Model Pembeda Sampah Organik dan Anorganik dengan Machine Learning berbasis Phyton
Abstract
This research developed an artificial intelligence-based system to differentiate between organic and inorganic waste using a Convolutional Neural Network (CNN) model. The problem addressed is the community's limited understanding of how to properly separate types of waste, which contributes to environmental pollution. The methodology employed includes data collection of waste images, training the CNN model, and system testing. The test results showed that the model achieved an accuracy of 75%, with a precision of 78% and a recall of 70%. These findings suggest that the developed system can help the community better understand and separate waste more effectively. This research makes a positive contribution to waste management and is expected to serve as a foundation for further development in waste classification technology.
Keywords: Artificial intelligence; Convolutional Neural Network; Waste separation; Environmental management; Machine learning
Abstrak
Penelitian ini mengembangkan sistem berbasis kecerdasan buatan untuk membedakan sampah organik dan anorganik menggunakan model Convolutional Neural Network (CNN). Permasalahan yang dihadapi adalah rendahnya pemahaman masyarakat dalam memisahkan jenis sampah, yang berdampak pada pencemaran lingkungan. Metodologi yang digunakan meliputi pengumpulan data gambar sampah, pelatihan model CNN, dan pengujian sistem. Hasil pengujian menunjukkan bahwa model mencapai akurasi 75%, dengan presisi 78% dan recall 70%. Temuan ini menunjukkan bahwa sistem yang dikembangkan dapat membantu masyarakat dalam memahami dan memisahkan sampah dengan lebih baik. Penelitian ini memberikan kontribusi positif terhadap pengelolaan sampah dan diharapkan dapat menjadi dasar untuk pengembangan lebih lanjut dalam teknologi klasifikasi sampah.
Keywords
References
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