[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-118846-id":3,"doc-seo-118846-113":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},118846,549768064778,"Finn","https://ap-avatar.wpscdn.com/davatar_6f874abed73319feea01a86fa6f0fab8",54,"Penelitian & Laporan","Studi Perbandingan Deteksi Intrusi Jaringan Menggunakan Machine Learning - (Metode SVM dan ANN)","Studi ini membandingkan efektivitas algoritma Support Vector Machine (SVM) dan Artificial Neural Network (ANN) untuk deteksi intrusi jaringan. IDS, IPS, dan firewall tradisional sering mengalami false alarm, kerentanan, serta salah konfigurasi, sehingga penerapan machine learning dinilai lebih efektif. Penelitian menggunakan metode eksperimen dengan melatih serta menguji SVM dan ANN pada Dataset KDD Cup 99 melalui Google Colaboratory. Parameter pembanding meliputi akurasi pelatihan dan pengujian, waktu pelatihan dan pengujian, kurva ROC (AUC), serta kecepatan jaringan. Hasil menunjukkan skor di atas 90%; SVM unggul dengan akurasi 99,87% dan 99,81%, AUC 1 untuk lima kelas, serta waktu pelatihan lebih singkat.","Studi Perbandingan Deteksi Intrusi Jaringan Menggunakan Machine Learning:  \n(Metode SVM dan ANN)  \nComparative Study of Network Intrusion Detection Using Machine Learning:  \n(SVMand ANN Method)  \nTony Tan 1, Hendi Sama2, Gautama Wijaya3, Osei Enoch Aboagye4  \nProgram Studi Sistem Informasi, Universitas Internasional Batam, Indonesia1234 [tony@uib.ac.id](tony@uib.ac.id1)[1](tony@uib.ac.id1), [hendi@uib.ac.id](hendi@uib.ac.id2)[2](hendi@uib.ac.id2), [gautama.wijaya@uib.ac.id](gautama.wijaya@uib.ac.id3)[3](gautama.wijaya@uib.ac.id3), [2031172.osei@uib.edu](2031172.osei@uib.edu) *4  \nAbstrak  \nMachine Learning berkaitan dengan penggunaan algoritma untuk membuat mesin berfungsi. Algoritma supervised machine learning belajar pada dataset untuk membuat prediksi berdasarkan pengetahuan yang mereka peroleh saat belajar. Machine Learning memiliki dampak signifikan dalam keamanan siber. Sistem deteksi intrusi (IDS), sistem pencegahan intrusi (IPS) dan firewall tradisional membantu mendeteksi intrusi tetapi sayangnya, kebanyakan dari mereka memberi false alarm, dapat memiliki kerentanan dan dapat salah konfigurasi. Penggunaan algoritma machine learning telah terbukti lebih efektif dalam deteksi intrusi. Penelitian ini bertujuan untuk membandingkan efektivitas Algoritma Support Vector Machine (SVM) dan Artificial Neural Network (ANN) untuk intrusi deteksi. Penelitian ini menggunakan metode eksperimen dengan melatih dan menguji SVM dan ANN pada Dataset KDD Cup 99 di Google Colaboratory. Skor akurasi pelatihan dan pengujian, waktu pelatihan dan pengujian, Receiver Operating Characteristic Curve (Kurva ROC) dan kecepatan jaringan adalah parameter untuk perbandingan. Hasil dari eksperimen menunjukkan bahwa; Kedua model bagus untuk mendeteksi intrusi karena SVM dan ANN memiliki skor di atas 90% . SVM lebih efektif daripada ANN dalam deteksi intrusi dengan akurasi pelatihan dan pengujian 99,87% dan 99,81% . Juga AUC untuk SVM adalah 1 untuk semua lima kelas dan mengambil lebih sedikit waktu dalam melatih dataset.  \nKata kunci: Machine Learning; Jaringan; Deteksi Intrusi; SVM; ANN.  \nAbstract  \nMachine Learning deals with the use of algorithms to make machines function. Supervised Machine learning algorithms learn on datasets to make predictions based on the knowledge they obtain while learning. Machine Learning have a significant impact on cybersecurity. Intrusion detection systems (IDS), Intrusion prevention systems (IPS) and traditional firewall help to detect intrusion but unfortunately, most of them suffer false alarms, could have vulnerabilities, and could be misconfigured. The use of machine learning models has proved to be more effective in intrusion detection. This study aims to compare the effectiveness of Support Vector Machine (SVM) and Artificial Neural Network (ANN) in detecting intrusions. The study uses