Work

Identifikasi Tingkat Kematangan Buah Tomat Melalui Warna dengan Penerapan Jaringan Saraf Tiruan (JST)

Year: 2024

Type: article

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%).

Source: Polygon Jurnal Ilmu Komputer dan Ilmu Pengetahuan Alam

DOI: 10.62383/polygon.v2i6.292

Authors: Nazwa Alya Faradita, Lailan Sofinah Harahap

Institutions: Universitas Islam Negeri Sumatera Utara, Universitas Muhammadiyah Sumatera Utara

Language: English

Topic: Smart Agriculture and AI

Subfield: Plant Science

Field: Agricultural and Biological Sciences

Domain: Life Sciences

SDG: Zero hunger

Open Access status: hybrid

OpenAlex ID: W4404619170

FWCI:

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