A rapid classification of wheat flour protein content using artificial neural network model based on bioelectrical properties

Sucipto, Sucipto; Anna, Maffudhotul; Arwani, Muhammad; Hendrawan, Yusuf

doi:10.12928/telkomnika.v17i2.9450

Cited by 8 publications

(6 citation statements)

References 17 publications

(24 reference statements)

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“…Selain pengenalan pola objek, kecerdasan buatan juga mampu melakukan identifikasi terhadap suara seperti pengenalan suara dengan jenis bahasa arab [6], kemudian identifikasi video secara realtime [7]. Selanjutnya mengklasifikasikan kandungan protein dari tepung terigu [8].…”

Section: Pendahuluanunclassified

Biometric Technology Using Eigenface Algorithm

Ramadhan¹,

Nurarif²,

Syahril³

et al. 2021

Com, Engine, Sys, Sci

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Penelitian ini membahas tentang teknologi biometrik yang digunakan untuk mengenali pola fisik dari seseorang sehingga dapat teridentifikasi. Focus biometrik yang akan diteliti adalah deteksi wajah. Hal ini dilakukan karena melihat kurang optimalnya pengenalan biometrik pada sidik jari yang rentan tidak terdeteksi akibat luka atau perubahan struktur kulit seseorang. Teknologi biometrik wajah ini dapat digunakan untuk mengenali seseorang dalam melakukan perekaman data seperti pengelolaan biodata pegawai, masyarakat dan absensi. Untuk membangun teknologi biometrik tersebut maka dibutuhkan sebuah algoritma yang mampu melakukan pengenalan pola yaitu Eigenface. Algoritma tersebut telah teruji dan banyak digunakan pada penelitian sebelumnya dalam pengenalan pola, sehingga sangat tepat untuk digunakan dalam penelitian ini. Proses pengenalan wajah dimulai dengan mengambil citra wajah sehingga akan memperoleh nilai RGB, kemudian melakukan konversi dari RGB ke grayscale. Setelah proses konversi selesai maka selanjutnya mengubah citra tersebut ke dalam bentuk matriks dan menghitung nilai Flatvektor sehingga nantinya akan dicari nilai kedekatan dari data citra yang direkam. Dari hasil penelitian yang dilakukan maka diperoleh hasil bahwa algoritma Eigenface telah berhasil melakukan pengenalan wajah dengan akurasi 100% dari 10 data uji. Pengujian akurasi dilakukan dengan insitas cahaya 10 sampai dengan 1000 Lux, jarak antara 30 sampai dengan 200 cm dan gerak objek diatas 4 detik

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Section: Pendahuluanunclassified

Biometric Technology Using Eigenface Algorithm

Ramadhan¹,

Nurarif²,

Syahril³

et al. 2021

Com, Engine, Sys, Sci

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“…Studying the prediction of moisture content and water activity of semi-finished cassava crackers using ANNs, Lertworasirikul & Tipsuwan (2008) concluded that the best one was the network with the 3-9-2 configuration, presenting RMSE of 0.0917 and correlation of 0.99. Sucipto et al (2019), seeking to classify the protein content of wheat flour from its bioelectric properties (capacitance and resistance) found that the best RNA topology for classification between hard, medium and soft flour of protein content is 2-20-50-3, which produced RMSE values of 0.0097 and 0.0399 and a correlation of 0.99 and 0.98 for the learning and verification stages, respectively.…”

Section: Evaluation Of the Best Ann Architecturesmentioning

confidence: 99%

“…used the ANN to estimate the firmness of kiwifruit having as inputs the concentrations of different nutrients in the fruits (N, K, Ca and Mg),while Sartori, et al (2017) proposed models of artificial neural networks to predict the effects of different operational variables (peroxidation time, temperature, pH, peroxide dosage and initial ° Brix) on the removal of color and sucrose content of sugarcane juice clarified.Mutlu, et al (2011) predicted 10 different quality properties of wheat flours combining near infrared reflectance spectroscopy (NIR) with the artificial neural networkand Sucipto, et al (2019) …”

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confidence: 99%

Modeling thermal properties of exotic fruits pulps: an artificial neural networks approach

Santos

Moraes

Lopes

et al. 2020

RSD

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Thermophysical properties are important in design, simulation, optimization, and control of food processing. Its prediction is very important but theoretical basis is difficult and empirical models were commonly used. In this work, the modeling of neural networks was applied as an alternative to predict density, thermal conductivity and thermal diffusivity from the temperature and moisture content of jackfruit, genipap and umbu. Data sets from literature were used, combined and individually, to obtain four networks. Supervised multilayer perceptron networks were developed, using the back-propagation algorithm. Several configurations of artificial neural networks (ANNs) were evaluated with one or two hidden layers and a maximum of 21 and 12 neurons in each one, respectively. Data sets were divided to learning (60%) and verification (40%) steps. Best ANNs were chosen based on correlation coefficient and root mean square errors (RMSE), and compared with polynomial models using average absolute deviations (AADs). From total disposable data set, the best ANN developed presents one hidden layer with 15 neurons and shows the same predictive ability of ANNs created from individual fruits data sets, presenting close RMSE and correlation coefficient. The ANNs developed presents AADs near to polynomial models and appers as alternative to conventional modeling. Results indicate that the ANN created from total data set can replace nine polynomial models to predict the thermophysical properties of jackfruit, genipap and umbu pulps.

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“…The same research states that ANN is faster and has a high degree of accuracy in making [7], [8]. So that ANN is inside [9], states that the model describes a systematic model in the learning process from input and output [10]. In the prediction case, the ANN model can be developed to produce optimal predictive results based on performance which is influenced by the amount of data used [11].…”

Section: Introductionmentioning

confidence: 99%

Implementation multiple linear regresion in neural network predict gold price

Yanto

Sanjaya

Yulasmi

et al. 2021

IJEECS

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<p>The movement of gold prices in the previous period was crucial for investors. However, fluctuations in gold price movements always occur. The problem in this study is how to apply multiple linear regression (MRL) in predicting artificial neural networks (ANN) of gold prices. MRL is mathematical calculation technique used to measure the correlation between variables. The results of the MRL analysis ensure that the network pattern that is formed can provide precise and accurate prediction results. In addition, this study aims to develop a predictive pattern model that already exists. The results of the correlation test obtained by MRL provide a correlation of 62% so that the test results are said to have a significant effect on gold price movements. Then the prediction results generated using an ANN has a mean squared error (MSE) value of 0.004264%. The benefits obtained in this study provide an overview of the gold price prediction pattern model by conducting learning and approaches in testing the accuracy of the use of predictor variables.</p>

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A rapid classification of wheat flour protein content using artificial neural network model based on bioelectrical properties

Cited by 8 publications

References 17 publications

Biometric Technology Using Eigenface Algorithm

Biometric Technology Using Eigenface Algorithm

Modeling thermal properties of exotic fruits pulps: an artificial neural networks approach

Implementation multiple linear regresion in neural network predict gold price

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