A Wearable Device for Enhancing Basketball Shooting Correctness with MPU6050 Sensors and Support Vector Machine Classification

Fadillah, Baginda Achmad; Putrada, Aji Gautama; Abdurohman, Maman

doi:10.22219/kinetik.v7i2.1435

Cited by 10 publications

(4 citation statements)

References 11 publications

(13 reference statements)

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“…Value is the contents of the package. We apply normalization and standardization to the dataset in the pre-processing stage [17]. Normalization and standardization can reduce misclassification caused by different data ranges between each feature.…”

Section: Data Preparationmentioning

confidence: 99%

Enhancing Intrusion Detection System on Nodemcu With Ensemble Voting

Alamsyah

2023

JETT

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As part of the Internet, a NodeMCU becomes a vulnerability to cyber-attack, so the intrusion detection system (IDS) against cyber-attack on the NodeMCU becomes a poignant research challenge. Several studies have applied several machine learning techniques for IDS on NodeMCU; however, there is a research opportunity to improve the performance of existing models. This research aims to increase the IDS performance on NodeMCU with ensemble voting. We hypothesize the realization of how the implementation of an IDS in NodeMCU. Then we obtain and observe a dataset from Kaggle. The dataset comprises five attacks: misconfiguration, DDoS, probe, scanning, and MiTM. Then we design the detection with ensemble voting consisting of a decision tree and a random forest. We benchmark our proposed solution with decision trees and random forest performance. This study uses several test metrics, including , , , and .The test results show that the decision tree has better Precision in predicting misconfiguration attacks and scan attacks than random forests. On the other hand, the random forest has better in predicting normal data, DDoS attacks, probe attacks, and MiTM attacks. In terms of , ensemble voting has the best performance, which is , compared to the decision tree and random forest, which are and , respectively. We conclude that by assembling a decision tree in the random forest with ensemble voting, random forest performance can improve.

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Section: Data Preparationmentioning

confidence: 99%

Enhancing Intrusion Detection System on Nodemcu With Ensemble Voting

Alamsyah

2023

JETT

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“…The hyperplane is a field that can divide the data linearly, called a dataset that is linearly separable [25]. If the dataset is not linearly separable, then the kernel trick is used, namely changing the dimensions of the feature space so that a hyperplane can be created that divides the [26] dataset in half.…”

Section: Airplane Failure Detection By Bird Strikementioning

confidence: 99%

ImbGAFS: GA Feature Selection for AUC in Bird Strike Prediction

Putrada,

Prabowo

2023

Machine Learning Techniques and NLP

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Several studies discuss airplane failure prediction due to bird strikes. However, these studies need to analyze further the imbalance in their dataset. Our research aim is to create an airplane failure prediction by bird strike using a machine learning method optimized using GA feature selection. GA feature selection uses AUC maximization as the objective function to tackle imbalance problems in the bird strike dataset. First, we obtained the airplane bird strike dataset from Kaggle. We carry out preprocessing on the dataset.We then compared and chose one of four stateof-the-art machine learning methods: SVM, MLP, logistic regression, and random forest. The selection process involves oversampling methods, synthetic minority oversampling technique (SMOTE), and optimum threshold selection, which involves geometric mean (g- mean) and area under curve (AUC) values. Finally, we optimize airplane failure prediction by performing AUC maximization using GA feature selection. Our test results show that random forest is the best machinelearning method in airplane failure prediction compared to SVM, logistic regression, and MLP. SMOTE can increase random forest AUC from 0.845 to 0.878. Finally, the random forest model from ImbGAFS is better than the conventional method without feature selection. The increase in the AUC value is from 0.878 to 0.889. Then, after carrying out optimal threshold selection, ImbGAFS+random forest also has better sensitivity, specificity, and g-mean than conventional methods. The increase is from 0.7737, 0.8350, and 0.8037 to 0.8033, 0.8301, and 0.8166, respectively.

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“…That range is in stark contrast to the lux sensor, which ranges from 0 to approximately 10000. Another example as in [6], although the sensor comes from a three-axis accelerometer, each gives a different axis range. The diversity of the range of feature values can result in the prediction model's performance decline [7].…”

Section: Introductionmentioning

confidence: 99%

NS-SVM: Bolstering Chicken Egg Harvesting Prediction with Normalization and Standardization

Putrada

Alamsyah

Fauzan

et al. 2023

JUITA

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Breeding chickens and chicken eggs are poignant, and recent studies have applied computer science to optimize this field, including chicken egg harvesting prediction. However, existing research does not emphasize the importance of data transformation to obtain optimum chicken egg harvesting prediction. This paper proposes the normalization and standardization-bolstered support vector machine (NS-SVM) method, namely normalization, and standardization, to improve the prediction of chicken egg harvest using SVM. First, we obtain the chicken egg dataset from Africa using Kaggle. The problem and solution become urgent, whereas chicken egg production can ease businesspeople to invest in chicken eggs. We adopt the normalization and standardization method from previous research. However, the notation is to differentiate the method from legacy SVM. The dataset has up to 13 features. Then we apply standard pre- processing such as label encoding and random oversampling. We also review the dataset feature using the Pearson correlation coefficient (PCC). We use two SVM kernels: radial basis function (RBF) and the 2nd-degree polynomial. Then we again apply the same model but by applying normalization and standardization. We use cross- validation with 𝑲 = 𝟏𝟎 to measure the Accuracy of the compared models. The results show that normalization and standardization positively affect the prediction model of the two SVM kernels. The model with the highest performance is NS-SVM with a 2nd-degree kernel, namely 𝑨𝒄𝒄𝒖𝒓𝒂𝒄𝒚 = 𝟎. 𝟗𝟗𝟔. At the same time, the model with the lowest performance is SVM with RBF, namely𝑨𝒄𝒄𝒖𝒓𝒂𝒄𝒚 = 𝟎. 𝟗𝟖𝟔. In addition, the results of ROC AUC analysis show that the performance of our model on the imbalanced dataset with a moderate degree is 𝑨𝑼𝑪 = 𝟎.𝟗𝟐𝟕 to 𝟎.𝟗𝟗𝟑.

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A Wearable Device for Enhancing Basketball Shooting Correctness with MPU6050 Sensors and Support Vector Machine Classification

Cited by 10 publications

References 11 publications

Enhancing Intrusion Detection System on Nodemcu With Ensemble Voting

Enhancing Intrusion Detection System on Nodemcu With Ensemble Voting

ImbGAFS: GA Feature Selection for AUC in Bird Strike Prediction

NS-SVM: Bolstering Chicken Egg Harvesting Prediction with Normalization and Standardization

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