[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-118351-id":3,"doc-seo-118351-113":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},118351,687207022233,"Riley","https://ap-avatar.wpscdn.com/davatar_155a257f0dc6eb9ab79c44ca47cae57d",54,"Penelitian & Laporan","Analisis Perbandingan Kinerja Algoritma Machine Learning Berbasis Feature Selection dalam Deteksi Serangan Botnet - Fokus Utama Penelitian","Perkembangan internet yang pesat meningkatkan jumlah perangkat terhubung, sehingga keamanan siber menjadi isu kritis dan memperluas peluang serangan. Salah satu ancaman utama adalah botnet, yang pada Indonesia tercatat sebagai anomali trafik tertinggi pada 2022. Tantangan deteksi muncul dari kompleksitas data, kesulitan mengidentifikasi serangan secara akurat, serta false detection sehingga data normal dianggap sebagai serangan. Penelitian ini menerapkan machine learning berbasis feature selection dengan fokus pada pola serangan botnet dan pemisahan data normal.","ANALISIS PERBANDINGAN KINERJA ALGORITMA MACHINE LEARNING BERBASIS FEATURE SELECTION DALAM DETEKSI  \nSERANGAN BOTNET  \nRio1, Koko Handoko2  \n1Mahasiswa Program Studi Teknik Informatika , Universitas Putera Batam  \n2Dosen Program Studi Teknik Informatika , Universitas Putera Batam  \nemail: [pb210210013@upbatam.ac.id](pb210210013@upbatam.ac.id)  \nABSTRACT  \nInternet has experienced significant development. Increasing devices connected to internet makes security against cyber attacks a critical issue, thus creates opportunities for cyber attackers, one form of those attack is botnets. In Indonesia, Botnets is the highest traffic anomalies in 2022 by BSSN. High number of attacks because detecting botnet can be challenging, difficulty of detecting attacks and low level of detection accuracy means that normal data sometimes considered an attack, so choosing method that can handle this is very important. Machine learning algorithms are able to study network data traffic and identify suspicious activity, this makes machine learning an effective method. Machine learning based on feature selection has an accuracy of above 90% in detecting DDoS attacks on datasets and machine learning algorithms are also able to detect attack data and normal data. Thus, in this research machine learning algorithms such as K-Nearest Neighbors, Support Vector Machine and Naive Bayes will be applied to dataset containing botnet and normal data to explore how machine learning algorithms can effectively detect botnet attack patterns and normal data. This research compares the performance of commonly used machine learning algorithms to find which one effective for detecting botnet attacks in existing datasets.  \nKeywords: Botnet, Dataset, Feature selection, K-Nearset Neigbors, Machine Learning, Naive Bayes, Support Vector Machine  \nPENDAHULUAN  \nPenggunaan internet mengalami perkembangan yang signifikan hal iniditandai dengan mudahnya mendapatkanakses ke internet dimana kemajuan teknologi informasi menjadi pendukung dari perkembangan penggunaan internet yang secara langsung juga meningkatkan perkembangan teknologi jaringan data baik secara lokal maupun global. Meningkatnya jumlah perangkat sepertikomputer dan ponsel yang terhubung  \npada internet membuat keamanan akanserangan siber menjadi isu kritis dengan banyaknya perangkat ini membuat peluang lebih banyak celah bagipenyerang siber untuk melakukan serangan terhadap perangkat tersebut yang mana salah satunya adalah serangan botnet.  \nDi Indonesia sendiri, MyloBot salah satu bentuk dari Botnet menjadi sumbertrafik anomali tertinggi pada tahun 2022 dengan 254.260.339 jumlah kasus  \nsebagaimana tertera dalam dokumen“Lanskap Keamanan Siber Indonesia Tahun 2022” oleh BSSN. Alasan mengapa tingginya angka serangan botnet disebabkan karena deteksi serangan botnet bisa menjadi sebuah tantangan, sulitnya mendeteksi serangannya dan rendahnya tingkat akurasipendeteksian serangan menyebabkan tingginya false detection sehingga data normal dianggap menjadi serangan. Botnet ini kumpulan dari aplikasi bot (robot) yang dikonfigurasi untuk dapat berjalan secara otomatis dalam jaringanmaka setiap komputer yang telah terinfeksi dan tergabung dalam jaringan botnet akan mengeksekusi perintah atau instruksi yang diberikan oleh Botmaster dengan dilakukan dari jarak jauh. Botnet mampu menyediakan platform yang dapat didistribusikan pada kegiatan illegalseperti spam, phishing, click fraud, pencurian kata sandi dan Distributed Denial of Service (DdoS) (Xing et al. , 2021) .  \nMetode yang akan digunakan dalampenelitian ini menggunakan machine learning. Algoritma machine learning mampu mempelajari data trafik jaringandan mengidentifikasi aktivitas yang mencurigakan, hal ini membuat machine learning menjadi metode yang efektif. Machine learning dengan berbasis fiturseleksi memiliki akurasi diatas 90% dalam mendeteksi serangan DdoS pada dataset(Maslan et al. , 2020) , algoritma machine learning juga mampu mendeteksi serangan dan data normal","cbCaib6SB2GRw82t","https://ap.wps.com/l/cbCaib6SB2GRw82t","pdf",524236,5,1,10,"Indonesian","id",113,"# Pendahuluan\n## Latar Belakang Masalah Botnet\n## Tantangan Deteksi Botnet dan False Detection\n# Kajian Teori\n## Botnet\n## Algoritma Machine Learning","[{\"question\":\"Mengapa deteksi serangan botnet sulit dilakukan?\",\"answer\":\"Deteksi botnet sulit karena kompleksitas data, fitur yang tidak relevan dapat menurunkan performa model, serta akurasi deteksi yang rendah memicu false detection saat data normal dianggap serangan.\"},{\"question\":\"Apa peran feature selection dalam penelitian ini?\",\"answer\":\"Feature selection digunakan untuk mengurangi kompleksitas data dan meningkatkan akurasi deteksi dengan tetap menjaga keterwakilan dataset.\"},{\"question\":\"Algoritma machine learning apa saja yang dibandingkan untuk mendeteksi botnet?\",\"answer\":\"Penelitian membandingkan kinerja K-Nearest Neighbors, Support Vector Machine, dan Naive Bayes pada dataset yang berisi data botnet dan data normal.\"}]","Analisis Perbandingan Kinerja Algoritma Machine Learning Berbasis Feature Selection dalam Deteksi Serangan Botnet - Fokus Utama Penelitian | PDF",1785683241,15,{"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},"performance-comparison-analysis-of-feature-selection-based-machine-learning-algorithms-in-detecting-botnet-attacks-research-focus","",{"@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/performance-comparison-analysis-of-feature-selection-based-machine-learning-algorithms-in-detecting-botnet-attacks-research-focus/118351/",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","2026-08-16","2026-08-02",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 deteksi serangan botnet sulit dilakukan?","Question",{"text":77,"@type":78},"Deteksi botnet sulit karena kompleksitas data, fitur yang tidak relevan dapat menurunkan performa model, serta akurasi deteksi yang rendah memicu false detection saat data normal dianggap serangan.","Answer",{"name":80,"@type":75,"acceptedAnswer":81},"Apa peran feature selection dalam penelitian ini?",{"text":82,"@type":78},"Feature selection digunakan untuk mengurangi kompleksitas data dan meningkatkan akurasi deteksi dengan tetap menjaga keterwakilan dataset.",{"name":84,"@type":75,"acceptedAnswer":85},"Algoritma machine learning apa saja yang dibandingkan untuk mendeteksi botnet?",{"text":86,"@type":78},"Penelitian membandingkan kinerja K-Nearest Neighbors, Support Vector Machine, dan Naive Bayes pada dataset yang berisi data botnet dan data normal.","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"]