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Improved performance of fake account classifiers with percentage overlap features selection

IAES International Journal of Artificial Intelligence · Vol. 13 No. 2 (2024) · hlm. 1585–1595

DOI
10.11591/ijai.v13.i2.pp1585-1595
ISSN
2252-8938, 2089-4872
Terbit
2024-06-01
Penerbit
Institute of Advanced Engineering and Science
Bahasa
eng
Lisensi
creativecommons.org/licenses/by-sa/4.0

Abstrak

Feature selection plays a crucial role in the development of high-performance classification models. We propose an innovative method for detecting fake accounts. This method leverages the percentage overlap technique to refine feature selection. We introduce our technique upon earlier work that showcased the enhanced efficacy of the Naïve Bayesian classifier through dataset normalization. Our study employs a dataset of account profiles sourced from Twitter, which we normalize using the Min-Max method. We analyze the results through a series of comprehensive experiments involving diverse classification algorithms—such as Naïve Bayes, decision tree, k-nearest neighbors (KNN), deep learning, and support vector machines (SVM). Our experimental results demonstrate a 100% accuracy achieved by the SVM and deep learning classifiers. The results are attributed to the percentage overlap technique, which facilitates the identification of four highly informative features. These findings outperform models with more extensive feature sets, underscoring the efficacy of our approach.

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  • Institut Teknologi Sepuluh Nopember
  • Universitas Bahaudin Mudhary Madura
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Tampilkan sebagai tabel
Rekan penulisOrganisasiArtikel bersama
Supeno DjanaliInstitut Teknologi Sepuluh Nopember3
Imam KuswardayanInstitut Teknologi Sepuluh Nopember2
Wahyu SuadiInstitut Teknologi Sepuluh Nopember2
Ilham Gurat AdillionInstitut Teknologi Sepuluh Nopember2
Faosan MapaAfiliasi tidak diketahui1
Ni Putu Rina Y.Afiliasi tidak diketahui1
Cahyono CahyonoInstitut Teknologi Sepuluh Nopember1
Nurul HidayatUniversitas Bahaudin Mudhary Madura1
Anny YuniartiInstitut Teknologi Sepuluh Nopember1
Krisma Marinda Sormin SiregarAfiliasi tidak diketahui1
Sujianik SujianikAfiliasi tidak diketahui1
Rakhmadany PrimanandaAfiliasi tidak diketahui1
Baskoro Adi PratomoInstitut Teknologi Sepuluh Nopember1
Junaidi JunaidiInstitut Teknologi Sepuluh Nopember1
Prasetyo Adi WibowoInstitut Teknologi Sepuluh Nopember1
Dini YuniasriInstitut Teknologi Sepuluh Nopember1
Putri DamayantiInstitut Teknologi Sepuluh Nopember1
Waskitho WibisonoInstitut Teknologi Sepuluh Nopember1
Radityo AnggoroInstitut Teknologi Sepuluh Nopember1
Prima WiratamaInstitut Teknologi Sepuluh Nopember1
Hazna At ThooriqohInstitut Teknologi Sepuluh Nopember1
M. Naufal AzzmiInstitut Teknologi Sepuluh Nopember1
Yoga Ari TofanInstitut Teknologi Sepuluh Nopember1
Doni Putra PurbawaInstitut Teknologi Sepuluh Nopember1
Azzam Jihad UlhaqInstitut Teknologi Sepuluh Nopember1
Gusna IkhsanInstitut Teknologi Sepuluh Nopember1
Rully SoelaimanInstitut Teknologi Sepuluh Nopember1
Suhadi LiliInstitut Teknologi Sepuluh Nopember1
Muhammad Raihan AninditaInstitut Teknologi Sepuluh Nopember1
Umi Laili YuhanaInstitut Teknologi Sepuluh Nopember1
Muhammad Faisal FananiInstitut Teknologi Sepuluh Nopember1
Bilqis AmaliahInstitut Teknologi Sepuluh Nopember1
Bagaskoro Kuncoro ArdiInstitut Teknologi Sepuluh Nopember1
I Komang Ari MogiInstitut Teknologi Sepuluh Nopember1
Agung Mustika RizkiInstitut Teknologi Sepuluh Nopember1
Bintang NuralamsyahInstitut Teknologi Sepuluh Nopember1
Moch. Nafkhan AlzamzamiInstitut Teknologi Sepuluh Nopember1

Afiliasi berasal dari OpenAlex; metadata OAI-PMH jurnal tidak memuatnya. Penulis tanpa ID OpenAlex dicocokkan berdasarkan nama, sehingga dua orang bisa tergabung atau satu orang bisa terpisah.

