Optimization of Genetic Algorithm Performance Using Naïve Bayes for Basis Path Generation

Arwan, Achmad; Rusdianto, Denny Sagita

doi:10.22219/kinetik.v2i4.370

Cited by 5 publications

(5 citation statements)

References 8 publications

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“…The main purpose of the graph matrix is to determine the test path that must be carried out to achieve comprehensive test coverage. With a graph matrix, testers can identify which nodes belong to a particular execution path (Arwan and Rusdianto 2017;Shuaibu et al, 2019;Padmanabhan, 2022). Apart from that, the graph matrix is also useful for tracking which nodes have been covered and which have not been covered by the test cases that have been designed.…”

Section: Independent Path and Graph Matrixmentioning

confidence: 99%

Optimization of White Box Testing by Utilizing Branching and Repeating Structures in Java Programs Using Base Path

Kalfin,

Ibrahim,

Laksito

2024

IJMSC

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Software testing is a process created to detect anomalies in the operation of a program or system in order to achieve the expected results. White box testing is a software testing method that tests the internal structure, design and program code. This research aims to produce an optimization method for white box testing in Java programs by utilizing branching and repetition structures using the basis path method, as well as analyzing the effectiveness of the proposed method in generating test cases. The Java program tested in this research includes input function calls, loops, branching, and exception handling. Test case design is carried out by applying the basis path method to achieve comprehensive coverage. The test results show that the base path method is able to produce effective test cases for testing control structures without redundancy. Test case design is assisted by flowgraph and matrix graph modeling. It is hoped that this research can contribute to the optimization of white box testing techniques.

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Section: Independent Path and Graph Matrixmentioning

confidence: 99%

Optimization of White Box Testing by Utilizing Branching and Repeating Structures in Java Programs Using Base Path

Kalfin,

Ibrahim,

Laksito

2024

IJMSC

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“…GA merupakan metode optimasi yang dikembangkan berdasarkan mekanisme seleksi alam dengan cara meniru genetika makhluk hidup dalam memecahkan masalah [21]. Optimasi yang dilakukan oleh GA adalah dengan memprediksi jumlah iterasi yang tepat, sehingga tidak diperlukan lagi perhitungan dengan jumlah iterasi yang berbeda untuk mendapatkan kemunculan yang lengkap dari jalur bebas [22]. Keuntungan paling signifikan dari GA adalah kemampuannya dalam pencarian global serta kemampuan beradaptasi terhadap spektrum masalah yang luas [23].…”

Section: Pendahuluanunclassified

Perbandingan Optimasi Feature Selection pada Naïve Bayes untuk Klasifikasi Kepuasan Airline Passenger

Religia

Amali²

2021

RESTI

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The quality of an airline's services cannot be measured from the company's point of view, but must be seen from the point of view of customer satisfaction. Data mining techniques make it possible to predict airline customer satisfaction with a classification model. The Naïve Bayes algorithm has demonstrated outstanding classification accuracy, but currently independent assumptions are rarely discussed. Some literature suggests the use of attribute weighting to reduce independent assumptions, which can be done using particle swarm optimization (PSO) and genetic algorithm (GA) through feature selection. This study conducted a comparison of PSO and GA optimization on Naïve Bayes for the classification of Airline Passenger Satisfaction data taken from www.kaggle.com. After testing, the best performance is obtained from the model formed, namely the classification of Airline Passenger Satisfaction data using the Naïve Bayes algorithm with PSO optimization, where the accuracy value is 86.13%, the precision value is 87.90%, the recall value is 87.29%, and the value is AUC of 0.923.

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“…The optimization carried out by GA is to predict the right number of iterations, so that there is no need to calculate the number of different iterations to get complete occurrences of independent paths. [16]. The most significant advantage of GA is its ability to search globally as well as adaptability to a wide spectrum of problems [17].…”

Section: Introductionmentioning

confidence: 99%

Genetic Algorithm Optimization on Nave Bayes for Airline Customer Satisfaction Classification

Religia

Maulana

2021

JISA

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Airline companies need to provide satisfactory service quality so that people do not switch to using other airlines. The way that can be used to determine customer satisfaction is to use data mining techniques. Currently, the website www.kaggle.com has provided Airline Passenger Satisfaction data consisting of 22 attributes, 1 label and 25976 instances which are included in the supervised learning data category. Based on several previous studies, the Naïve Bayes algorithm can provide better classification performance than other classification algorithms. Several studies also state that the use of Naive Bayes can be optimized using Genetic Algorithm (GA) to obtain better performance. The use of Genetic Algorithm for Nave Bayes optimization in classifying Airline Passenger Satisfaction data requires further research to ensure the performance of the given classification. This study aims to compare the use of the Naive Bayes algorithm for the classification of Airline Passenger Satisfaction with and without GA optimization. The data validation process used in this study is to use split validation to divide the dataset into 95% training data and 5% testing data. The test results show that the use of GA on Naive Bayes can improve the classification performance of Airline Passenger Satisfaction data in terms of accuracy and recall with an accuracy value of 85.99% and a recall of 87.91%.

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Optimization of Genetic Algorithm Performance Using Naïve Bayes for Basis Path Generation

Cited by 5 publications

References 8 publications

Optimization of White Box Testing by Utilizing Branching and Repeating Structures in Java Programs Using Base Path

Optimization of White Box Testing by Utilizing Branching and Repeating Structures in Java Programs Using Base Path

Perbandingan Optimasi Feature Selection pada Naïve Bayes untuk Klasifikasi Kepuasan Airline Passenger

Genetic Algorithm Optimization on Nave Bayes for Airline Customer Satisfaction Classification

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