[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-124251-id":3,"doc-seo-124251-113":31,"detail-sidebar-cat-0-id-113":93},{"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},124251,549768702563,"Sage","https://ap-avatar.wpscdn.com/avatar/8000c4aa63b76e948b?x-image-process=image/resize,m_fixed,w_180,h_180&k=1786536092046926083",54,"Penelitian & Laporan","Perbandingan Algoritma Machine Learning untuk Intrusion Detection System pada Dataset NSL-KDD - Comparative Study","Era digital mendorong serangan siber menjadi semakin kompleks, sehingga IDS berbasis aturan statis sering kurang mampu mengenali pola serangan baru. Penelitian ini merancang dan mengimplementasikan model machine learning untuk mendeteksi intrusi jaringan secara efektif dengan meminimalkan latensi melalui analisis komparatif algoritma Decision Tree, Random Forest, Support Vector Machine (SVM), dan Boosting. Data NSL-KDD diproses melalui transformasi, pembersihan, normalisasi, serta pembagian pelatihan dan pengujian. Evaluasi menggunakan akurasi, presisi, recall, F1-score, dan analisis waktu pelatihan/prediksi. Hasil menunjukkan Boosting mencapai akurasi 99,36% dan lebih andal pada kelas minoritas meski membutuhkan waktu pelatihan lebih lama.","PerbandinganAlgoritma Machine Learning untuk Intrusion Detection System pada Dataset NSL-KDD  \nA Comparative Study ofMachine Learning Algorithms for Intrusion Detection  \nSystems using theNSL-KDD Dataset  \n1Rulyansyah Permata Putra, 2Amarudin*  \n1Program Studi Teknologi Informasi, Fakultas Teknik dan Ilmu Komputer, Universitas Teknokrat  \nIndonesia  \n2Program Studi Ilmu Komputer, Fakultas Teknik dan Ilmu Komputer, Universitas Teknokrat  \nIndonesia  \n1,2Jl. Zainal Abidin Pagaralam No.9-11 Labuhan Ratu, Bandar Lampung, Indonesia  \n*e-mail: [rulyansyah_permata_putra@teknokrat.ac.id](rulyansyah_permata_putra@teknokrat.ac.id), [amarudin@teknokrat.ac.id](amarudin@teknokrat.ac.id)  \n(received: 25 April 2025, revised: 24 May 2025, accepted: 25 May 2025)  \nAbstrak  \nDi era digital saat ini, serangan siber berkembang semakin kompleks, sehingga sistem deteksi intrusi (IDS) berbasis aturan statis sering kali gagal mengenali pola serangan baru. Tujuan utama penelitian adalah merancang dan mengimplementasikan model machine learning untuk mendeteksi intrusijaringan komputer secara efektif dengan meminimalkan latensi, melalui analisis komparatif antara beberapa algoritma, yaitu Decision Tree, Random Forest, Support Vector Machine (SVM), dan Boosting. Metode penelitian yang digunakan meliputi pengumpulan dataset NSL-KDD, proses transformasi data, pembersihan, normalisasi, dan pembagian data menjadi set pelatihan dan pengujian. Masing-masing algoritma dilatih dengan parameter yang telah disesuaikan, dan performanya dievaluasi menggunakan metrik akurasi, presisi, recall, f1-score, serta analisis waktu pelatihan dan prediksi. Hasil penelitian menunjukkan bahwa algoritma Boosting menonjol dengan tingkat akurasimencapai 99,36% . Boosting juga terbukti lebih handal dalam mendeteksi kelas minoritas meskimemerlukan waktu pelatihan lebih lama. Penerapan metode machine learning, khususnya Boosting, merupakan pendekatan efektif untuk meningkatkan deteksi intrusi yang dapat dijadikan dasar bagipengembangan sistem keamanan siber lebih adaptif dan andal.  \nKata kunci: intrusion detection system, boosting, machine learning, NSL-KDD, keamanan siber  \nAbstract  \nIn today’s digital era, cyberattacks are becoming increasingly complex, rendering traditional rulebased Intrusion Detection Systems (IDS) often ineffective in recognizing new attack patterns. The primary objective of this study is to design and implement a machine learning model for detecting network intrusions efficiently while minimizing latency, through a comparative analysis of several algorithms: Decision Tree, Random Forest, Support Vector Machine (SVM), and Boosting. The research methodology includes the collection of the NSL-KDD dataset, followed by data transformation, cleaning, normalization, and partitioning into training and testing sets. Each algorithm was trained using tuned parameters, and performance was evaluated using metrics such as accuracy, precision, recall, F1-score, and an analysis of training and prediction time. The results indicate that the Boosting algorithm stands out, achieving an accuracy rate of 99.36%. Boosting also demonstrated greater reliability in detecting minority classes, despite requiring longer training times. The application of machine learning methods—particularly Boosting—proves to be an effective approach to enhancing intrusion detection and can serve as a foundation for developing more adaptive and reliable cybersecurity systems.  \nKeywords: intrusion detection system, boosting, machine learning, NSL-KDD, cyber security  \n1 Pendahuluan  \nKeamanan sistem informasi menjadi aspek krusial dalam dunia digital yang terus berkembang pesat. Ancaman siber, seperti serangan peretasan, pencurian data, dan penyusupan jaringan, semakin kompleks dan sulit dideteksi. Seiring dengan meningkatnya jumlah data yang mengalir dalam suatusistem, metode konvensional dalam mendeteksi serangan siber sering kali kurang efektif dalam mengenali pola serangan yang baru dan canggih. Ol","cbCaisbKilKUDAo9","https://ap.wps.com/l/cbCaisbKilKUDAo9","pdf",766528,6,1,11,"Indonesian","id",113,"# Pendahuluan\n## Latar belakang keamanan sistem informasi dan IDS\n## Keterbatasan IDS berbasis aturan statis\n## Integrasi machine learning pada IDS\n## Tujuan penelitian dan algoritma yang digunakan","[{\"question\":\"Mengapa IDS berbasis aturan statis sering gagal mendeteksi serangan baru?\",\"answer\":\"Karena serangan baru belum memiliki pola yang terdefinisi dalam database aturan yang ada, sehingga IDS statis sulit mengenali variasi ancaman tersebut.\"},{\"question\":\"Algoritma apa saja yang dibandingkan dalam penelitian ini?\",\"answer\":\"Penelitian membandingkan Decision Tree, Random Forest, Support Vector Machine (SVM), dan Boosting.\"},{\"question\":\"Apa hasil utama yang menunjukkan keunggulan metode Boosting?\",\"answer\":\"Boosting menonjol dengan akurasi 99,36% dan lebih handal mendeteksi kelas minoritas, meskipun waktu pelatihan cenderung lebih lama.\"}]","Perbandingan Algoritma Machine Learning untuk Intrusion Detection System pada Dataset NSL-KDD - Comparative Study | PDF",1785821227,17,{"code":4,"msg":32,"data":33},"ok",{"site_id":25,"language":24,"slug":34,"title":13,"keywords":35,"description":14,"schema_data":36,"social_meta":88,"head_meta":90,"extra_data":92,"updated_unix":29},"comparative-study-of-machine-learning-algorithms-for-intrusion-detection-system-on-the-nsl-kdd-dataset","",{"@graph":37,"@context":87},[38,55,70],{"@type":39,"itemListElement":40},"BreadcrumbList",[41,45,49,52],{"item":42,"name":43,"@type":44,"position":21},"https://docshare.wps.com","Home","ListItem",{"item":46,"name":47,"@type":44,"position":48},"https://docshare.wps.com/id/document/","Document",2,{"item":50,"name":12,"@type":44,"position":51},"https://docshare.wps.com/id/document/penelitian-laporan/",3,{"item":53,"name":13,"@type":44,"position":54},"https://docshare.wps.com/id/document/comparative-study-of-machine-learning-algorithms-for-intrusion-detection-system-on-the-nsl-kdd-dataset/124251/",4,{"url":53,"name":13,"@type":56,"author":57,"headline":13,"publisher":59,"fileFormat":62,"inLanguage":24,"description":14,"dateModified":63,"datePublished":64,"encodingFormat":62,"isAccessibleForFree":65,"interactionStatistic":66},"DigitalDocument",{"name":9,"@type":58},"Person",{"url":42,"name":60,"@type":61},"DocShare","Organization","application/pdf","2026-08-17","2026-08-04",true,{"@type":67,"interactionType":68,"userInteractionCount":20},"InteractionCounter",{"@type":69},"ViewAction",{"@type":71,"mainEntity":72},"FAQPage",[73,79,83],{"name":74,"@type":75,"acceptedAnswer":76},"Mengapa IDS berbasis aturan statis sering gagal mendeteksi serangan baru?","Question",{"text":77,"@type":78},"Karena serangan baru belum memiliki pola yang terdefinisi dalam database aturan yang ada, sehingga IDS statis sulit mengenali variasi ancaman tersebut.","Answer",{"name":80,"@type":75,"acceptedAnswer":81},"Algoritma apa saja yang dibandingkan dalam penelitian ini?",{"text":82,"@type":78},"Penelitian membandingkan Decision Tree, Random Forest, Support Vector Machine (SVM), dan Boosting.",{"name":84,"@type":75,"acceptedAnswer":85},"Apa hasil utama yang menunjukkan keunggulan metode Boosting?",{"text":86,"@type":78},"Boosting menonjol dengan akurasi 99,36% dan lebih handal mendeteksi kelas minoritas, meskipun waktu pelatihan cenderung lebih lama.","https://schema.org",{"og:url":53,"og:type":89,"og:title":13,"og:site_name":60,"og:description":14},"article",{"robots":91,"canonical":53},"index,follow",{"doc_id":7,"site_id":25},{"code":4,"msg":5,"data":94},[95,100,104,108,112,116,118,122,126,130,134],{"id":96,"doc_module":4,"doc_module_name":47,"category_name":97,"show_sort_weight":98,"slug":99},55,"Agama & Spiritualitas",60,"religion-spirituality",{"id":101,"doc_module":4,"doc_module_name":47,"category_name":102,"show_sort_weight":98,"slug":103},48,"Cerita & Novel","story-novel",{"id":105,"doc_module":4,"doc_module_name":47,"category_name":106,"show_sort_weight":98,"slug":107},56,"Gaya Hidup","lifestyle",{"id":109,"doc_module":4,"doc_module_name":47,"category_name":110,"show_sort_weight":98,"slug":111},51,"Komik","comic",{"id":113,"doc_module":4,"doc_module_name":47,"category_name":114,"show_sort_weight":98,"slug":115},53,"Layanan Kesehatan","healthcare",{"id":11,"doc_module":4,"doc_module_name":47,"category_name":12,"show_sort_weight":98,"slug":117},"research-report",{"id":119,"doc_module":4,"doc_module_name":47,"category_name":120,"show_sort_weight":98,"slug":121},49,"Sastra","literature",{"id":123,"doc_module":4,"doc_module_name":47,"category_name":124,"show_sort_weight":98,"slug":125},52,"Teknologi","technology",{"id":127,"doc_module":4,"doc_module_name":47,"category_name":128,"show_sort_weight":98,"slug":129},50,"Ujian","exam",{"id":131,"doc_module":4,"doc_module_name":47,"category_name":132,"show_sort_weight":98,"slug":133},57,"Umum","general",{"id":135,"doc_module":4,"doc_module_name":47,"category_name":136,"show_sort_weight":4,"slug":137},181,"Formulir","formulir"]