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All Journal Jurnal CoreIT Krea-TIF: Jurnal Teknik Informatika JOISIE (Journal Of Information Systems And Informatics Engineering) Building of Informatics, Technology and Science Jurnal Daur Lingkungan Jurnal Computer Science and Information Technology (CoSciTech) Journal of Community Development JURNAL ILMU DAN TEKNOLOGI KESEHATAN TERPADU Malcom: Indonesian Journal of Machine Learning and Computer Science JURNAL SANITASI LINGKUNGAN Journal Of Artificial Intelligence And Software Engineering Jurnal Indonesia : Manajemen Informatika dan Komunikasi
Iwan Iskandar
Jurusan Kesehatan Lingkungan, Poltekkes Kemenkes, Tanjungpinang, Kepulauan Riau,, 29124
Computer Science & IT Control & Systems Engineering Environmental Science Health Professions Languange, Linguistic, Communication & Media Library & Information Science Nursing Public Health Social Sciences

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Journal : Building of Informatics, Technology and Science

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Klasifikasi Kematangan Buah Mangga Menggunakan Pendekatan Deep Learning Dengan Arsitektur DenseNet-121 dan Augmentasi Data Permata, Rizkiya Indah; Yanto, Febi; Budianita, Elvia; Iskandar, Iwan; Syafria, Fadhilah
Building of Informatics, Technology and Science (BITS) Vol 6 No 1 (2024): June 2024
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v6i1.5381

Abstract

Mango is a seasonal fruit in Indonesia. In lowland areas and hot climates, this mango plant can grow abundantly. People who use mangoes generally focus more on the characteristics of the fruit which require a more precise classification to be more certain. Traditional classifications sometimes fail to properly articulate maturity criteria. This research classifies mango ripeness using a deep learning approach with densenet-121 architecture, parameters, learning rate, dropout, and data augmentation. Augmentation is the process of changing or modifying an image in such a way that the computer will detect that the image has been changed is the same picture. The original dataset was 895 data, after being augmented it became 1790 data consisting of three classes, namely ripe mango, young mango, and rotten mango. The test compares the original data and the original data added with augmentation. Accuracy using original data is 95.95%. Meanwhile, using original data combined with augmentation gets an accuracy of 99.73%
Co-Authors Afriyanti, Liza Agustian, Surya Agustina, Auliyah Ahmad, Rizmah Zakiah Nur Alvinur Hidayat, Alvinur Amelia, Felina Ananda, Sherly Arasy, Abdurrahman Aulia Ramadhani Elin Haerani Elvia Budianita Fadhilah Syafria Fauziyyah, Laila Nurul Febi Yanto, Febi Fitri Insani Gusti, Siska Kurnia Handayani, Lala Syafitri Herdiana, Dora Idris, M. Fadhil Irsyad (Scopus ID: 57204261647), Muhammad Jasril Jasril Kholilah Samosir Lawami, Reza Haya Lestari Handayani Mai Candra, Reski MF, M.Yusuf Muhammad Affandes, Muhammad Muhammad Irsyad Muliani, Sarifah Nazir, Alwis Nazruddin Safaat Nazruddin Safaat H Negara, Benny Sukma Permata, Rizkiya Indah Purnama, Irgi Putri. P, Zurneli Kurnia Reski Mai Candra Ridho Hilmawan, Ridho rizkaramdhaniartie, siti Sandi, Wahyu Ari Sofiyah, Wan Suwanto Sanjaya Tri Prastio Nugroho Yuli Novita Sari, Yuli Novita Yusra Yusra, Yusra Zulya Erda