Perbandingan Normalisasi Data untuk Klasifikasi Wine Menggunakan Algoritma K-NN

Nasution, Darnisa Azzahra; Khotimah, Hidayah Husnul; Chamidah, Nurul

doi:10.24114/cess.v4i1.11458

Cited by 82 publications

(86 citation statements)

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“…The difference in vulnerability in each attribute is the cause of the malfunctioning of attributes that have a much smaller value than other attributes. Therefore, normalization is needed so that vulnerable values can be set to a certain scale [6]. The Min-Max Normalization method is used in this paper.…”

Section: Normalizationmentioning

confidence: 99%

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Comparison of Neural Network and Recurrent Neural Network to Predict Rice Productivity in East Java

Hamdianah

Mahmudy

Widaryanto

2021

JITeCS

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Rice is the staple food for most of the population in Indonesia which is processed from rice plants. To meet the needs and food security in Indonesia, a prediction is required. The predictions are carried out to find out the annual yield of rice in an area. Weather factors greatly affect production results so that in this study using weather parameters as input parameters. The Input Parameters are used in the Recurrent Neural Network algorithm with the Backpropagation learning process. The results are compared with Neural Networks with Backpropagation learning to find out the most effective method. In this study, the Recurrent Neural Network has better prediction results compared to a Neural Network. Based on the computational experiments, it is found that the Recurrent Neural Network obtained a Means Square Error of 0.000878 and a Mean Absolute Percentage Error of 10,8832%, while the Neural Network obtained a Means Square Error of 0.00104 and a Mean Absolute Percentage Error of 10,3804.

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Section: Normalizationmentioning

confidence: 99%

“…The Min-Max Normalization method is used in this paper. This method is believed to be good to use because it has higher accuracy compared to other normalization methods [6]. The Min-max Normalization method can use the following formula:…”

Section: Normalizationmentioning

confidence: 99%

Comparison of Neural Network and Recurrent Neural Network to Predict Rice Productivity in East Java

Hamdianah

Mahmudy

Widaryanto

2021

JITeCS

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“…Normalization is the process of transforming data to equate a range of values with a certain scale. Normalization of z-score is a normalization method based on the average value and standard deviation of the data [13]. The formula of z-score can be written as follows:…”

Section: Normalization Of Z-scorementioning

confidence: 99%

Clustering of Cardiovascular Disease Patients Using Data Mining Techniques with Principal Component Analysis and K-Medoids Clustering of Cardiovascular Disease Patients Using Data Mining Techniques with Principal Component Analysis and K-Medoids

Irwansyah¹,

Pratama²,

Ohyver³

2020

Preprint

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Cardiovascular disease is the number one cause of death in the world and Quoting from WHO, around 31% of deaths in the world are caused by cardiovascular diseases and more than 75% of deaths occur in developing countries. The results of patients with cardiovascular disease produce many medical records that can be used for further patient management. This study aims to develop a method of data mining by grouping patients with cardiovascular disease to determine the level of patient complications in the two clusters. The method applied is principal component analysis (PCA) which aims to reduce the dimensions of the large data available and the techniques of data mining in the form of cluster analysis which implements the K-Medoids algorithm. The results of data reduction with PCA resulted in five new components with a cumulative proportion variance of 0.8311. The five new components are implemented for cluster formation using the K-Medoids algorithm which results in the form of two clusters with a silhouette coefficient of 0.35. Combination of techniques of Data reduction by PCA and the application of the K-Medoids clustering algorithm are new ways for grouping data of patients with cardiovascular disease based on the level of patient complications in each cluster of data generated.

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“…The difference ranges of values in each parameter are significant therefore it is necessary to normalize the data with the result that no dominance of each other [14]. The normalization method used is the Min-Max normalization method [15] which performs a linear transformation of the input data and produces a balance of comparative values between original data and normalized data. Min-max normalization formula is shown in Equation 1.…”

Section: Data Processingmentioning

confidence: 99%

Prediction of Biochemical Oxygen Demand Using Radial Basis Function Network

Noor

Buliali

2020

KINETIK

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Biochemical oxygen demand shows the amount of oxygen needed by microorganisms to decompose dissolved organic substances suspended in water. This variable determines water quality. The higher value indicates lower water quality. Obtaining this value requires a lengthy procedure of five days in typical laboratories. This paper proposes to predict biochemical oxygen demand using a radial basis function network with improvement relational fuzzy c-means clustering to set centroid by using 11 parameters that come from water quality records. The dataset used in testing consisting of weekly parameters between 2014-2019. Testing results show performance measurement of mean absolute error, mean square error, root mean square error, mean absolute percentage error, and accuracy using centroid with improvement relational fuzzy c-means 0.15016, 0.3677, 0.19082, 21.64490 and 78.35510 comparing with centroid from fuzzy c-means 0.16002, 0.04021, 0.19963, 22.83184, and 77.16816.

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Perbandingan Normalisasi Data untuk Klasifikasi Wine Menggunakan Algoritma K-NN

Cited by 82 publications

References 0 publications

Comparison of Neural Network and Recurrent Neural Network to Predict Rice Productivity in East Java

Comparison of Neural Network and Recurrent Neural Network to Predict Rice Productivity in East Java

Clustering of Cardiovascular Disease Patients Using Data Mining Techniques with Principal Component Analysis and K-Medoids Clustering of Cardiovascular Disease Patients Using Data Mining Techniques with Principal Component Analysis and K-Medoids

Prediction of Biochemical Oxygen Demand Using Radial Basis Function Network

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