0,975, sementara kesalahan terbesar terjadi pada kelas yang mirip secara visual atau minim data. Temuan ini menegaskan bahwa sistem end-to-end mampu mengestimasi kalori otomatis dari satu foto dan layak dikembangkan lebih lanjut dengan menambah kelas dan menyeimbangkan dataset.