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Abstract

Penelitian ini membahas penerapan Edge Artificial Intelligence berbasis neural network pada sistem klasifikasi warna menggunakan sensor TCS3200 dan mikrokontroler Arduino. Penelitian didasari oleh kebutuhan akan sistem klasifikasi warna yang mampu bekerja secara mandiri, akurat, dan real-time pada perangkat embedded system dengan keterbatasan sumber daya komputasi. Tujuannya adalah merancang dan mengimplementasikan model Artificial Neural Network yang ringan namun andal untuk melakukan klasifikasi warna secara langsung di sisi perangkat (edge). Metode yang digunakan meliputi pengambilan dataset warna RGB, normalisasi data agar sesuai dengan fungsi aktivasi sigmoid, serta pelatihan model Multilayer Perceptron (MLP) menggunakan algoritma backpropagation. Arsitektur jaringan terdiri dari tiga neuron input (R, G, B), dua hidden layer dengan masing-masing 12 dan 8 neuron, serta lima neuron output yang merepresentasikan kelas warna. Proses pelatihan dilakukan menggunakan 100 data latih dengan learning rate sebesar 0,1 dan menghasilkan nilai Mean Squared Error (MSE) sebesar 0,0099. Evaluasi sistem menggunakan 50 data uji menunjukkan tingkat akurasi klasifikasi mencapai 100% pada seluruh kelas warna. Hasil penelitian ini menunjukkan bahwa pendekatan Edge AI berbasis neural network efektif diterapkan pada sistem klasifikasi warna berbasis embedded system dengan performa yang stabil dan andal.

Keywords

Edge Artificial Intelligence Klasifikasi Warna Artificial Neural Network Embedded System Sensor TCS3200

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Copyright (c) 2026 Wahyu Ramadhani Gusti, Nurfauziah

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This work is licensed under a Creative Commons Attribution 4.0 International License.

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