[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-120180-id":3,"doc-seo-120180-113":31,"detail-sidebar-cat-0-id-113":92},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":20,"is_deleted":4,"is_public":21,"is_downloadable":21,"audit_status":21,"page_count":22,"language":23,"language_code":24,"site_id":25,"html_lang":24,"table_of_contents":26,"faqs":27,"seo_title":28,"seo_description":14,"update_tm":29,"read_time":30},120180,2336474466412,"Ezra","https://ap-avatar.wpscdn.com/davatar_155a257f0dc6eb9ab79c44ca47cae57d",52,"Teknologi","PENGEMBANGAN ALGORITMA MACHINE LEARNING UNTUK MENDETEKSI ANOMALI DALAM JARINGAN KOMPUTER","Penelitian ini mengkaji perkembangan terkini pengembangan algoritma machine learning untuk mendeteksi anomali dalam jaringan komputer. Studi literatur mengelompokkan metode dari pendekatan statistik hingga teknik klasifikasi dan clustering, serta menilai kelebihan, kelemahan, kompleksitas, dan tantangan saat menghadapi serangan yang semakin canggih. Penelitian menekankan pentingnya evaluasi performa dengan metrik seperti akurasi, sensitivitas, dan spesifisitas untuk menilai efektivitas algoritma. Temuan menyoroti trade-off antara ketepatan dan efisiensi komputasi, sekaligus mengusulkan integrasi deep learning serta pengujian pada dataset yang lebih beragam.","| Jurnal Review Pendidikan dan Pengajaran [http://journal.universitaspahlawan.ac.id/index.php/jrpp](http://journal.universitaspahlawan.ac.id/index.php/jrpp)[ ](http://journal.universitaspahlawan.ac.id/index.php/jrpp)Volume 7 Nomor1, 2024\u003Cbr>P-2655-710X e-ISSN 2655-6022 | S\u003Cbr>R\u003Cbr>A\u003Cbr>P | ubmitted : 16/01/2024\u003Cbr>eviewed : 17/01/2024\u003Cbr>ccepted : 26/01/2024\u003Cbr>ublished : 27/01/2024 |\n| --- | --- | --- |\n|  |  |  |\n\nGrace Martha  \nGeertruida Bororing1  \nPENGEMBANGAN ALGORITMA MACHINE  \nLEARNING UNTUK MENDETEKSI  \nANOMALI DALAM JARINGAN KOMPUTER  \nAbstrak  \nPenelitian ini menggali perkembangan terkini dalam pengembangan algoritma machine learning untuk mendeteksi anomali dalam jaringan komputer. Studi literatur yang mendalam mengidentifikasi berbagai metode, dari pendekatan berbasis statistik hingga teknik pengklasifikasi dan clustering. Analisis literatur membahas kelebihan dan kelemahan masingmasing metode, sambil menyoroti kompleksitas serta tantangan yang dihadapi dalam mendeteksi serangan yang semakin canggih. Pentingnya evaluasi performa dengan metrik yang tepat, seperti akurasi, sensitivitas, dan spesifisitas, menjadi fokus utama dalam memahamiefektivitas algoritma deteksi anomali. Hasil penelitian memberikan wawasan tentang trade-off antara keakuratan dan efisiensi komputasi, membuka pintu untuk pengembangan algoritma yang dapat memberikan keseimbangan optimal. Saran untuk penelitian selanjutnya mencakupeksplorasi lebih lanjut terhadap integrasi teknik deep learning dan uji coba pada dataset yanglebih bervariasi. Pengembangan algoritma yang adaptif terhadap perubahan taktik penyeranganjuga diusulkan sebagai langkah proaktif dalam menghadapi evolusi serangan. Penelitian inimemiliki implikasi positif terhadap keamanan jaringan komputer, dengan kontribusi padapemahaman mendalam tentang metode deteksi anomali yang dapat memberikan perlindunganyang lebih efektif. Kesimpulan menegaskan bahwa pemahaman lebih lanjut terhadap aspek teknis dan implementasi praktis algoritma machine learning dapat memperkuat pertahananterhadap serangan anomali di lingkunganjaringan komputer.  \nKata Kunci: Machine Learning, Deteksi Anomali, Keamanan Jaringan Komputer, Integrasi Teknologi Terkini.  \nAbstract  \nThis research explores the recent developments in the development of machine learning algorithms for detecting anomalies in computer networks. A comprehensive literature review identifies various methods, ranging from statistically-based approaches to classification and clustering techniques. The literature analysis discusses the strengths and weaknesses of each method while highlighting the complexity and challenges faced in detecting increasingly sophisticated attacks. The importance of performance evaluation using appropriate metrics, such as accuracy, sensitivity, and specificity, is a primary focus in understanding the effectiveness of anomaly detection algorithms. The research findings provide insights into the trade-off between accuracy and computational efficiency, paving the way for the development of algorithms that can strike an optimal balance. Recommendations for further research include further exploration of integrating deep learning techniques and testing on more diverse datasets. The development of algorithms adaptive to changing attack tactics is also suggested as a proactive step in addressing the evolution of attacks. This research has positive implications for computer network security, contributing to a deeper understanding of anomaly detection methods that can  \n1Program Studi Teknik Informatika, Fakultas Komputer dan Komunikasi, Institut Bisnis dan Informatika Kwik Kian Gie  \nemail: [grace.martha@kwikkiangie.ac.id](grace.martha@kwikkiangie.ac.id)  \nJurnal Review Pendidikan dan Pengajaran (JRPP)  \noffer more effective protection. The conclusion emphasizes that further understanding of the technical aspects and practical implementation of machine learning algorithms can strengthen defenses against anomalous attacks in com","cbCaifV7Wit0ljbV","https://ap.wps.com/l/cbCaifV7Wit0ljbV","pdf",684521,4,1,8,"Indonesian","id",113,"# PENDAHULUAN\n## Latar belakang kebutuhan keamanan jaringan\n## Tantangan serangan anomali\n# TINJAUAN PUSTAKA\n## Pendekatan berbasis statistik\n## Teknik klasifikasi dan clustering\n# EVALUASI DAN METRIK\n## Akurasi, sensitivitas, spesifisitas\n## Kompleksitas dan tantangan deteksi","[{\"question\":\"Penelitian ini fokus pada pengembangan algoritma apa?\",\"answer\":\"Penelitian ini berfokus pada pengembangan algoritma machine learning untuk mendeteksi anomali dalam jaringan komputer.\"},{\"question\":\"Metode deteksi anomali apa saja yang dibahas dalam studi literatur?\",\"answer\":\"Studi literatur membahas metode berbasis statistik serta teknik pengklasifikasi dan clustering.\"},{\"question\":\"Mengapa evaluasi performa dengan metrik seperti akurasi dan sensitivitas penting?\",\"answer\":\"Evaluasi performa dengan metrik seperti akurasi, sensitivitas, dan spesifisitas menjadi dasar untuk memahami efektivitas algoritma deteksi anomali.\"}]","PENGEMBANGAN ALGORITMA MACHINE LEARNING UNTUK MENDETEKSI ANOMALI DALAM JARINGAN KOMPUTER | 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ini fokus pada pengembangan algoritma apa?","Question",{"text":76,"@type":77},"Penelitian ini berfokus pada pengembangan algoritma machine learning untuk mendeteksi anomali dalam jaringan komputer.","Answer",{"name":79,"@type":74,"acceptedAnswer":80},"Metode deteksi anomali apa saja yang dibahas dalam studi literatur?",{"text":81,"@type":77},"Studi literatur membahas metode berbasis statistik serta teknik pengklasifikasi dan clustering.",{"name":83,"@type":74,"acceptedAnswer":84},"Mengapa evaluasi performa dengan metrik seperti akurasi dan sensitivitas penting?",{"text":85,"@type":77},"Evaluasi performa dengan metrik seperti akurasi, sensitivitas, dan spesifisitas menjadi dasar untuk memahami efektivitas algoritma deteksi 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