Sumber metadata: OAI-PMH (metadata jurnal) http://ijai.iaescore.com/index.php/IJAI/oai, pertama di-harvest 8 Nov 2026, terakhir terlihat 8 Nov 2026.
Metadata asli (oai_dc) 
<oai_dc:dc xmlns="http://www.openarchives.org/OAI/2.0/" xmlns:dc="http://purl.org/dc/elements/1.1/" xmlns:oai_dc="http://www.openarchives.org/OAI/2.0/oai_dc/" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xsi:schemaLocation="http://www.openarchives.org/OAI/2.0/oai_dc/  http://www.openarchives.org/OAI/2.0/oai_dc.xsd">
	<dc:title xml:lang="en-US">Improved performance of fake account classifiers with percentage overlap features selection</dc:title>
	<dc:creator>Tjahyanto, Aris</dc:creator>
	<dc:creator>Pratama, Rivanda Putra</dc:creator>
	<dc:creator>Shiddiqi, Ary Mazharuddin</dc:creator>
	<dc:subject xml:lang="en-US">Machine Learning; Neural Network; Classifier</dc:subject>
	<dc:subject xml:lang="en-US">Fake users; Fake accounts; Feature selection; Bots; Internet</dc:subject>
	<dc:description xml:lang="en-US">Feature selection plays a crucial role in the development of high-performance classification models. We propose an innovative method for detecting fake accounts. This method leverages the percentage overlap technique to refine feature selection. We introduce our technique upon earlier work that showcased the enhanced efficacy of the Naïve Bayesian classifier through dataset normalization. Our study employs a dataset of account profiles sourced from Twitter, which we normalize using the Min-Max method. We analyze the results through a series of comprehensive experiments involving diverse classification algorithms—such as Naïve Bayes, decision tree, k-nearest neighbors (KNN), deep learning, and support vector machines (SVM). Our experimental results demonstrate a 100% accuracy achieved by the SVM and deep learning classifiers. The results are attributed to the percentage overlap technique, which facilitates the identification of four highly informative features. These findings outperform models with more extensive feature sets, underscoring the efficacy of our approach.</dc:description>
	<dc:publisher xml:lang="en-US">Institute of Advanced Engineering and Science</dc:publisher>
	<dc:contributor xml:lang="en-US">Institut Teknologi Sepuluh Nopember</dc:contributor>
	<dc:date>2024-06-01</dc:date>
	<dc:type>info:eu-repo/semantics/article</dc:type>
	<dc:type>info:eu-repo/semantics/publishedVersion</dc:type>
	<dc:type xml:lang="en-US"></dc:type>
	<dc:format>application/pdf</dc:format>
	<dc:identifier>https://ijai.iaescore.com/index.php/IJAI/article/view/24034</dc:identifier>
	<dc:identifier>10.11591/ijai.v13.i2.pp1585-1595</dc:identifier>
	<dc:source xml:lang="en-US">IAES International Journal of Artificial Intelligence (IJ-AI); Vol 13, No 2: June 2024; 1585-1595</dc:source>
	<dc:source>2252-8938</dc:source>
	<dc:source>2089-4872</dc:source>
	<dc:source>10.11591/ijai.v13.i2</dc:source>
	<dc:language>eng</dc:language>
	<dc:relation>https://ijai.iaescore.com/index.php/IJAI/article/view/24034/13960</dc:relation>
	<dc:rights xml:lang="en-US">Copyright (c) 2024 Institute of Advanced Engineering and Science</dc:rights>
	<dc:rights xml:lang="en-US">http://creativecommons.org/licenses/by-sa/4.0</dc:rights>
</oai_dc:dc>