Penerapan Metode Residual Network (Resnet) Dalam Klasifikasi Penyakit Pada Daun Gandum

Ridhovan, None Andreanov; Suharso, Aries

doi:10.29100/jipi.v7i1.2410

Cited by 11 publications

(13 citation statements)

References 10 publications

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“…Several studies have also proven the excellence of this architecture of CNN as reported in the research [10] on the classification of diseases attacking wheat leaves. The best scenario discussed in the research was related to the 80:20 ratio, with the data consisting of 232 training data and 59 test data by using the architecture of ResNet 152V2.…”

Section: Introductionmentioning

confidence: 77%

ResNet101 Model Performance Enhancement in Classifying Rice Diseases with Leaf Images

Wicaksono

Andreawan

2023

J. RESTI (Rekayasa Sist. Teknol. Inf.)

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Indonesia is the fourth biggest rice producer in Asia with its production accounting for 35.4 million metric tons yearly. This figure can increase unless rice crop failure is resolved. Identifying rice diseases, however, may serve as an approach to minimizing the risk of crop failure. The classification to detect rice diseases was previously researched using ResNet101 method with 100% accuracy. Despite this perfect accuracy, this approach does not come without an issue, where the prediction is not yet optimal for each label and loss results which are regarded as too high due to overfitting. Departing from this issue, this research aims to improve the model by reducing the layer complexity of the model and comparing two layers structures of the model, two different data, and the ResNet101 model. The performance resulting from the model could be enhanced with the structuring of simple architectural layers. Despite the small quantity of dataset, the model performance can yield 100% accuracy in the classification of rice diseases with a loss value of 2.91%. The model performance in this research experienced a 2.7% increase at the loss value and it could accurately classify the type of rice diseases according to leaf images on each label. The problem solved by this research is that ResNet101 is able to classify rice disease accurately even with a small amount of data by utilizing the appropriate layer arrangement with data requirements. In addition, the overfitting that occurred in previous research can also be resolved properly. This matter proves that the correlation between the layers of the model with the amount of data is very influential.

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Section: Introductionmentioning

confidence: 77%

ResNet101 Model Performance Enhancement in Classifying Rice Diseases with Leaf Images

Wicaksono

Andreawan

2023

J. RESTI (Rekayasa Sist. Teknol. Inf.)

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“…(13) Precision indicates a correctly classified prediction of positive values divided across positive classified data [28]. (14) Recall shows the comparison of the positive correct predicted value with the entire positive correct value [29]. (15) The F1-Score shows the average comparison of precision and recall values [29].…”

Section: ) Kernel Radial Basic Function (Rbf)mentioning

confidence: 99%

“…(14) Recall shows the comparison of the positive correct predicted value with the entire positive correct value [29]. (15) The F1-Score shows the average comparison of precision and recall values [29]. ( 16)…”

Section: ) Kernel Radial Basic Function (Rbf)mentioning

confidence: 99%

Performance Comparison of the Kernels of Support Vector Machine Algorithm for Diabetes Mellitus Classification

Anggoro¹,

Permatasari²

2023

IJACSA

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Diabetes Mellitus is a disease where the body cannot use insulin properly, so this disease is one of the health problems in various countries. Diabetes Mellitus can be fatal and can cause other diseases and even lead to death. Based on this, it is important to have prediction activities to find out a disease. The SVM algorithm is used in classifying Diabetes Mellitus diseases. The purpose of this study was to compare the accuracy, precision, recall, and F1-Score values of the SVM algorithm with various kernels and data preprocessing. The data preprocessing used included data splitting, data normalization, and data oversampling. This research has the benefit of solving health problems based on the percentage of Diabetes Mellitus and can be used as material for accurate information. The results of this study are that the highest accuracy was obtained by 80% obtained from the polynomial kernel, the highest precision was obtained by 65% which was also obtained from the polynomial kernel, and the highest recall was obtained by 79% obtained from the RBF kernel and the highest f1-score was obtained by 70% obtained from RBF kernel.

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“…(13) Precision indicates a correctly classified prediction of positive values divided across positive classified data [28]. (14) Recall compares the positive correct predicted value with the entire positive correct value [29]. (15) The F1-Score shows the average comparison of precision and recall values [29].…”

Section: C) Kernel Radial Basic Function (Rbf)mentioning

confidence: 99%

“…(14) Recall compares the positive correct predicted value with the entire positive correct value [29]. (15) The F1-Score shows the average comparison of precision and recall values [29]. ( 16)…”

Section: C) Kernel Radial Basic Function (Rbf)mentioning

confidence: 99%

Performance Comparison of the Kernels of Support Vector Machine Algorithm for Diabetes Mellitus Classification

Anggoro¹,

Permatasari²

2023

IJACSA

View full text Add to dashboard Cite

Diabetes Mellitus is a disease where the body cannot use insulin properly, so this disease is one of the health problems in various countries. Diabetes Mellitus can be fatal, cause other diseases, and even lead to death. Based on this, it is essential to have prediction activities to find out a disease. The SVM algorithm is used in classifying Diabetes Mellitus diseases. This study aimed to compare the accuracy, precision, recall, and F1-Score values of the SVM algorithm with various kernels and data preprocessing. Data preprocessing included data splitting, normalization, and data oversampling. This research has the benefit of solving health problems based on the percentage of Diabetes Mellitus and can be used as material for accurate information. The results of this study are that the highest accuracy was obtained by 80% (obtained from the polynomial kernel), the highest precision was obtained by 65%, which was also obtained from the polynomial kernel, and the highest recall was obtained by 79% (obtained from the RBF kernel) and the highest F1-score was obtained by 70% (which was also obtained from the RBF kernel).

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Penerapan Metode Residual Network (Resnet) Dalam Klasifikasi Penyakit Pada Daun Gandum

Cited by 11 publications

References 10 publications

ResNet101 Model Performance Enhancement in Classifying Rice Diseases with Leaf Images

ResNet101 Model Performance Enhancement in Classifying Rice Diseases with Leaf Images

Performance Comparison of the Kernels of Support Vector Machine Algorithm for Diabetes Mellitus Classification

Performance Comparison of the Kernels of Support Vector Machine Algorithm for Diabetes Mellitus Classification

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