[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-118776-id":3,"doc-seo-118776-113":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},118776,687207017582,"Himbo","https://ap-avatar.wpscdn.com/davatar_994ba38a5ba835b3df7d355c54d3ed8d",54,"Penelitian & Laporan","ANALISIS PERFORMA ALGORITMA MACHINE LEARNING PADA PERANGKAT EMBEDDED ATMEGA328P","Analisis performa mengevaluasi tiga algoritma machine learning pada perangkat embedded berbasis ATmega328P: K-Nearest Neighbor, Naive Bayes, dan SEFR. Kajian berangkat dari keterbatasan embedded system terkait waktu komputasi, konsumsi daya, dan penggunaan memori. Hasil menunjukkan K-Nearest Neighbor memberikan akurasi paling baik dan konsisten hingga 93.3% pada klasifikasi tiga dataset. Naive Bayes memakai SRAM paling sedikit dengan rata-rata 764 Bytes. SEFR memberikan waktu komputasi tercepat dengan klasifikasi rata-rata 1.16 milidetik serta konsumsi daya 0.1 mili joule.","ANALISIS PERFORMA ALGORITMA MACHINE LEARNING PADAPERANGKAT EMBEDDED ATMEGA328P  \nJeffry Atur Firdaus1, Agung Setia Budi*2, Eko Setiawan3  \n1,2,3Universitas Brawijaya, Malang  \n[Email:](Email:1jeffryaf@student.ub.ac.id)[1](Email:1jeffryaf@student.ub.ac.id)[jeffryaf@student.ub.ac.id](Email:1jeffryaf@student.ub.ac.id), [2](2agungsetiabudi@ub.ac.id)[agungsetiabudi@ub.ac.id](2agungsetiabudi@ub.ac.id), [3](3ekosetiawan@ub.ac.id)[ekosetiawan@ub.ac.id](3ekosetiawan@ub.ac.id)  \n*Penulis Korespondensi  \n(Naskah masuk: 05 Maret 2022, diterima untuk diterbitkan: 10 April 2023)  \nAbstrak  \nArtificial intelligence (AI) merupakan sistem kompleks yang meniru kecerdasan manusia untuk melakukan tugas dan dapat mengembangkan kecerdasannya menggunakan informasi yang mereka kumpulkan. Machine learning yang merupakan bagian dari AI, sering ditemui pada perangkat embedded. Beberapa algoritma machine learning yang banyak dikembangkan pada perangkat embedded adalah K-Nearest Neighbor, Naive Bayes dan SEFR. Padatahun 2019, evaluasipasar embedded system mencapai $100 miliar dan akan diprediksi jumlahnya terus meningkat sebesar enam persen setiap tahunnya. Salah satu perangkat embedded open source yang sering ditemui di pasaran adalah Arduino Nano berbasis ATmega328P. Namun tidak seperti komputer, embedded system memiliki sumber daya yang terbatas. Dalam mengembangkan sistem pada perangkat embedded perlu diperhatikan faktor sepertiwaktu komputasi, daya yang diperlukan dan penggunaan memori. Penelitian ini mengkaji tiga algoritma tersebut untuk mencari algoritma yang paling sesuai di perangkat embedded. Penelitian ini menemukan bahwa dalam melakukan klasifikasi tiga dataset, algoritma K-Nearest Neighbor mendapatkan akurasi paling baik dan paling konsisten hingga 93.3% akurat. Penggunaan sumber daya SRAM paling sedikit didapatkan pada algoritma Naive Bayes dengan rata-rata 764 Bytes. Waktu komputasi paling cepat didapatkan oleh algoritma SEFR dengan waktu yang dibutuhkan untuk melakukan klasifikasi dataset dalam waktu rata-rata 1.16 mili sekon dan konsumsi daya 0.1 mili joule.  \nKata kunci: machine learning, embedded system, k-nearest neighbor, naive bayes, SEFR  \nMACHINE LEARNING ALGORITHM PERFORMANCE ANALYSIS ON ATMEGA328P  \nEMBEDDED DEVICES  \nAbstract  \nArtificial intelligence (AI) is a complex system that imitates human intelligence to perform tasks and can develop their intelligence using the information they collect. Machine learning, which is part of AI, is often encountered in embedded devices. Several machine learning algorithms that have been developed on embedded devices are KNearest Neighbor, Naive Bayes and SEFR. In 2019, the evaluation of the embedded systems market reached $100 billion and is predicted to continue to increase by six percent annually. One of the open source embedded devices that is often found in the market is the Arduino Nano based on the ATmega328P. However, unlike computers, embedded systems have limited resources. In developing systems on embedded devices, factors such as computing time, required power and memory usage must be considered. This study examines these three algorithms to find the most suitable for the embedded device. This study found that in classifying three datasets, the K-Nearest Neighbor algorithm got the best and most consistent accuracy up to 93.3% accurate. The least use of SRAM resources is found in the Naive Bayes algorithm with an average of 764 Bytes. The fastest computation time is obtained by the SEFR algorithm with the time required to classify the dataset in an average time of 1.16 milliseconds and a power consumption of 0.1 milli joules.  \nKeywords: machine learning, embedded system, k-nearest neighbor, naive bayes, SEFR  \n1. PENDAHULUAN  \nArtificial intelligence atau yang biasa disingkat AI merupakan sistem kompleks yang menirukecerdasan manusia untuk melakukan tugas dan  \ndapat mengembangkan kecerdasannya menggunakan informasi yang mereka kumpulkan (Russell & Norvig, 2010) . Salah satu caba","cbCailBieJOxSSe7","https://ap.wps.com/l/cbCailBieJOxSSe7","pdf",1373312,4,1,10,"Indonesian","id",113,"# PENDAHULUAN\n## Latar belakang AI dan machine learning\n## Konsep embedded system dan keterbatasannya\n## Gambaran pasar embedded system\n# METODOLOGI PENELITIAN\n## Algoritma yang diuji: KNN, Naive Bayes, SEFR","[{\"question\":\"Algoritma machine learning apa saja yang dianalisis pada perangkat ATmega328P?\",\"answer\":\"Penelitian mengkaji tiga algoritma: K-Nearest Neighbor (KNN), Naive Bayes, dan Scalable, Efficient, and Fast classifieR (SEFR).\"},{\"question\":\"Bagaimana hasil akurasi terbaik pada klasifikasi dataset?\",\"answer\":\"K-Nearest Neighbor memberikan akurasi terbaik dan paling konsisten hingga 93.3% saat melakukan klasifikasi tiga dataset.\"},{\"question\":\"Parameter performa apa yang dibandingkan pada embedded device?\",\"answer\":\"Perbandingan berfokus pada waktu komputasi, konsumsi daya, serta penggunaan memori/SRAM.\"}]","ANALISIS PERFORMA ALGORITMA MACHINE LEARNING PADA PERANGKAT EMBEDDED ATMEGA328P | PDF",1785720192,15,{"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":87,"head_meta":89,"extra_data":91,"updated_unix":29},"performance-analysis-of-machine-learning-algorithms-on-embedded-device-atmega328p","",{"@graph":37,"@context":86},[38,54,69],{"@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":20},"https://docshare.wps.com/id/document/performance-analysis-of-machine-learning-algorithms-on-embedded-device-atmega328p/118776/",{"url":53,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":24,"description":14,"dateModified":62,"datePublished":63,"encodingFormat":61,"isAccessibleForFree":64,"interactionStatistic":65},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":42,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-16","2026-08-03",true,{"@type":66,"interactionType":67,"userInteractionCount":20},"InteractionCounter",{"@type":68},"ViewAction",{"@type":70,"mainEntity":71},"FAQPage",[72,78,82],{"name":73,"@type":74,"acceptedAnswer":75},"Algoritma machine learning apa saja yang dianalisis pada perangkat ATmega328P?","Question",{"text":76,"@type":77},"Penelitian mengkaji tiga algoritma: K-Nearest Neighbor (KNN), Naive Bayes, dan Scalable, Efficient, and Fast classifieR (SEFR).","Answer",{"name":79,"@type":74,"acceptedAnswer":80},"Bagaimana hasil akurasi terbaik pada klasifikasi dataset?",{"text":81,"@type":77},"K-Nearest Neighbor memberikan akurasi terbaik dan paling konsisten hingga 93.3% saat melakukan klasifikasi tiga dataset.",{"name":83,"@type":74,"acceptedAnswer":84},"Parameter performa apa yang dibandingkan pada embedded device?",{"text":85,"@type":77},"Perbandingan berfokus pada waktu komputasi, konsumsi daya, serta penggunaan memori/SRAM.","https://schema.org",{"og:url":53,"og:type":88,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":90,"canonical":53},"index,follow",{"doc_id":7,"site_id":25},{"code":4,"msg":5,"data":93},[94,99,103,107,111,115,117,121,125,129,133],{"id":95,"doc_module":4,"doc_module_name":47,"category_name":96,"show_sort_weight":97,"slug":98},55,"Agama & Spiritualitas",60,"religion-spirituality",{"id":100,"doc_module":4,"doc_module_name":47,"category_name":101,"show_sort_weight":97,"slug":102},48,"Cerita & Novel","story-novel",{"id":104,"doc_module":4,"doc_module_name":47,"category_name":105,"show_sort_weight":97,"slug":106},56,"Gaya Hidup","lifestyle",{"id":108,"doc_module":4,"doc_module_name":47,"category_name":109,"show_sort_weight":97,"slug":110},51,"Komik","comic",{"id":112,"doc_module":4,"doc_module_name":47,"category_name":113,"show_sort_weight":97,"slug":114},53,"Layanan Kesehatan","healthcare",{"id":11,"doc_module":4,"doc_module_name":47,"category_name":12,"show_sort_weight":97,"slug":116},"research-report",{"id":118,"doc_module":4,"doc_module_name":47,"category_name":119,"show_sort_weight":97,"slug":120},49,"Sastra","literature",{"id":122,"doc_module":4,"doc_module_name":47,"category_name":123,"show_sort_weight":97,"slug":124},52,"Teknologi","technology",{"id":126,"doc_module":4,"doc_module_name":47,"category_name":127,"show_sort_weight":97,"slug":128},50,"Ujian","exam",{"id":130,"doc_module":4,"doc_module_name":47,"category_name":131,"show_sort_weight":97,"slug":132},57,"Umum","general",{"id":134,"doc_module":4,"doc_module_name":47,"category_name":135,"show_sort_weight":4,"slug":136},181,"Formulir","formulir"]