Pendekatan Backpropagation Artificial Neural Network Untuk Prediksi Kemurnian Madu

Authors

  • Andi Muh Ihsanul Tafsir Universitas Sulawesi Barat, Majene, Sulawesi Barat, Indonesia
  • Sulfayanti Sulfayanti Universitas Sulawesi Barat, Majene, Sulawesi Barat, Indonesia
  • Nahya Nur Universitas Sulawesi Barat, Majene, Sulawesi Barat, Indonesia

DOI:

https://doi.org/10.62411/tc.v24i4.14855

Abstract

Madu merupakan produk alami yang kemurniannya menjadi indikator utama kualitas dan keasliannya. Penelitian ini bertujuan untuk memprediksi tingkat kemurnian madu menggunakan algoritma Artificial Neural Network (ANN) dengan metode Backpropagation. Dataset terdiri dari 247.903 data dengan 10 atribut madu yang digunakan sebagai variabel input, sedangkan tingkat kemurnian madu dijadikan sebagai target output. Tahapan penelitian meliputi pra-pemrosesan data, pelatihan model, serta evaluasi hasil prediksi. Setelah melalui tahap pra-pemrosesan, jumlah fitur input bertambah menjadi 27. Pada proses eksperimen, dilakukan pengujian beberapa variasi arsitektur (27-14-14-1, 27-27-27-1, 27-54-54-1), fungsi aktivasi (ReLU, sigmoid biner, sigmoid bipolar), learning rate (0,01, 0,1, 0,5), dan jumlah epoch (1000, 1500, 2000) untuk memperoleh konfigurasi terbaik. Hasil optimal diperoleh pada arsitektur jaringan 27-54-54-1 dengan fungsi aktivasi ReLU, learning rate 0,5, dan jumlah epoch sebanyak 2000. Konfigurasi tersebut menghasilkan kinerja prediksi dengan nilai Mean Squared Error (MSE) 0,000542, R-squared (R²) sebesar 0,972010, dan Mean Absolute Percentage Error (MAPE) 1,26%. Hasil ini membuktikan bahwa algoritma Backpropagation Artificial Neural Network dapat digunakan secara efektif dalam memprediksi tingkat kemurnian madu. Kata Kunci - Artificial Neural Network, Backpropagation, Prediksi, Kemurnian Madu

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Published

2025-11-28

Issue

Vol. 24 No. 4 (2025): November 2025

Section

Articles

License

Copyright (c) 2025 Andi Muh Ihsanul Tafsir, Sulfayanti Sulfayanti, Nahya Nur

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