PENGEMBANGAN MODEL PREDIKSI LAJU PENGERINGAN PADA IRISAN WORTEL (Daucus carota) BERBASIS REGRESI LINIER BERGANDA (RLB)  DAN JARINGAN SYARAF TIRUAN (JST)

Saputra, Tri Wahyu; Waluyo, Sri; Septiawan, Andrie; Ristiyana, Suci

doi:10.29303/jrpb.v8i2.191

Cited by 3 publications

(4 citation statements)

References 9 publications

(9 reference statements)

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“…The accuracy value from the results of the development of the ANN model shows a reasonable prediction interpretation (reasonable forecast) because the value is in the range of 60-80% (Lewis, 1982). (Saputra et al, 2020) This research still requires further development efforts in developing a nondescriptive measurement method for rice amylose content even though it has reached a reasonable accuracy. Table 6 presents the results of research related to the content of food ingredients that can be used as a comparison in the development of the ANN model.…”

Section: Rice Amylose Contentmentioning

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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Section: Rice Amylose Contentmentioning

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

Self Cite

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“…Perhitungan numerik dilakukan untuk mengetahui besar volume pada tangki A dan B menggunakan beberapa skenario nilai C1 dan C2 dengan tujuan untuk mendapatkan model yang paling mendekati kebenaran. Simulasi numerik dilakukan menggunakan aplikasi microsoft excel dengan menerapkan persamaan ( 9) dan (10).…”

Section: Analisis Numerikunclassified

“…Berdasarkan Gambar 3, terlihat bahwa skenario yang paling mendekati dengan kurva hasil eksperimen adalah skenario 1,2 dan 4. Namun, untuk menentukan skenario terbaik, diperlukan perhitungan root mean square error (RMSE) dimana semakin kecil nilainya maka semakin baik suatu model [10]. Hasil perhitungan RMSE setiap skenario disajikan pada Tabel 4.…”

Section: Hasil Pemodelan Sistem Persamaan Diferensial Linear Dan Simulasi Numerikunclassified

Penerapan Sistem Persamaan Diferensial Linier pada Simulasi Debit Air pada Pipa

Efendi

Sagita

2021

JMPM

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Salah satu permasalahan dalam kehidupan sehari-hari yang cukup rumit untuk diselesaikan adalah sistem aliran fluida (air) dalam bejana bertingkat. Pada penelitian ini, telah dilakukan pembuatan model sistem persamaan diferensial linear (SPDL) yang menggambarkan aliran air dari sistem instalasi air dengan tangki bertingkat serta membandingkan hasil simulasi numerik dari model dengan hasil eksperimen. Model SPDL dibangun dengan melibatkan observasi lapang secara langsung untuk menentukan nilai konstanta model (C1, C2 dan C3). Model yang telah dibangun disimulasikan secara numerik menggunakan metode Euler dengan 4 skenario nilaikonstanta. Hasil menunjukkan bahwa volume tangki A dan B serta laju perubahan volume airnya telah berhasil digambarkan oleh model SPDL yang telah dibangun dengan hasil yang paling mendekati hasil eksperimen adalah nilai C1 sebesar 0.01323 dan C2 sebesar 0.01091 dengan nilai RMSE 0.2599.

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“…ANN models have also been applied to estimate the volume and surface area of apple fruits with better feasibility compared to mathematical models (Ziaratban et al, 2017). Not only limited to fruits, ANN models have been applied to automatically identify potato varieties (Azizi et al, 2016), predict the drying rate of carrots (Saputra et al, 2020). Basic geometric attribute data of various popular banana types cultivated in Indonesia, which are crucial for post-harvest handling techniques, have not been extensively published.…”

Section: Introductionmentioning

confidence: 99%

Classification of Banana Types Based on The Geometrical Attributes using Artificial Neural Network Method

Waluyo,

Pangestu,

Warji

et al. 2024

JTEP-L

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Banana (Musa paradisiaca) is one of the important horticultural commodities. This study aims to measure the physical and geometrical parameters of three different bananas (Muli, Ambon, and Kepok) and to develop prediction equations using an Artificial Neural Network (ANN) model. In this study the backpropagation ANN model with supervised learning method was used. The ANN model had one output node, two hidden layers, and network architecture of 8 inputs, namely fruit weight and volume, projected area and roundness of the fruit, cross section, peel color, and geometric mean fruit cross section diameter. The data for building the model and testing the model were respectively 70% and 30% of the 150 data number in total. The results showed that the best ANN model structure for estimating Muli, Ambon and Kepok bananas was purelin-logsig-logsig with an RMSE value of 0.0077 and an R2 of 0.9999. This shows that the ANN model is highly robust to predict the banana types. Using the built model, the accuracy of the prediction results is 100%. Keywords: Artificial Neural Network, Banana fruits, Geometry attribute.

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PENGEMBANGAN MODEL PREDIKSI LAJU PENGERINGAN PADA IRISAN WORTEL (Daucus carota) BERBASIS REGRESI LINIER BERGANDA (RLB) DAN JARINGAN SYARAF TIRUAN (JST)

Cited by 3 publications

References 9 publications

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

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

Penerapan Sistem Persamaan Diferensial Linier pada Simulasi Debit Air pada Pipa

Classification of Banana Types Based on The Geometrical Attributes using Artificial Neural Network Method

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