PORTABLE NEAR INFRARED SPECTROMETER DENGAN SENSOR AS7263 UNTUK PENDUGAAN SIFAT KIMIA JERUK SIAM (CITRUS NOBILIS) SECARA NON-DESTRUKTIF

Susanto B. Sulistyo, Arief Sudarmaji, Siswantoro Siswantoro, Agus Margiwiyatno, Masrukhi Masrukhi, Asna Mustofa, Rifah Ediati, Riana Listanti, Hety Handayani Hidayat

Abstract


ABSTRAK

Evaluasi mutu buah jeruk secara umum masih dilakukan secara destruktif. Penelitian ini bertujuan untuk memprediksi kandungan kimia buah jeruk siam secara non-destruktif menggunakan Near Infrared Spectrometer portable dengan sensor AS7263 dan aplikasi Neural Network Ensemble (NNE) dengan genetic algorithm (GA) untuk optimasi. Keluaran dari enam channel NIRS portable digunakan sebagai input NNE. NNE yang dikembangkan terdiri atas empat buah Backpropagation Neural Network (BPNN) dengan dua buah lapisan tersembunyi dan kombinasi transfer function yang berbeda-beda. Keluaran dari keempat BPNN ini digabung untuk menghasilkan keluaran NNE yang baru dan dioptimasi menggunakan GA. Karakteristik kimia buah jeruk yang diestimasi adalah total padatan terlarut (TPT) dan vitamin C. Hasil penelitian menunjukkan bahwa akurasi estimasi NNE lebih tinggi dibandingkan akurasi sebuah BPNN tunggal. Estimasi kadar TPT buah jeruk siam menggunakan NNE berbasis GA tergolong sangat akurat dengan nilai Mean Absolut Percentage Error (MAPE) 8,04%. Adapun estimasi kadar vitamin C menggunakan NNE berbasis GA tergolong akurat dengan MAPE sebesar 11,02%. Namun demikian, hasil penelitian ini masih perlu dilanjutkan untuk mengetahui performansi alat yang dikembangkan untuk memprediksi mutu internal jeruk varietas lain yang berbeda karakteristik fisikokimianya.

ABSTRACT 

In general, the evaluation of the quality of citrus is still carried out destructively. This study aimed to predict the chemical characteristics, i.e. Total Soluble Solids (TSS) and vitamin C, of Siamese citrus non-destructively using a portable Near Infrared Spectrometer (NIRS) with the AS7263 sensor and the application of the neural network ensemble (NNE) with a Genetic Algorithm (GA) for optimization. The outputs of the six portable NIRS channels were used as predictors of the NNE. The developed NNE consisted of four backpropagation neural networks (BPNN) with two hidden layers and different combinations of transfer functions. The outputs of the four BPNNs were combined to produce new NNE outputs and were then optimized using GA. The results showed that the NNE estimation accuracy was higher than that of a single BPNN. The estimation of TSS content of Siamese citrus using GA-optimized NNE was classified as very accurate with a Mean Absolute Percentage Error (MAPE) of 8.04%. The estimation of vitamin C using GA-optimized NNE was classified as accurate with a MAPE of 10.01%. However, the results of this study still need to be continued to determine the performance of the instrument developed to predict the internal quality of other citrus varieties with different physicochemical characteristics.


Keywords


Backpropagation Neural Network; Inframerah; Jaringan Syaraf Tiruan; Total Padatan Terlarut; Vitamin C

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DOI: http://dx.doi.org/10.21776/ub.jtp.2021.022.02.1

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