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All Journal International Journal of Electrical and Computer Engineering HAYATI Journal of Biosciences ComEngApp : Computer Engineering and Applications Journal IJCCS (Indonesian Journal of Computing and Cybernetics Systems) Bulletin of Electrical Engineering and Informatics Indonesian Journal of Electrical Engineering and Informatics (IJEEI) Prosiding SNATIKA Vol 01 (2011) Khazanah Informatika: Jurnal Ilmu Komputer dan Informatika Proceeding of the Electrical Engineering Computer Science and Informatics ILKOM Jurnal Ilmiah Indonesian Journal of Electrical Engineering and Computer Science Journal of Applied Data Sciences
Aina Musdholifah
Department Of Computer Science And Electronics, Universitas Gadjah Mada
Agriculture, Biological Sciences & Forestry Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Earth & Planetary Sciences Electrical & Electronics Engineering Engineering

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Classification of clove types using convolution neural network algorithm with optimizing hyperparamters Firman Tempola; Retantyo Wardoyo; Aina Musdholifah; Rosihan Rosihan; Lilik Sumaryanti
Bulletin of Electrical Engineering and Informatics Vol 13, No 1: February 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v13i1.5533

Abstract

This study uses clove imagery by classifying it according to ISO 2254-2004 standards: whole, headless, and mother clove. This type of clove will affect the quality and economic value when it has been dried. For this reason, it is necessary to take a first step to control cloves' quality. One way is to classify it from the start. This research will utilize the convolution neural network algorithm and compare it with model transfer learning and modified VGG16 architecture on clove images. In addition, research is also looking for the most optimal hyperparameter. The results of this study indicate that the application of convolution neural network (CNN) to clove images obtains an accuracy value of 84% using a hyperparameter of 50 epochs, a learning rate of 0.001, and a batch size of 16. Meanwhile, for the application of transfer learning VGG16, Resnet50, MobileNetV2, InceptionV3, DensetNet151, and modified VGG16 have respectively each of the highest accuracy including 95.70%, 76.15%, 96.89%, 98.07%, 98.96%, and 99.11%.
Co-Authors Abednego Dwi Septiadi Aditya Permana Anastasya Latubessy Anjani, Sarah Anny Kartika Sari Arda Yunianta Arif Nurwidyantoro Br Bangun, Elsi Titasari Edi Faizal Ekki Rinaldi Erick Alfons Lisangan Faisal Ramadhan Hamda, Hizbullah Hananto, Andhika Rafi Hani Febri Mustika Hani Febri Mustika Hartati, Sri Haviluddin Haviluddin Herdiesel Santoso Herlina Jayadiyanti Jayadiyanti, Herlina Lilik Sumaryanti Luthfiansyah Ilhamnanda Yusuf Maulana Ihsan Ahmad Mirwan Ushada Mohd Shahizan Othman Omar Obarukab Norazah Yusof Othman, Mohd Shahizan Rahman Indra Kesuma Rahmat Budiarsa Retantyo Wardoyo Rinaldi, Ekki rosihan, rosihan Sapuletea, Chrissandy Sri Hartati Sri Kusrohmaniah, Sri Sri Mulyana Tempola, Firman Timothy K. Shih Tsuyoshi Okayama Wahyu Srimulyani Yudha Islami Sulistya Yunianta, Arda Yusof, Omar Obarukab Norazah