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Systematic Literature Review Penelitian Hydrothermal Cangkang Telur Ayam dengan Analisis Bibliometrik Menggunakan Software VOSviewer Agusty, Muhammad Naufal; Etih Hartati; Djaenudin; Herlian Eriska Putra
Jurnal Serambi Engineering Vol. 11 No. 1 (2026): Januari 2026
Publisher : Faculty of Engineering, Universitas Serambi Mekkah

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Abstract

Food waste management is a global challenge that encourages the application of the circular economy concept through resource reuse. Chicken eggshell waste has the potential to be used as a renewable source of calcium, while hydrothermal processing is a promising method for converting it into value-added products. This study aims to map the development of hydrothermal processing research on chicken eggshell waste through a bibliometric analysis of 75 Scopus-indexed publications from 2019 to 2024. The results of the analysis show that China is the country with the highest number of publications. Co-authorship analysis identified 27 collaborating authors, with Zhang, X., Yin, J., and Wang, I. as the main authors. The most dominant keywords were “hydroxyapatite,” “hydrothermal method,” and “calcium.” This study shows that hydrothermal research focuses on the development of calcium-based biomaterials and nanomaterials and opens up opportunities for further research on the sustainable use of chicken eggshell waste.
Real-time deep neural network-based waste detection and classification using a camera sensor Darlis, Arsyad Ramadhan; Lidyawati, Lita; Kristiana, Lisa; Hartati, Etih; Trisani, Faradilla Rizqi
SINERGI Vol 30, No 1 (2026)
Publisher : Universitas Mercu Buana

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Abstract

Waste generation is a growing environmental concern, with manual sorting methods often being inefficient and error-prone, particularly under varying lighting and environmental conditions. In Indonesia, waste is typically categorized into organic and nonorganic, yet existing automated classification systems lack real-time capabilities and robustness in dynamic settings. This study proposes a novel real-time waste detection and classification system using a deep neural network, implemented on the Jetson Nano platform with a camera sensor. The system utilizes the ResNet-18 convolutional neural network architecture and is developed using Python. It is designed to distinguish between organic and nonorganic waste in real-time. Training was conducted over 30 epochs, and the system was tested under various lighting conditions—morning, daytime, afternoon, and nighttime. Results show high accuracy: 95.24% in the morning, 95.24% during the day, 90.45% in the afternoon, and 86.90% at night, with an average accuracy of 91.96%. Performance was influenced by factors such as lighting intensity, distance, waste position, changes in organic waste, and occlusion by plastic. The proposed system offers a significant improvement over traditional and existing methods by enabling accurate, real-time waste classification under diverse conditions, contributing to more efficient and intelligent waste management.