an experimental method by training and testing SVM andANN on the KDD Cup 99 Dataset in the Google Colaboratory. Train and test accuracy scores, train and test times, Receiver operating characteristic curve (ROC Curve), and internet speed were the parameters for the comparison. Results from the experiments show that; Both models are good for intrusion detection since SVM and ANN had scores above 90%. SVM is more effective than ANN in intrusion detection with a training and testing accuracy of 99.87% and 99.81%. Also AUC for SVM is 1 for all the five classes and took less time in training the dataset.  \nKeywords: Machine Learning; Network; Intrusion Detection; SVM; ANN.  \nNaskah diterima 16 Juli 2023; direvisi 16 Agustus 2023; dipublikasi 1 September 2023. JATI is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.  \n1. Pendahuluan  \nMachine learning berkaitan dengan perangkat-perangkat, data dan algoritma [1] . Dalam machine learning, data disediakan untuk algoritma untuk dipelajari dan diklasifikasikan [2] . Ada dua jenis machine learning yaitu; supervised machine learning dan unsupervised machine le","cbCaitxzauCL0LhF","https://ap.wps.com/l/cbCaitxzauCL0LhF","pdf",487951,8,1,13,"Indonesian","id",113,"# Pendahuluan\n## Konsep machine learning dan jenisnya\n## Relevansi machine learning untuk keamanan siber\n# Metode dan perbandingan\n## Dataset dan lingkungan eksperimen\n## Parameter evaluasi (akurasi, waktu, ROC, kecepatan)\n# Hasil penelitian\n## Performa SVM vs ANN\n## Nilai akurasi dan AUC","[{\"question\":\"Mengapa machine learning digunakan untuk deteksi intrusi jaringan?\",\"answer\":\"IDS, IPS, dan firewall tradisional sering mengalami false alarm, kerentanan, dan salah konfigurasi. Machine learning dinilai lebih efektif karena mampu mempelajari pola traffic dan memprediksi intrusi/anomali.\"},{\"question\":\"Apa metode eksperimen yang digunakan untuk membandingkan SVM dan ANN?\",\"answer\":\"Penelitian melatih dan menguji SVM serta ANN pada Dataset KDD Cup 99 menggunakan Google Colaboratory, lalu membandingkan performanya berdasarkan metrik yang ditetapkan.\"},{\"question\":\"Apa temuan utama terkait performa SVM dibanding ANN?\",\"answer\":\"Kedua model memiliki skor di atas 90%, namun SVM lebih efektif. Akurasi pelatihan dan pengujian SVM mencapai 99,87% dan 99,81%, dengan AUC bernilai 1 untuk semua lima kelas serta waktu pelatihan lebih singkat.\"}]","Studi Perbandingan Deteksi Intrusi Jaringan Menggunakan Machine Learning - (Metode SVM dan ANN) | PDF",1785720595,20,{"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-network-intrusion-detection-using-machine-learning-svm-and-ann-methods","",{"@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-network-intrusion-detection-using-machine-learning-svm-and-ann-methods/118846/",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","2026-08-18","2026-08-03",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 machine learning digunakan untuk deteksi intrusi jaringan?","Question",{"text":77,"@type":78},"IDS, IPS, dan firewall tradisional sering mengalami false alarm, kerentanan, dan salah konfigurasi. Machine learning dinilai lebih efektif karena mampu mempelajari pola traffic dan memprediksi intrusi/anomali.","Answer",{"name":80,"@type":75,"acceptedAnswer":81},"Apa metode eksperimen yang digunakan untuk membandingkan SVM dan ANN?",{"text":82,"@type":78},"Penelitian melatih dan menguji SVM serta ANN pada Dataset KDD Cup 99 menggunakan Google Colaboratory, lalu membandingkan performanya berdasarkan metrik yang ditetapkan.",{"name":84,"@type":75,"acceptedAnswer":85},"Apa temuan utama terkait performa SVM dibanding ANN?",{"text":86,"@type":78},"Kedua model memiliki skor di atas 90%, namun SVM lebih efektif. Akurasi pelatihan dan pengujian SVM mencapai 99,87% dan 99,81%, dengan AUC bernilai 1 untuk semua lima kelas serta waktu pelatihan lebih singkat.","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"]