Identifying Types of Waste as Efforts in Plastic Waste Management Based on Deep Learning

Irawadi Buyung, Agus Qomaruddin Munir, Nurhadi Wijaya, Latifah Listyalina

Abstract


Purpose: This research aims at designing a computer algorithm for automatic waste sorting.

Design/methodology/apprach: This research is quantitative and uses secondary data, specifically images of various types of waste. The images will be classified into organic and inorganic waste types with the assistance of a deep learning model. In this research, we propose the EfficientNet method for Waste Type Identification as an Effort in Plastic Waste Management. Experiments were conducted on a secondary dataset from Kaggle.com, which involved classifying various types of waste into 'Plastic' and 'Non-Plastic' categories, showing the effectiveness of the proposed method.

Findings/result: The measurement is performed to compute the accuracy of the designed deep learning model in classifying waste images into the appropriate waste types. Based on the research results, our system achieved the highest accuracy of 97% during testing.

Originality/value/state of the art: The designed method can perform fast and automatic waste sorting, which is useful in reducing the increasing amount of waste accumulating each year.

 


Full Text:

PDF

References


A. T. Setiawan, “Identifikasi Jenis Sampah Secara Otomatis Menggunakan Metode Convolutional Neural Network (CNN),” Smart Comp, vol. 11, no. 2, 2022.

V. K. A. Md Zahangir Alom, Tarek M. Taha, Christopher Yakopcic, Stefan Westberg, Paheding Sidike, Mst Shamima Nasrin, Brian C Van Esesn, Abdul A S. Awwal, “The History Began from AlexNet: A Comprehensive Survey on Deep Learning Approaches,” Comput. Vis. Pattern Recognit., 2018.

A. Dinda Clasissa Aulia, Harry Kiswanto Situmorang, Ahmad Fauzy Habiby Prasetya, Adhe Fadilla, I. Safira Nisa, Asiyah Khoirunnisa, Deo Farhan, Dwi Nur’aini Nindya, Hanisa Purwantari, R. F. L. Octaviani Dwi Jasmin, Johninda Aulia Akbar, Novi Mesrina Cicionta BR Ginting, and Z. P. G, “Peningkatan Pengetahuan dan Kesadaran Masyarakat tentang Pengelolaan Sampah dengan Pesan Jepapah,” J. Pengabdi. Kesehat. Masy., vol. 1, no. 1, 2021.

L. et al Listyalina, “Deep-RIC: Plastic Waste Classification using Deep Learning and Resin Identification Codes (RIC).,” Telemat. J. Inform. dan Teknol. Inf., vol. 19, no. 2, pp. 215–228, 2022.

D. Fahmi, “Pemilahan Sampah Menggunakan Model Klasifikasi Support Vector Machine Gabungan dengan Convolutional Neural Network,” JURIKOM (Jurnal Ris. Komputer), vol. 10, no. 1, 2023.

W. Budiharto, Machine Learning & Computational Intelligence, Indonesia. 2016.

A. C. Malina, Suhasman, A. Muchtar, and Sulfahri, “KAJIAN LINGKUNGAN TEMPAT PEMILAHAN SAMPAH DI KOTA MAKASSAR,” J. Inov. dan Pelayanan Publik Makassar, vol. 1, no. 1, 2017.

H. S. Stephen Stephen, Raymond Raymond, “Applikasi Convolution Neural Network Untuk Mendeteksi Jenis-jenis Sampah,” Explor. J. Sist. Inf. dan Telemat., 2019.

Y. Kusnaedi, “Eksplorasi Sampah Plastik Menggunakan Metode ‘Heating’ Untuk Produk Pakai,” in Seminar Nasional Itenas, 2018, pp. 11–21.

M. I. Utami and dan D. E. A. F. Ningrum, “Proses Pengolahan Sampah Plastik di UD Nialdho Plastik Kota Madiun,” Indones. J. Conserv., vol. 9, no. 2, pp. 89–95, 2020.

Hendri dkk, “Penerapan Machine Learning Untuk Mengategorikan Sampah Plastik Rumah Tangga,” J. STMIK, vol. X, no. 2, 2022.

L. et al. Listyalina, “Conv-Tire: Tire Condition Assessment using Convolutional Neural Networks,” Telemat. J. Inform. dan Teknol. Inf., vol. 19, no. 3, pp. 323–336, 2022.

dkk. Kohsasih, “Analisis Perbandingan Algoritma Convolutional Neural Network Dan Algoritma Multi-Layer Perceptron Neural Dalam Klasifikasi Citra Sampah,” J. TIMES, vol. X, no. 2, 2022.

I. G. M. A. I Made Pageh, “Solusi Strategis Penangan Masalah Sampah Dengan Mengolah Sampah Dapur Menjadi Pupuk Organik Cair (POC): (Kasus Dua Desa Pinggir Kota di Kota Singaraja Bali,” J. Ilm. Ilmu Sos., vol. 4, no. 2, pp. 175–180, 2018.

Dacipta, “Sistem Klasifikasi Limbah Menggunakan Metode Convolutional Neural Network (CNN)Pada Web Service BerbasisFramework Flask,” JINACS (Journal Informatics Comput. Sci., vol. 3, no. 4, 2022.




DOI: https://doi.org/10.31315/telematika.v20i3.10804

DOI (PDF): https://doi.org/10.31315/telematika.v20i3.10804.g6215

Refbacks

  • There are currently no refbacks.


Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.

Copyright of :
TELEMATIKA: Jurnal Informatika dan Teknologi Informasi
ISSN 1829-667X (print); ISSN 2460-9021 (online)


Dipublikasi oleh
Jurusan Teknik Informatika, UPN Veteran Yogyakarta
Jl. Babarsari 2 Yogyakarta 55281 (Kampus Unit II)
Telp: +62 274 485786
email: jurnaltelematika@upnyk.ac.id

 

Jurnal Telematika sudah diindeks oleh beberapa lembaga berikut:
 

 

 

 

 

Status Kunjungan Jurnal Telematika