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Utarianingrum, Ratna
Politeknik ATK Yogyakarta
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Development of a real-time plastic waste detection system based on deep learning to support the automation of industrial waste sorting processes Listyalina, Latifah; Sarisky, Mario; Arifin, Uma Fadzilia; Utarianingrum, Ratna; Chandra, Hekin Irfan
OPSI Vol 18 No 2 (2025): OPSI - December 2025
Publisher : Jurusan Teknik Industri, Fakultas Teknologi Industri UPN "Veteran" Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31315/opsi.v18i2.15682

Abstract

The accumulation of plastic waste has become one of the major environmental issues in Indonesia, where conventional waste management systems are still limited in handling and classifying various types of waste. This research aims to develop an automatic waste detection system using Artificial Intelligence (AI) and implement it in a mobile application capable of identifying plastic waste in real time. The model was trained using the WasteIn dataset, which contains annotated images of different waste categories, including plastic, paper, glass, metal, organic, and electronic waste. The YOLO11-Nano architecture was applied due to its lightweight structure and efficiency for mobile-based deployment. The trained model was then converted into TensorFlow Lite (TFLite) format and integrated into an Android Studio environment to enable real-time inference through smartphone cameras. Based on the evaluation of 36 test images, the system achieved an accuracy of 91.67%, with consistent performance in detecting plastic, paper, and organic waste. The inference time of less than 100 milliseconds per frame demonstrates the system’s feasibility for real-time mobile applications. The results indicate that the integration of deep learning and computer vision technologies can effectively support waste classification processes and contribute to sustainable waste management practices.
VALIDASI EKSPERIMENTAL OSCILLATING DISK RHEOMETER DALAM PENENTUAN WAKTU VULKANISASI DAN ANALISIS KINETIKA VULKANISASI KOMPON KARET E. Ratna Utarianingrum; Andri Saputra
Warta Perkaretan Vol. 45 No. 1 (2026): Volume 45, Nomor 1, Tahun 2026
Publisher : Pusat Penelitian Karet - PT. Riset Perkebunan Nusantara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22302/ppk.wp.v45i1.1267

Abstract

Pengendalian optimum cure time (tc90) merupakan parameter kritis dalam penjaminan mutu dan efisiensi proses vulkanisasi karet, namun validasi metodologis serta integrasi hasil laboratorium dengan kondisi industri masih terbatas. Penelitian ini bertujuan untuk memvalidasi penggunaan Oscillating Disk Rheometer (ODR) sebagai alat penentuan parameter vulkanisasi sekaligus mengevaluasi kinetika reaksi berbasis pendekatan Arrhenius pada variasi suhu. Pengujian dilakukan sesuai ASTM D2084 pada 150 °C untuk studi validasi melibatkan empat analis independen, dengan evaluasi keterulangan dan keterbandingan menggunakan analisis Z-score. Analisis kinetika dilakukan pada rentang suhu 140-160°C melalui parameter scorch time (ts2), tc90, dan cure rate index (CRI). Hasil menunjukkan bahwa metode ODR memiliki reliabilitas tinggi dengan dominasi |Z| ≤ 2 tanpa indikasi bias sistematis. Peningkatan suhu secara konsisten menurunkan tc90 dan ts2 serta meningkatkan CRI, menunjukkan percepatan laju reaksi yang mengikuti hubungan linier dalam plot Arrhenius. Evaluasi peran tekanan menunjukkan bahwa tekanan berkontribusi terhadap keseragaman derajat vulkanisasi melalui peningkatan efisiensi transfer panas, namun tidak memengaruhi konstanta laju reaksi secara langsung. Integrasi parameter laboratorium dengan simulasi kondisi industri mengindikasikan bahwa data ODR dapat digunakan sebagai dasar penetapan cure cycle yang lebih presisi dan terkontrol. Studi ini menegaskan kontribusi metodologis dan kinetika dalam menjembatani pengujian laboratorium dengan praktik produksi industri karet.
Co-Authors Andri Saputra Chandra, Hekin Irfan Listyalina, Latifah Mario Sarisky Dwi Ellianto Uma Fadzilia Arifin, Uma Fadzilia