PEMODELAN PENGERINGAN KUNYIT (CURCUMA DOMESTICA VAL.) BERBASIS MACHINE VISION DENGAN MENGGUNAKAN ARTIFICIAL NEURAL NETWORK

Muchammad Zakaria, Yusuf Hendrawan, Gunomo Djojowasito

Abstract


ABSTRAK

Pengeringan pada kunyit (Curcuma Domestica Val.) bertujuan untuk memperpanjang umur simpan serta mengurangi kadar air hingga batas perkembangan mikroorganisme dan kegiatan enzim yang menyebabkan pembusukan menjadi terhambat. Saat ini, pengeringan kunyit menggunakan sinar matahari dan alat pengering mekanis dengan kontrol waktu dan suhu. Banyaknya kendala pada proses pengeringan meyebabkan dibutuhkannya suatu teknologi yang dapat memonitoring kadar air dari kunyit secara pasti dan akurat, yaitu dengan mesin pengering berbasis machine vision dan artificial neural network (ANN). Tujuan penelitian untuk mengetahui waktu terbaik untuk pengeringan kunyit berbasis machine vision dengan menggunakan ANN, mengetahui perbedaan grafik ANN untuk gambar yang memenuhi syarat kadar air standar pengeringan kunyit, mengetahui ANN terbaik dalam proses pengeringan kunyit. Penelitian ini menggunakan metode deskriptif yang terdiri dari lama waktu pengeringan yaitu 5 jam dengan 5 kali pengulangan dan menggunakan bahan kunyit. Metode aplikasi mesin pengering yang dilengkapi dengan machine vision sebagai pengambil data gambar pada bahan, kemudian di ekstrak warnanya untuk mengetahui nilai (red, green, dan blue). Proses pembangunan model ANN digunakan learning rate sebesar 0.1, 0.2, 0.3, 0.4, dan 0.5 pada momentum rate sebesar 0.5, 0.6, 0.7, 0.8, dan 0.9. Hasil learning process terbaik adalah learning rate 0.3 dan momentum rate 0.9. Model ANN dengan nilai error terendah yaitu untuk training 0.005 MSE, dan 24.59% ARE (Average Error), untuk validasi 0.005 MSE dan 25.35% ARE

 

ABSTRACT

To maintain turmeric (Curcuma domestica Val.) to be durable is by drying. The purpose of drying to reduce the moisture content up to limit the development of microorganisms and enzyme activities that cause spoilage. Nowadays, turmeric drying is using sunlight and mechanical drier with time and temperature control. However, drying process often arise various problems, therefore require a technology to monitor the moisture content of turmeric definitively and accurately, that is using drying machine-based machine vision and ANN (Artificial Neural Network). The purpose of this study to determine the best time for drying turmeric-based machine vision by using ANN, to know the difference of ANN’s graph for image that qualify the standard of moisture content in drying turmeric, to know the best ANN in the turmeric drying process. This research use descriptive method that consisted of duration of drying time, 5 hours with five repetitions. The application of drying machine equipped with a machine vision is to take data image on the materials, then color was extracted to know the value of (red, green, and blue). In the development process of ANN model, use learning rate of 0.1, 0.2, 0.3, 0.4, and 0.5 on the momentum rate of 0.5, 0.6, 0.7, 0.8, and 0.9. Best results is showed on the learning process of learning rate 0.3 and momentum rate 0.9. ANN models with the lowest error value is for training 0005 MSE and 24.59% ARE, for validation MSE 0005 and 25.35% ARE


Keywords


Artificial Neural Network; Kunyit; Kadar Air; Machine Vision

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DOI: http://dx.doi.org/10.21776/ub.jtp.2017.018.01.2

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