The effectiveness of Bat algorithm for data handling in various applications

Mohamed, Ramizi; Yusof, Munirah Mohd; Wahid, Noorhaniza

doi:10.1109/iccsce.2016.7893562

Cited by 2 publications

(3 citation statements)

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“…Teknik klasifikasi ini kemudian berkembang menjadi suatu algoritma klasifikasi. Dari sekian algoritma yang sering digunakan tersebut adalah Naive Bayes Classifier dan Decision Tree [8] [9]. Pada penelitian ke media Twitter sebelumnya, Naive Bayes Classifier menghasilkan Nilai Precision 74,5 persen, Recall 73,5 persen, Accuracy 73,9 persen [10].…”

Section: Pendahuluanunclassified

“…Untuk itu adanya suatu optimasi atas algoritma klasifikasi diharapkan mampu menghasilkan tingkat solusi yang lebih baik [12]. Ada beberapa algoritma optimasi yang memungkinkan dapat digunakan dalam mengoptimasi algoritma klasifikasi salah satunya yaitu Algoritma Bat atau Bat Algorithm (BA) [8][13] [14].…”

Section: Pendahuluanunclassified

“…Penelitian Rozlini, Munirah dan Noorhaniza, Melakukan pengujian tingkat efektivitas pengolahan data untuk Algoritma Bat melalui tipe data dan ukuran atribut dalam berbagai dataset menggunakan algoritma Naive Bayes, Decision Tree dan K-NN [8].…”

Section: Tinjauan Pustaka 21 Penelitian-penelitian Terdahuluunclassified

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Pengukuran Kinerja Optimasi Algoritma Bat Pada Algoritma Naive Bayes, KNN Dan Decision Tree Untuk Sentimen Analisis Di Lini Masa Twitter

Adipradana

2023

TIKomSiN

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Social media is a very effective communication media in today's digital era. Of the social media, Twitter is the most widely used social media. Many tweets entered on Twitter have encouraged research in the field of text mining. One of the branches of text mining is sentiment analysis. Sentiment analysis in this study was formed from 3 classification algorithms, namely Naïve Bayes and Decission Tree. In practice, the results of the 3 classification algorithms often produce very low levels of accuracy. Bat algorithm is an algorithm that can optimize the results from the accuracy of the Naïve Bayes, K-NN algorithm and Decission Tree. In this study, two research scenarios were made: first, calculating the accuracy of the Naïve Bayes, K-NN algorithm, and Decission Tree. Second, optimizing the classification results of the 3 algorithms with the Bat algorithm method, which then re-tested the accuracy value. In the first scenario the percentage is generated from the accuracy value of Naïve Bayes of 33,58, K-NN of 33,61 and Decission Tree of 32,82. In the second scenario, using one of the objective functions, namely f(x) = x2, the Naïve Bayes value is obtained 39,01, K-NN 66,15 and Decission Tree 76,63. From the results of 3 the optimization test of classification Algorithm, it was found that the overall objective functions of the Bat algorithm were all able to increase the data accuracy value from before optimization. From all the tests, it was found that the Decision Tree algorithm has the highest average value of optimization increment, namely 43,81 %

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Section: Pendahuluanunclassified