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All Journal Jurnal FASILKOM (teknologi inFormASi dan ILmu KOMputer) Jurnal Pengabdian Masyarakat (ABDIRA)
Safitri, Arisanti Dwi
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Aerospace Engineering Computer Science & IT Decision Sciences, Operations Research & Management Languange, Linguistic, Communication & Media Nursing Social Sciences Other

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Implementation Of Machine Learning To Identify Types Of Waste Using CNN Algorithm Haqqi, Matsnan; Rochmah, Lailatur; Safitri, Arisanti Dwi; Pratama, Rizki Adhi; Tarwoto
JURNAL FASILKOM Vol. 14 No. 3 (2024): Jurnal FASILKOM (teknologi inFormASi dan ILmu KOMputer)
Publisher : Unversitas Muhammadiyah Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37859/jf.v14i3.8116

Abstract

Waste management remains a significant challenge globally, particularly in Indonesia, where the annual waste generation reached 24.67 million tonnes in 2021, with only 50.43% properly managed. To address the issue of mixed organic and inorganic waste and the lack of public awareness regarding waste separation, this study applied machine learning, specifically the Convolutional Neural Network (CNN) algorithm, to classify waste types. The research aimed to develop an effective automated waste classification model to improve waste management processes. The research involved collecting a dataset of 2,848 images representing six waste categories: glass, cardboard, paper, metal, organic, and plastic. Preprocessing techniques such as cropping, noise reduction with Gaussian filters, and data augmentation were applied to enhance data quality. The dataset was divided into training, validation, and testing subsets in a 70:20:10 ratio. The CNN model employed feature extraction through convolution, activation, and pooling layers, followed by classification using a fully connected layer and a softmax function. Model performance was evaluated using accuracy, precision, recall, and F1-score metrics. The model achieved an overall accuracy of 95%, with an average precision, recall, and F1-score of 0.95 across all classes. These results demonstrate the CNN model’s ability to reliably classify waste types. Compared to previous studies, this research achieved higher accuracy through the use of enhanced preprocessing and CNN optimization. This study highlights the potential of CNN-based models for automated waste classification, contributing to sustainable waste management practices and fostering environmental awareness in the future research.
Meningkatkan Literasi dan Numerasi dengan Memadukan Teknologi pada Program Kampus Mengajar 6 di SD N 2 Tamansari Safitri, Arisanti Dwi; Yunita, Ika Romadoni; Ajeng Widiawati, Chyntia Raras
Jurnal Pengabdian Masyarakat (ABDIRA) Vol 4, No 2 (2024): Abdira, April
Publisher : Universitas Pahlawan Tuanku Tambusai

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31004/abdira.v4i2.445

Abstract

The Teaching Campus is an MBKM (Free Learning Campus) program organized by the Ministry of Education and Culture with the aim of providing opportunities for students to learn and develop themselves through activities outside of lectures. This program gives students the opportunity to become teacher partners, encourage learning innovation, and develop literacy, numeracy, and technology adaptation in the 3T area. Focus is also given to efforts to increase people's interest in reading and the importance of literacy and numeracy, including the obstacles faced in its implementation. This research aims to revive the school literacy movement through literacy and numeracy familiarization activities. The research results show that technical training, understanding material concepts, and introducing technology are the main focuses of mentoring. The implementation of the Literacy and Numeracy Minimum Competency Assessment (AKM) with technological devices provides positive results, including increasing understanding of numeracy material, individual development, motivation and self-confidence of students. This research provides a positive picture of the potential for improving the quality of basic education through the Teaching Campus program.
Co-Authors Ajeng Widiawati, Chyntia Raras Haqqi, Matsnan Pratama, Rizki Adhi Rochmah, Lailatur Tarwoto Yunita, Ika Romadoni