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All Journal EXPERT: Jurnal Manajemen Sistem Informasi dan Teknologi Jurnal Pseudocode Jurnal Pengabdian Kepada Masyarakat Sakai Sambayan Jurnal Informatika dan Teknik Elektro Terapan agriTECH Jurnal Kreativitas PKM Barometer International Journal of Aviation Science and Engineering International Journal of Electronics and Communications Systems Jurnal Inovasi Pembangunan Journal of Applied Science, Engineering and Technology (J. ASET) Malcom: Indonesian Journal of Machine Learning and Computer Science Seminar Nasional Insinyur Profesional (SNIP) Prosiding Seminar Nasional Hasil Penelitian Sains, Teknik dan Aplikasi Industri Fakultas Teknik Universitas Lampung Electrician : Jurnal Rekayasa dan Teknologi Elektro Jurnal Teknologi Riset Terapan
Mulyani, Yessi
Universitas Lampung
Aerospace Engineering Agriculture, Biological Sciences & Forestry Humanities Automotive Engineering Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Earth & Planetary Sciences Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Environmental Science Health Professions Industrial & Manufacturing Engineering Mathematics Mechanical Engineering Medicine & Pharmacology Nursing Public Health Social Sciences Transportation Other

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Journal : International Journal of Aviation Science and Engineering

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Systematic Comparison of Machine Learning Model Accuracy Value Between MobileNetV2 and XCeption Architecture in Waste Classification Syste Yessi Mulyani; Rian Kurniawan; Puput Budi Wintoro; Muhammad Komarudin; Waleed Mugahed Al-Rahmi
AVIA Vol. 4, No. 2 (December 2022)
Publisher : Institut Teknologi Bandung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47355/avia.v4i2.70

Abstract

Garbage generated every day can be a problem because some types of waste are difficult to decompose so they can pollute the environment. Waste that can potentially be recycled and has a selling value is inorganic waste, especially cardboard, metal, paper, glass, plastic, rubber and other waste such as product packaging. Various types of waste can be classified using machine learning models. The machine learning model used for classification of waste systems is a model with the Convolutional Neural Network (CNN) method. The selection of the CNN architecture takes into account the required accuracy and computational costs. This study aims to determine the best architecture, optimizer, and learning rate in the waste classification system. The model designed using the MobileNetV2 architecture with the SGD optimizer and a learning rate of 0.1 has an accuracy of 86.07% and the model designed using the Xception architecture with the Adam optimizer and a learning rate of 0.001 has an accuracy of 87.81%.
Systematic Comparison of Machine Learning Model Accuracy Value Between MobileNetV2 and XCeption Architecture in Waste Classification System Mulyani, Yessi; Kurniawan, Rian; Budi Wintoro, Puput; Komarudin, Muhammad; Mugahed Al-Rahmi, Waleed
International Journal of Aviation Science and Engineering - AVIA Vol. 4, No. 2 (December 2022)
Publisher : FTMD Institut Teknologi Bandung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47355/avia.v4i2.70

Abstract

Garbage generated every day can be a problem because some types of waste are difficult to decompose so they can pollute the environment. Waste that can potentially be recycled and has a selling value is inorganic waste, especially cardboard, metal, paper, glass, plastic, rubber and other waste such as product packaging. Various types of waste can be classified using machine learning models. The machine learning model used for classification of waste systems is a model with the Convolutional Neural Network (CNN) method. The selection of the CNN architecture takes into account the required accuracy and computational costs. This study aims to determine the best architecture, optimizer, and learning rate in the waste classification system. The model designed using the MobileNetV2 architecture with the SGD optimizer and a learning rate of 0.1 has an accuracy of 86.07% and the model designed using the Xception architecture with the Adam optimizer and a learning rate of 0.001 has an accuracy of 87.81%.
Co-Authors Adhi Nurhartanto, Adhi Ageng Sadnowo Repelianto Agus Haryanto Agustina, Indria Ardi Ragil Saputra Arifudin, M. Bagus Budi Wintoro, Puput Cahyana, Amanda Hasna Deni Achmad Djausal, Gita Paramita Dzihan Septiangraini Efendi, Ujang Eliza Hara Fajriansyah, Gilang Gigih Forda Nama Gilang Fajriansyah Gita Paramitha Djausal Gusti, Khalid Surya Halim Abdillah Sholeh Helmy Fitriawan Hery Dian Saptama Hery Dian Septama Hery Dian Septama Hilmi Hermawan Huda, Zulmiftah Ilim, Ilim Irza Sukmana Jaya, Winaldi Putra Khalid Surya Gusti Komarudin, M. lina marlina, lina M. Bagus Arifudin Mahendra Pratama MARDIANA Mardiana Mardiana Mareli Telaumbanua Martinus Martinus Martinus, Martinus Meizano Ardhi Muhammad Meizano Ardi Muhamad Mona Arif Muda Mugahed Al-Rahmi, Waleed Muhamad Komarudin Muhamad Komarudin Muhamad Komarudin Muhammad Amin Muhammad Komarudin Muhammad Komarudin Muhammad, Meizano Ardhi Nanda Sazqiah Nyoman Herman Ardike Panji Kurniawa Pratama, Rama Wahyu Ajie Puput Budi Wintoro Puput budi wintoro Puput Budi Wintoro, Puput Budi Putri, Renatha Amelia Manggala Rafi'syaiim, Muhammad Afif Ragil Saputra, Ardi Reza Dwi Permana Rhomadhona, Nazmah Wulan Rian Kurniawan Rian Kurniawan Septiangraini, Dzihan Shalihah , Atiqah Hanifah Sony Ferbangkara Sugeng Triyono Titin Yulianti Trisya Septiana Umi Murdika Wahyu Aji Pulungan Wahyu Eko Sulistiono Waleed Mugahed Al-Rahmi Wulan Rahma Izzati