Increasing The Precision Of Noise Source Detection System using KNN Method

Nando, Parlin; Putrada, Aji Gautama; Abdurohman, Maman

doi:10.22219/kinetik.v4i2.757

Cited by 5 publications

(2 citation statements)

References 16 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Confusion matrices, accuracy, precision, recall, and f1-score are metrics to compare the two kernels. A confusion matrix shows the prediction characteristics of a classification model [23].…”

Section: Test Metrics and Scenariomentioning

confidence: 99%

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

Fadillah

Putrada

Abdurohman

2022

KINETIK

Self Cite

View full text Add to dashboard Cite

One of the impacts of Covid-19 is the delay of basketball sports competitions, which influences the athlete’s fitness and the athlete’s ability to play, especially for shooting techniques. Existing research in wearable devices for basketball shooting correctness classification exists. However, there is still an opportunity to increase the classification performance. This research proposes designing and building a smartwatch prototype to classify the basketball shooting technique as correct or incorrect with enhanced sensors and classification methods. The system is based on an Internet of things architecture and uses an MPU6050 sensor to take gyroscope data in the form of X, Y, and Z movements and accelerometer data to accelerate hand movements. Then the data is sent to the Internet using NodeMCU microcontrollers. Feature extraction generates 18 new features from 3 axes on each sensor data before classification. Then, the correct or incorrect classification of the shooting technique uses the Support-Vector-Machine (SVM) method. The research compares two SVM kernels, linear and 3rd-degree polynomial kernels. The results of using the max, average, and variance features in the SVM classification with the polynomial kernel produce the highest accuracy of 94.4% compared to the linear kernel. The contribution of this paper is an IoT-based basketball shooting correctness classification system with superior accuracy compared to existing research.

show abstract

Section: Test Metrics and Scenariomentioning

confidence: 99%

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

Fadillah

Putrada

Abdurohman

2022

KINETIK

Self Cite

View full text Add to dashboard Cite

show abstract

“…KNN is a classification based on the shortest distance between the training data and the object to be classified [29]. Thus, this model is often called lazy learning [30].…”

Section: Classification and Evaluationmentioning

confidence: 99%

Overcoming Data Imbalance Problems in Sexual Harassment Classification with SMOTE

Putrada

Wijaya²,

Oktaria³

2022

ijoict

Self Cite

View full text Add to dashboard Cite

Delivery of justice with the help of artificial intelligence is a current research interest. Machine learning with natural language processing (NLP) can classify the types of sexual harassment experiences into quid pro quo (QPQ) and hostile work environments (HWE). However, imbalanced data are often present in classes of sexual harassment classification on specific datasets. Data imbalance can cause a decrease in the classifier's performance because it usually tends to choose the majority class. This study proposes the implementation and performance evaluation of the synthetic minority over-sampling technique (SMOTE) to improve the QPQ and HWE harassment classifications in the sexual harassment experience dataset. The term frequency-inverse document frequency (TF-IDF) method applies document weighting in the classification process. Then, we compare naïve Bayes with K-Nearest Neighbor (KNN) in classifying sexual harassment experiences. The comparison shows that the performance of the naïve Bayes classifier is superior to the KNN classifier in classifying QPQ and HWE, with AUC values of 0.95 versus 0.92, respectively. The evaluation results show that by applying the SMOTE method to the naïve Bayes classifier, the precision of the minority class can increase from 74% to 90%.

show abstract

Q8KNN: A Novel 8-Bit KNN Quantization Method for Edge Computing in Smart Lighting Systems with NodeMCU

Putrada,

Abdurohman,

Perdana

et al. 2024

Lecture Notes in Networks and Systems

View full text Add to dashboard Cite

Increasing The Precision Of Noise Source Detection System using KNN Method

Cited by 5 publications

References 16 publications

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

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

Overcoming Data Imbalance Problems in Sexual Harassment Classification with SMOTE

Q8KNN: A Novel 8-Bit KNN Quantization Method for Edge Computing in Smart Lighting Systems with NodeMCU

Contact Info

Product

Resources

About