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TEKNOLOGI: Jurnal Ilmiah Sistem Informasi
ISSN : 20878893     EISSN : 25273671     DOI : -
TEKNOLOGI: Jurnal Ilmiah Sistem Informasi published by the Department of Information Systems Unipdu Jombang. TEKNOLOGI published twice a year, in January and July, TEKNOLOGI includes research in the field of Information Technology Design and Development of Information Systems; Business intelligence; Functions and Organization Management Information Systems; and others. Editors invite lecturers researchers, reviewers, practitioners, industry, and observers to contribute to this journal. The language used in the form of Indonesian and English. TEKNOLOGI is the national scientific journals are open to seeking innovation, creativity and novelty. Either in the form of letters, research notes, Articles, supplemental Articles Articles or reviews in the field of information systems and information technology. TEKNOLOGI aims to achieve state-of-the-art in the theory and application of this field. TEKNOLOGI provide a platform for scientists and academics across Indonesia to promote, share, and discuss new issues and the development of information systems and information technology.
Articles 1 Documents
Search results for , issue "Vol 12, No 1 (2022): January" : 1 Documents clear
Perbaikan klasifikasi sampah menggunakan pretrained Convolutional Neural Network
TEKNOLOGI: Jurnal Ilmiah Sistem Informasi Vol 12, No 1 (2022): January
Publisher : Universitas Pesantren Tinggi Darul 'Ulum (Unipdu) Jombang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26594/teknologi.v0i0.2403


Recycling waste is necessary for the sustainability of a good social life. The recycling process for waste is generally carried out manually and with many types of waste categories. For the waste sorting process to run more effectively and efficiently, automatic waste sorting is required. One example is by using an image-based waste type classification system using deep learning or machine learning algorithms. In previous research, AlexNet and SVM were used as classification algorithms for waste types in 6 different waste categories. The results of this study show that SVM performance is better with an accuracy of 63% compared to AlexNet's performance with an accuracy of 20%. Whereas in general, the CNN algorithm's performance should produce better accuracy than SVM. Based on this, we propose to re-examine the classification of waste types in the same dataset as previous research using the latest CNN algorithm architecture, namely ResNet. More completely, the architectures used are AlexNet, ResNet-18, and ResNet-50, respectively with and without pretrained, so that a total of 6 types of CNN algorithm architecture are used in the training. In this study, the AlexNet architecture is used with a different configuration from previous studies. The test parameter is the number of epochs of 50 epochs with attention to how many epochs the training results have shown convergent. From the training results, the highest accuracy obtained by the ResNet-50 architecture with pretrained is 91.16% and shows convergent results since the 14th epoch. Then for the lowest accuracy obtained by the AlexNet architecture without pretrained, which is 58% and shows convergent results since the 21st epoch.

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