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All Journal Jurnal Teknologi Informasi dan Ilmu Komputer Journal of Information System Jurnal ELTIKOM : Jurnal Teknik Elektro, Teknologi Informasi dan Komputer Indonesian Journal of Artificial Intelligence and Data Mining JOURNAL OF APPLIED INFORMATICS AND COMPUTING JURTEKSI MIND (Multimedia Artificial Intelligent Networking Database) Journal KOMPUTEK Majalah Ilmiah UNIKOM Jurnal Teknologi dan Informasi JATI (Jurnal Mahasiswa Teknik Informatika) JITU : Journal Informatic Technology And Communication Paradigma Jurnal Ilmiah Edutic : Pendidikan dan Informatika Jurnal Inovasi Pendidikan dan Teknologi Informasi (JIPTI) Jurnal Manajemen Sistem Informasi Jurnal Manajemen Informatika, Sistem Informasi dan Teknologi Komputer (JUMISTIK) Jurnal Sistem Informasi, Manajemen dan Teknologi Informasi Jurnal Bisnis Digital dan Enterpreneur (BISENTER)
Rolliawati, Dwi
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Aerospace Engineering Arts Humanities Automotive Engineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Law, Crime, Criminology & Criminal Justice Library & Information Science Mechanical Engineering Social Sciences Other

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Journal : Paradigma

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Classification of Vegetable Types Using the Convolutional Neural Network (CNN) Algorithm Karunia, Wilis Arum; Puspita, Safriya Murni; Rolliawati, Dwi; Yusuf, Ahmad
Paradigma - Jurnal Komputer dan Informatika Vol. 27 No. 1 (2025): March 2025 Period
Publisher : LPPM Universitas Bina Sarana Informatika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31294/p.v27i1.7577

Abstract

This study aims to classify vegetable types using the Convolutional Neural Network (CNN) algorithm with a dataset encompassing 15 vegetable classes and a total of 31,000 images. By utilizing the TensorFlow and Keras libraries, the CNN model was designed with convolutional, pooling, and dense layers to recognize visual features such as color, texture, and shape. The results indicate a highest validation accuracy of 95.83% and a testing accuracy of 93%. This research contributes to the application of the CNN algorithm for image classification and demonstrates its potential in handling multi-class datasets effectively. However, since the vegetables used have very distinct shapes and textures, this study is more relevant in the context of the technical application of the CNN algorithm rather than practical benefits. The research would be more impactful if applied to vegetables with similar shapes and characteristics, thereby supporting farmers or individuals studying vegetable traits in greater depth. Additionally, such an approach could address challenges in differentiating visually similar vegetable types, making the technology more valuable in real-world agricultural or educational settings.
Co-Authors Abdan Kamaludin, Mohammad Rizal Achmad Teguh Wibowo Ahmad Yusuf Amalia, Khofifah Amin, Faris Mushilihul Anang Kunaefi, Anang Ananta, Yulia Eka Andhy Permadi, Andhy Ardhani, Alvina Waihda Bayu Adhi Nugroho Bimantara, M. Dwi Cahya Eko, Anugrah Putra Febianti, Nidia Firmansyah, Afreza Restu Ihsan, Muhammad Sakhil INDRARTO, REYHAN DANY Indri Sudanawati Rozas Izzah, Khoirul Izzuddin, Andik Karunia, Wilis Arum Khalid Khunaefi, Anang Kusuma Wardani, Syafrina Dyah Kusuma, Mukhlis Yoga Ma’arif, Wildan Mufti Mujib Ridwan Nasrullah, Muhammad Alfan Nur Aini, Salsabilla Annafi’u Puspita, Safriya Murni Puspitasari, Arimbi Rahmadini, Dwi Ramadhan, Hafid Ramdhany Alamsyah, Muhammad Rani, Aulia Cahya Retnaningrum, Dea Ayu Riski, Achmad Maulana Rizqi, Nur Irvan Rizqi, Ramadhani Akbaru Salsabila, Diana Sava Salsabila, Nur Diana Fahma Saputra, Achmad Fauzi Sayyida, Anggy Claudy Shofiudin, Muhammad Sona, Mohammad Niko Subhan Nooriansyah Sufairoh, Ifatus UBAIDILLAH, M SYAFIQ Ulumiyyah, Ikrimatul Ulya, Dzakiya Ishmatul Ummah, Syarifatul Umroh, Masfufahtul Wibowo, Raya Akbar Tiar Yuniati, Diah