A Good Accuracy in Apple Fruits Quality Based on Back Propagation Neural Network and Feature Extraction

Authors

DOI:

https://doi.org/10.31289/jite.v6i1.6938

Keywords:

Kualitas, apel, BPNN, RGB

Abstract

Apel merupakan varietas buah yang memounyai banyak jenis. Aple ang secara visual tampak sama, bisa jadi merupakan variates yang berbeda misalnya jenis apel Grany smith, Apel golden dan apel malang. Ketiga apel tersebut sama-sama berwarna hijau. Secara kasat mata warna saja tidak cukup untuk mengklasifikasi, sehingga perlu adanya pemnafaatan teknologi yang dapat membantu proses klasifikasi menjadi lebih akurat. Pengolahancitra digital, lewat proses segmentasi warna menggunakan algoritma Back Propagation Neural Network (BPNN) dapat digunakan untuk proses klasifikasi jenis apel. Dalam penelitian ini digunakan 12 macam apel yaitu apel Golden, apel Grany Smith, apel Braeburn, apel Red delicious, apel Malang, apel Red Yellow, apel Red, apel Anna, apel Golden Delicious, apel Fuji, apel Gala, dan aepl Honeycrisp. Pemnafaatan fitur ekstraksi warna dan ciri membuktikan bahwa akurasi dapat mencapai nilai optimal hingga 93%., presisi 94% dan recal 94% dengan menggunakan ekstraksi fitur RGB

Author Biographies

Ajib Susanto, Universitas Dian Nuswantoro

Teknik Informatika

Ibnu Utomo Wahyu Mulyono, Universitas Dian Nuswantoro

Teknik Informatika

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Published

2022-07-21