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All Journal Abdimas Toddopuli: Jurnal Pengabdian Pada Masyarakat Journal of System and Computer Engineering
A. Inayah Auliyah
Institut Teknologi Bacharuddin Jusuf Habibie
Agriculture, Biological Sciences & Forestry Civil Engineering, Building, Construction & Architecture Computer Science & IT Decision Sciences, Operations Research & Management Other

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Implementasi Pembelajaran Berbasis Kecerdasan Buatan Di Upt Sd Negeri 16 Parepare Muhaimin Hading; Radhiansyah Radhiansyah; Nurul Chairunnisa Noor; A. Syahrinaldy syahruddin; A. Inayah Auliyah; Andi Nurfadillah Ali; Muhammad Ikhwan Burhan; Muhammad Irsan
Abdimas Toddopuli: Jurnal Pengabdian Pada Masyarakat Vol. 6 No. 2 (2025): Volume 6, No 2, Juni 2025
Publisher : Universitas Cokroaminoto Palopo

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30605/atjpm.v6i2.6311

Abstract

Pemanfaatan teknologi dalam pendidikan dasar menjadi semakin krusial di tengah perkembangan era digital dan Revolusi Industri 4.0. Kegiatan pengabdian ini dilatarbelakangi oleh pentingnya peningkatan literasi teknologi di lingkungan sekolah dasar, khususnya dalam pemanfaatan kecerdasan buatan (Artificial Intelligence/AI) untuk mendukung proses belajar mengajar yang lebih interaktif dan adaptif. Program ini dilaksanakan di UPT SD Negeri 16 Parepare dengan tujuan utama untuk mengimplementasikan pendekatan pembelajaran berbasis AI. Metode pelaksanaan meliputi pelatihan intensif kepada guru mengenai konsep dan praktik penggunaan AI, penerapan langsung AI dalam kegiatan pembelajaran di kelas, serta evaluasi untuk mengukur dampak kegiatan. Hasil kegiatan menunjukkan adanya peningkatan pemahaman dan keterampilan guru dalam mengintegrasikan AI ke dalam pembelajaran, serta meningkatnya minat dan partisipasi aktif siswa selama proses belajar. Kegiatan ini memberikan kontribusi positif dalam memperkenalkan transformasi digital di lingkungan sekolah dasar, serta berpotensi menjadi model replikasi untuk sekolah lainnya.
Deteksi dan Estimasi Berat Sampah Plastik Berbasis Visi Komputer Andri Dwi Utomo; Mar’atuttahirah Mar’atuttahirah; A. Inayah Auliyah; Muhammad Nur
Journal of System and Computer Engineering Vol 7 No 3 (2026): JSCE: July 2026
Publisher : Universitas Pancasakti

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61628/jsce.v7i3.2755

Abstract

Plastic waste management is an increasingly critical environmental challenge due to the growing volume of waste and the limitations of conventional manual weighing methods, which are inefficient and prone to human error. This study proposes a computer vision-based system for automatic detection, classification, and weight estimation of plastic waste using the YOLOv8n model for object detection and Random Forest Regression for weight estimation. The YOLOv8n model is used to detect and classify seven types of plastic waste based on the Resin Identification Code (RIC), namely PET, HDPE, PVC, LDPE, PP, PS, and OTHER. Subsequently, weight estimation is performed using a Random Forest Regression model based on bounding box features, including width, height, area, aspect ratio, and perimeter. The proposed system is evaluated using an unseen test set to ensure unbiased performance measurement. Experimental results show that the YOLOv8n model achieves a mean Average Precision (mAP@0.5) of 91.93% and mAP@0.5:0.95 of 73.27%, while the Random Forest Regression model achieves an R² score of 95.5% with a Mean Absolute Error (MAE) of 4.28 grams. These results demonstrate that the integration of object detection and regression enables accurate and automatic estimation of plastic waste weight, thereby improving the efficiency and objectivity of waste management systems.
Co-Authors A. Syahrinaldy syahruddin Andi Nurfadillah Ali Andri Dwi Utomo Hading, Muhaimin Maratuttahirah Muhammad Ikhwan Burhan Muhammad Irsan Muhammad Nur Nurul Chairunnisa Noor Radhiansyah Radhiansyah