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      Integrasi Algoritma Boyer-Moore Dalam Framework Ptes Untuk Rekomendasi Solusi Kerentanan Keamanan Jaringan Wifi

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      Date
      2025
      Author
      Imran, Ali
      Neyman, Shelvie Nidya
      Rahmawan, Hendra
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      Abstract
      Pengguna internet saat ini mengalami pertumbuhan yang signifikan seiring perkembangan zaman. Perkembangan internet turut mempengaruhi kemajuan jaringan WiFi yang berperan penting dalam menyediakan akses layanan jaringan internet. Kebutuhan internet yang meningkat berjalan seiring dengan kemajuan teknologi informasi, tercermin dalam penerapan industri 4.0 dan industri 5.0, yang memerlukan internet untuk melakukan pertukaran data. Oleh karena itu, jaringan WiFi menjadi titik akses yang krusial dan perlu diperhatikan aspek kehandalannya demi peningkatan keamanan jaringan internet. Kehandalan keamanan jaringan WiFi dapat dianalisis dengan menerapkan penetration testing atau pentest. Pentest pada jaringan WiFi dilakukan untuk mengaudit dan menilai kehandalan keamanan jaringan tersebut. Beberapa framework yang umum digunakan dalam pentest meliputi PTES, PETA, NIST SP 800-115, OWASP, OSSTMM, dan ISSAF. Di antara berbagai framework tersebut, PTES merupakan framework yang sesuai dan relevan untuk penerapan pentest pada jaringan WiFi karena pendekatannya yang komprehensif dan sistematis. Penelitian ini bertujuan mengintegrasikan algoritma Boyer-Moore ke dalam framework PTES untuk mendukung rekomendasi solusi kerentanan keamanan pada jaringan WiFi. Metode yang diterapkan dalam penelitian ini meliputi penambahan proses suggestions dengan penerapan algoritma Boyer-Moore untuk melakukan string matching pada kumpulan data solusi pentest. Proses ini berjalan setelah mendapatkan identifikasi kerentanan saat melakukan pentest, di mana hasil temuan dijadikan input untuk suggestions guna mencari dan memberikan rekomendasi solusi yang sesuai. Hasil pengujian menunjukkan bahwa framework PTES yang telah diintegrasikan dengan algoritma Boyer-Moore mampu memberikan rekomendasi solusi secara efektif terhadap kerentanan yang terdeteksi. Algoritma Boyer-Moore dinilai cepat dalam proses pencarian data solusi, dengan kecepatan 0,0000087 detik dalam memberikan rekomendasi.
       
      The number of internet users has grown significantly over time. This growth has influenced the advancement of WiFi networks, which play a crucial role in providing internet access services. The increasing demand for internet is aligned with the progress of information technology, reflected in the implementation of Industry 4.0 and Industry 5.0, both of which require internet connectivity for data exchange. Therefore, WiFi networks become critical access points that must be carefully secured to enhance internet network safety. The reliability of WiFi network security can be analyzed through penetration testing (pentest). Pentesting of WiFi networks is conducted to audit and assess their security robustness. Several frameworks are used in pentesting, including PTES, PETA, NIST SP 800-115, OWASP, OSSTMM, and ISSAF. Among these, the PTES framework is considered appropriate and relevant for pentesting WiFi networks due to its comprehensive and systematic approach. This study aims to integrate the Boyer-Moore algorithm into the PTES framework to support recommendations for solutions to WiFi network security vulnerabilities. The method employed in this research includes adding a suggestions process that applies the Boyer-Moore algorithm for string matching against a dataset of pentest solutions. This process is conducted after identifying vulnerabilities during the pentesting phase, where the findings serve as input for the suggestion tool that searches and provides suitable solution recommendations efficiently and accurately. Test results indicate that the PTES framework integrated with the Boyer-Moore algorithm effectively offers solution recommendations for detected vulnerabilities. The Boyer-Moore algorithm proved to be fast in the solution search process, achieving a speed of 0,0000087 seconds in delivering recommendations.
       
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      http://repository.ipb.ac.id/handle/123456789/168891
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      • MT - School of Data Science, Mathematic and Informatics [69]

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      Copyright © 2020 Library of IPB University
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      Contact Us | Send Feedback
      Indonesia DSpace Group 
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