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All Journal Techno.Com: Jurnal Teknologi Informasi Jurnal INSYPRO (Information System and Processing) Journal TEPAT: Teknologi Terapan untuk Pengabdian Masyarakat Jurnal Penelitian Enjiniring Fakultas Teknik Unhas JURNAL SIPISSANGNGI: Jurnal Pengabdian Kepada Masyarakat INOVASI: Jurnal Hasil Pengabdian Masyarakat Jurnal Venus Teknovokasi : Jurnal Pengabdian Masyarakat
Bustamin, Anugrayani
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Agriculture, Biological Sciences & Forestry Humanities Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Environmental Science Library & Information Science Mechanical Engineering Public Health Social Sciences Transportation Other

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Journal : Jurnal INSYPRO (Information System and Processing)

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SISTEM MULTI KLASIFIKASI SAMPAH ANORGANIK DENGAN MENGGUNAKAN TRANSFER LEARNING Bustamin, Anugrayani; Zaman, Baizul; Hakim, Fadhil Khusnul
Jurnal INSYPRO (Information System and Processing) Vol 8 No 2 (2023)
Publisher : Prodi Sistem Informasi UIN Alauddin

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24252/insypro.v8i2.42385

Abstract

Waste management is one of the main challenges faced by society today in efforts to maintain environmental cleanliness and protect natural resources. Rapid population growth, lifestyle changes, and increased consumption have led to an increase in the volume of waste generated. Therefore, it is important to develop effective solutions for managing waste in order to achieve a clean and sustainable environment. Good waste management requires knowledge of waste classification, and with the help of Artificial Intelligence (AI), the process of waste classification can be done effectively. Therefore, this study aims to classification of 10 types of inorganic waste, including (battery, biological, cardboard, clothes, glass, metal, paper, plastic, shoes, and trash), using a Convolutional Neural Network (CNN) model designed with the ResNet-50 architecture. The training results of the ResNet-50 model with Adam optimizer and a learning rate of 0.00005 achieved an accuracy of 97.73%, indicating that it can effectively classify inorganic waste types.
Co-Authors Adnan Adnan Ahmad, Husnul Hatima Amil Ahmad Ilham Amirullah, Indrabayu Andina Setyawati, Andina Areni, Intan Sari Dh Pasha, Sartika Sari Wardanhi Fattah, Muh Shobur Furqan, Rahmatul Gosal, Angela Hervina Hakim, Fadhil Khusnul Ifwan, Ichsanul Al Indrabayu Indrabayu Ingrid Nurtanio Leonard, Calvin Rinaldy Mahdaniar, Mahdaniar Marindah, Tyanita Puti Mokobombang, Novy Nur R A Musyfirah, Kamtina Nur Hikmah Nurdin, Arliyanti Nuurhidayat Jafar Paundu, Ady Wahyudi Prayogi, A. Ais Rahayu, Mesra RIFALDI, AHMAD Sappewali, Andi Farhan Warandhi, Sartika Yohannes, Christoforus Zulkifli Tahir