Sistem Deteksi Cepat Mutu Organoleptik Beras Berbasis Android

Hadipernata, Mulyana; Somantri, Agus Supriatna; Hayuningtyas, Maulida; Hidayah, Nikmatul; Hoerudin, Hoerudin

doi:10.17728/jatp.7434

Cited by 2 publications

(2 citation statements)

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“…Vol.1 No.3 February 2022 the rice production process is the amount of broken rice when it is processed in the milling machine. This can cause the quality of rice to decrease (Hadipernata et al, 2020). In general, the effectiveness of different cooling methods in reducing the temperature of rice followed an order from high to low, each 10°C increase in milled rice temperature was associated with a decrease in percentage points in the temperature range produced by the various cooling methods.…”

Section: Agaricus: Advances Agriculture Science and Farmingmentioning

confidence: 99%

Pragilling Cooling Technique on Quality Characteristics of Milled Rice based on Cooling Temperature and Duration

Putra

Cahyono

2022

agaricus

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This study aims to determine the effect of temperature, duration of cooling, and physical quality of rice that is influenced by pragiling refrigeration technique. The resultsshowed a decrease of moisture content in the process of drying of impari varieties (22.4% -13.4%) and cisadane varieties (25% -14.3%). Dry shrinkage occurs due to the amount of water that evaporates during the drying process on the 16 kg varieties of impari and cisadane varieties of 21 kg. In addition to the varieties of grain milled rice varieties also affected by the characteristics of grain quality, the varieties of impari obtained average (52.40 ± 3.23%) while in cisadane varieties obtained average (54.54 ± 1.2%). Visual appearance, color degree measurement (Lab *) of milled rice with pragiling and non cooling cooling technique has no significant effect on the given treatment. In the analysis of quality characteristics of rice with pragiling cooling technique, the quality of rice produced is better than non-coolant treatment this is seen from the percentage of head grains, broken grains and groats that produced reached 87.68%. This is in accordance with the analysis data of variance where temperature has significant effect (0.00 <0.05) on the four temperatures used. The cooling time had a significant effect on the physical quality of rice produced with significance value (0.00 <0.05) at three time levels used.

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Section: Agaricus: Advances Agriculture Science and Farmingmentioning

confidence: 99%

Pragilling Cooling Technique on Quality Characteristics of Milled Rice based on Cooling Temperature and Duration

Putra

Cahyono

2022

agaricus

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“…The change in the color gradation of rice along with the change in amylose content is the basis for image processing-based measurements. This rice display will be captured and processed for the measurement of rice starch content (Hadipernata et al, 2020). The complexity of the gradation of rice objects becomes a separate problem when measuring.…”

Section: Introductionmentioning

confidence: 99%

Non-Destructive Measurement of Rice Amylose Content Based on Image Processing and Artificial Neural Networks (ANN) Model

Saputra¹,

Wijayanto²,

Ristiyana³

et al. 2022

JTEP-L

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The purpose of this study was to develop a method of measuring the amylose content of rice using image processing techniques and an Artificial Neural Network (ANN) model. The rice samples came from six varieties, namely Way Apo Buru, Mapan P05, IR-64, Cibogo, Inpari IR Nutri Zinc, and Inpari 33. The amylose content was measured by laboratory tests and the color intensity was measured based on the RGB (Red, Green, Blue). The ANN model will correlate the RGB color intensity as input with the amylose content as the output. The ANN model used is backpropagation type with 3 input layer nodes and 2 hidden layers with 3-5-5-1 architecture. Variations in the training model used are 27 variations of the activation function. The amount of data used for model training of 30 data while for validation of 12 data. The best ANN model is determined from the high value of accuracy (100%-MAPE) and the value of coefficient of determination (R2). The results showed the best network architecture on the activation function purelin-logsig-tansig. The R2 value on the best training and validation results of 0.98 and 0.66 while the accuracy values for the best training and validation results of 98.15 and 66.82. The validation results show that the developed non-destructive method can be used to quickly and accurately measure the amylose value of rice based on RGB color value. The test results show that the non-destructive method developed cannot be used to measure the amylose content of rice quickly and accurately based on the RGB color intensity, so it needs further development. Keywords: Amylose, Artificial neural networks, Image processing, Rice

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Sistem Deteksi Cepat Mutu Organoleptik Beras Berbasis Android

Cited by 2 publications

References 0 publications

Pragilling Cooling Technique on Quality Characteristics of Milled Rice based on Cooling Temperature and Duration

Pragilling Cooling Technique on Quality Characteristics of Milled Rice based on Cooling Temperature and Duration

Non-Destructive Measurement of Rice Amylose Content Based on Image Processing and Artificial Neural Networks (ANN) Model

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