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Model yang digunakan adalah Decision Tree dan Random Forest, dengan Random Forest mencapai akurasi 92% dibanding Decision Tree 87%.",{"@graph":14,"@context":73},[15,34,56],{"@type":16,"itemListElement":17},"BreadcrumbList",[18,23,27,31],{"item":19,"name":20,"@type":21,"position":22},"https://docshare.wps.com","Home","ListItem",1,{"item":24,"name":25,"@type":21,"position":26},"https://docshare.wps.com/id/document/","Document",2,{"item":28,"name":29,"@type":21,"position":30},"https://docshare.wps.com/id/document/penelitian-laporan/","Penelitian & 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ini mendeteksi serangan siber menggunakan pendekatan apa?","Question",{"text":63,"@type":64},"Penelitian ini mengembangkan sistem deteksi serangan siber berbasis machine learning dengan memanfaatkan data log aktivitas pada sistem informasi akademik.","Answer",{"name":66,"@type":61,"acceptedAnswer":67},"Algoritma machine learning apa yang digunakan dalam penelitian ini?",{"text":68,"@type":64},"Algoritma yang digunakan adalah Decision Tree dan Random Forest untuk klasifikasi serta interpretasi hasil deteksi.",{"name":70,"@type":61,"acceptedAnswer":71},"Bagaimana hasil evaluasi performa kedua algoritma?",{"text":72,"@type":64},"Random Forest lebih unggul dengan akurasi 92% dalam mendeteksi serangan, sedangkan Decision Tree mencapai 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Serangan Siber Menggunakan Machine Learning: Studi Pada Sistem Informasi Akademik  \nCantika Chandraa, Dio Prima Mulyab, Faradikac  \naProgram Studi Sistem Informasi, Universitas Dharma Andalas, [cantikachandra27@gmail.com](cantikachandra27@gmail.com)  \nAbstract  \nCybersecurity is a critical issue in academic information systems, which store sensitive data such as student grades, identities, and administrative documents. Attacks such as SQL injection, brute force login attempts, and unauthorized access can lead to significant losses and operational disruptions. This study aims to develop a cyberattack detection system using machine learning algorithms capable of identifying abnormal (anomalous) activities within the system. The algorithms applied are Decision Tree and Random Forest due to their strengths in classification and result interpretation. The research was conducted by collecting user activity log data, performing data cleaning, labeling the data, and training machine learning models. Evaluation results show that Random Forest outperforms Decision Tree with an accuracy of 92% in detecting attacks, compared to 87% achieved by Decision Tree. The implementation of this system can assist campus IT departments in improving the speed and effectiveness of cyber threat prevention and response.  \nKeywords: Cybersecurity, Academic Information System, Machine Learning, Attack Detection, Random Forest  \nAbstrak  \nKeamanan siber menjadi isu krusial dalam sistem informasi akademik yang menyimpan data sensitif seperti nilai, identitas mahasiswa, dan dokumen administrasi. Serangan seperti SQL injection, brute force login, hingga akses ilegal dapat menyebabkan kerugian besar dan gangguan operasional. Penelitian ini bertujuan mengembangkan sistem deteksi serangan siber menggunakan algoritma machine learning yang mampu mengenali aktivitas tidak normal (anomali) dalam sistem. Algoritma yang digunakan adalah Decision Tree dan Random Forest karena kemampuannya dalam klasifikasi dan interpretasi hasil. Penelitian dilakukan dengan mengumpulkan data log aktivitas pengguna, membersihkannya, melabeli data, kemudian melatih model ML. Hasil evaluasi menunjukkan bahwa Random Forest lebih unggul dengan akurasi 92% dalam mendeteksi serangan dibandingkan Decision Tree yang mencapai 87% . Penerapan sistem ini mampu membantu pihak IT kampus melakukan pencegahan dan respons lebih cepat terhadap ancaman siber.  \nKata kunci: Keamanan Siber, Sistem Informasi Akademik, Machine Learning, Deteksi Serangan, Random Forest This work is licensed under Creative Commons Attribution License 4.0 CC-BY International license  \nPENDAHULUAN  \n1.1. Latar Belakang  \nPerkembangan teknologi informasi yang pesat telah mendorong berbagai institusi pendidikan untuk mengadopsi sistem informasi akademik berbasis digital. Sistem ini tidak hanya mempermudah prosesadministrasi dan manajemen data, tetapi juga menjadi tulang punggung dalam penyelenggaraan layanan akademik, seperti pengisian KRS, penilaian, absensi, hingga pengelolaan data pribadi mahasiswa dan dosen. Dengan semakin meningkatnya ketergantungan terhadap sistem ini, aspek keamanan data (cybersecurity) menjadi sangat penting untuk diperhatikan.  \nSalah satu tantangan utama dalam pengelolaan sistem informasi akademik adalah ancaman serangan siber, seperti SQL injection, brute force login, phishing, dan akses ilegal oleh pihak yang tidak berwenang. Seranganserangan ini dapat mengakibatkan kebocoran data, manipulasi informasi, bahkan kerusakan sistem secarakeseluruhan. Sayangnya, banyak institusi pendidikan belum memiliki sistem deteksi dini yang handal untuk mengenali dan merespon serangan secara real-time.  \nUntuk mengatasi hal tersebut, teknologi Machine Learning (ML) menawarkan solusi inovatif. Dengankemampuannya dalam menganalisis data dalam jumlah besar dan mengenali pola yang tidak lazim (anomali), machine learning dapat digunakan sebagai alat bantu dalam mendeteksi aktivitas mencurigakan yang mengindikasikan po","cbCaimAaPFkiCdpJ","https://ap.wps.com/l/cbCaimAaPFkiCdpJ","pdf",374492,5,"Indonesian","# PENDAHULUAN\n## Latar Belakang\n## Perumusan Masalah\n## Tujuan Penelitian\n# METODE PENELITIAN\n## Jenis dan Pendekatan Penelitian\n## Objek Penelitian\n## Sumber Data dan Teknik Pengumpulan Data","[{\"question\":\"Penelitian ini mendeteksi serangan siber menggunakan pendekatan apa?\",\"answer\":\"Penelitian ini mengembangkan sistem deteksi serangan siber berbasis machine learning dengan memanfaatkan data log aktivitas pada sistem informasi akademik.\"},{\"question\":\"Algoritma machine learning apa yang digunakan dalam penelitian ini?\",\"answer\":\"Algoritma yang digunakan adalah Decision Tree dan Random Forest untuk klasifikasi serta interpretasi hasil deteksi.\"},{\"question\":\"Bagaimana hasil evaluasi performa kedua algoritma?\",\"answer\":\"Random Forest lebih unggul dengan akurasi 92% dalam mendeteksi serangan, sedangkan Decision Tree mencapai 87%.\"}]","Deteksi Serangan Siber Menggunakan Machine Learning - Studi Pada Sistem Informasi Akademik | PDF"]