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All Journal Jurnal Teknologi Informasi dan Ilmu Komputer Jurnal Optimasi Sistem Industri
Fajri, Radhiyatul
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Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Engineering Industrial & Manufacturing Engineering Mechanical Engineering

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Feature Selection and Performance Evaluation of Buzzer Classification Model Isnaeni Nurul Afra, Dian; Fajri, Radhiyatul; Annisa Prafitia, Harnum; Arief, Ikhwan; Jasa Mantau, Aprinaldi
Jurnal Optimasi Sistem Industri Vol. 23 No. 1 (2024): Published in July 2024
Publisher : The Industrial Engineering Department of Engineering Faculty at Universitas Andalas

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25077/josi.v23.n1.p1-14.2024

Abstract

In the rapidly evolving digital age, social media platforms have transformed into battleground for shaping public opinion. Among these platforms, X has been particularly susceptible to the phenomenon of 'buzzers', paid or coordinated actors who manipulate online discussions and influence public sentiment. This manipulation poses significant challenges for users, researchers, and policymakers alike, necessitating robust detection measures and strategic feature selection for accurate classification models. This research explores the utilization of various feature selection techniques to identify the most influential features among the 24 features employed in the classification modeling using Support Vector Machine. This study found that selecting 11 key features yields a remarkably effective classification model, achieving an impressive F1-score of 87.54 in distinguishing between buzzer and non-buzzer accounts. These results suggest that focusing on the relevant features can improve the accuracy and efficiency of buzzer detection models. By providing a more robust and adaptable solution to buzzer detection, our research has the potential to advance social media research and policy. This enabling researchers and policymakers to devise strategies aimed at mitigating misinformation dissemination and cultivating an environment of trust and integrity within social media platforms, thus fostering healthier online interactions and discourse.
Feature Selection and Performance Evaluation of Buzzer Classification Model Afra, Dian Isnaeni Nurul; Fajri, Radhiyatul; Prafitia, Harnum Annisa; Arief, Ikhwan; Mantau, Aprinaldi Jasa
Jurnal Optimasi Sistem Industri Vol. 23 No. 1 (2024): Published in July 2024
Publisher : The Industrial Engineering Department of Engineering Faculty at Universitas Andalas

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (318.919 KB) | DOI: 10.25077/josi.v23.n1.p1-14.2024

Abstract

In the rapidly evolving digital age, social media platforms have transformed into battleground for shaping public opinion. Among these platforms, X has been particularly susceptible to the phenomenon of 'buzzers', paid or coordinated actors who manipulate online discussions and influence public sentiment. This manipulation poses significant challenges for users, researchers, and policymakers alike, necessitating robust detection measures and strategic feature selection for accurate classification models. This research explores the utilization of various feature selection techniques to identify the most influential features among the 24 features employed in the classification modeling using Support Vector Machine. This study found that selecting 11 key features yields a remarkably effective classification model, achieving an impressive F1-score of 87.54 in distinguishing between buzzer and non-buzzer accounts. These results suggest that focusing on the relevant features can improve the accuracy and efficiency of buzzer detection models. By providing a more robust and adaptable solution to buzzer detection, our research has the potential to advance social media research and policy. This enabling researchers and policymakers to devise strategies aimed at mitigating misinformation dissemination and cultivating an environment of trust and integrity within social media platforms, thus fostering healthier online interactions and discourse.
Peningkatan Performa Pengenalan Wajah pada Gambar Low-Resolution Menggunakan Metode Super-Resolution Rohim, Muhammad Imaduddin Abdur; Nisa, Auliati; Hindratno, Muhammad Nurkhoiri; Fajri, Radhiyatul; Wibowanto, Gembong Satrio; Lestriandoko, Nova Hadi; Normakristagaluh, Pesigrihastamadya
Jurnal Teknologi Informasi dan Ilmu Komputer Vol 11 No 1: Februari 2024
Publisher : Fakultas Ilmu Komputer, Universitas Brawijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25126/jtiik.20241117947

Abstract

Kartu Tanda Penduduk Elektronik (KTP-el) merupakan identitas wajib bagi penduduk Indonesia. Penyimpanan pada cip KTP-el yang mana selain digunakan untuk menyimpan gambar potret wajah individu, juga harus dapat menyimpan identitas lain seperti biodata, tanda tangan, dan sidik jari kiri dan kanan. Keterbatasan tersebut mengharuskan gambar potret wajah disimpan pada ukuran low-resolution (LR) sehingga sistem pengenalan wajah tidak optimal. Dalam penelitian ini, kami menggunakan Poznan University of Technology (PUT) Face database yang terdiri atas 200 gambar dari 100 individu. Data tersebut dilakukan proses down sampling menggunakan bicubic interpolation untuk menghasilkan data LR. Kami menginvestigasi penggunaan metode super-resolution (SR) berbasis deep learning, termasuk DFDNet, LapSRN, GFPGAN, Real-ESRGAN, Real-ESRGAN+GFPGAN, dan FaceSPARNet. Hal ini bertujuan untuk meningkatkan kualitas gambar LR. Evaluasi performa dilakukan dengan menggunakan matriks False Rejection Rate(FRR) pada beberapa tingkatan False Acceptance Rate (FAR). Hasil penelitian menunjukkan bahwa beberapa metode SR terutama FaceSPARNet menunjukkan peningkatan performa face recognition hingga 2%. Sedangkan, metode SR yang berbasis GAN (GFPGAN, Real-ESRGAN, Real-ESRGAN+GFPGAN) cenderung meningkatkan false reject rate. Penelitian ini menunjukkan bahwa metode SR dari kategori General Basic CNN-based FSR dapat digunakan untuk meningkatkan kinerja face recognition pada gambar LR, seperti pada KTP-el.
Co-Authors Afra, Dian Isnaeni Nurul Annisa Prafitia, Harnum Hindratno, Muhammad Nurkhoiri Ikhwan Arief Isnaeni Nurul Afra, Dian Jasa Mantau, Aprinaldi Mantau, Aprinaldi Jasa Nisa, Auliati Normakristagaluh, Pesigrihastamadya Nova Hadi Lestriandoko Prafitia, Harnum Annisa Rohim, Muhammad Imaduddin Abdur Wibowanto, Gembong Satrio