📅 05 November 2024
DOI: 10.62383/polygon.v2i6.292
Identifikasi Tingkat Kematangan Buah Tomat Melalui Warna dengan Penerapan Jaringan Saraf Tiruan (JST)
Polygon : Jurnal Ilmu Komputer dan Ilmu Pengetahuan Alam
Asosiasi Riset Ilmu Matematika dan Sains Indonesia (ARIMSI)
📄 Abstract
The selection of agricultural and plantation products often relies on human perception of fruit color. Manual identification through visual observation has several drawbacks, such as time consumption, fatigue, and varying perceptions of quality. Digital image processing technology enables automatic sorting of products. This study applies the Perceptron learning method to identify tomato ripeness. Tomato images are captured using a webcam, analyzed through color histograms, and identified using artificial neural networks. The identification success rate reaches 43.33%, with outputs categorized as Unripe (10%), Half-Ripe (6.66%), and Ripe (26.66%).
🔖 Keywords
#Tomato Ripeness; Histogram; Perceptron; Artificial Neural Networks
ℹ️ Informasi Publikasi
Tanggal Publikasi
05 November 2024
Volume / Nomor / Tahun
Volume 2, Nomor 6, Tahun 2024
📝 HOW TO CITE
Nazwa Alya Faradita; Lailan Sofinah Harahap, "Identifikasi Tingkat Kematangan Buah Tomat Melalui Warna dengan Penerapan Jaringan Saraf Tiruan (JST)," Polygon : Jurnal Ilmu Komputer dan Ilmu Pengetahuan Alam, vol. 2, no. 6, Nov. 2024.
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