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Fokus diberikan pada perilaku pengendara, khususnya ibu-ibu di Indonesia, terkait kelalaian memberikan sinyal saat berbelok atau berpindah jalur. Data EEG dikumpulkan dari 100 pengendara dan digunakan untuk melatih model dengan algoritma SVM, KNN, dan Random Forest. Hasil eksperimen menunjukkan pengenalan pola sinyal otak yang efektif dengan akurasi tertinggi 92%, sensitivitas 89%, dan spesifisitas 91%. Implementasi sistem diharapkan menekan risiko kecelakaan akibat kelalaian sinyal saat berkendara.","|  | SNESTIK\u003Cbr>Seminar Nasional Teknik Elektro, Sistem Informasi, dan Teknik Informatika\u003Cbr>[https://ejurnal.itats.ac.id/snestik dan](https://ejurnal.itats.ac.id/snestik dan) [https://snestik.itats.ac.id](https://snestik.itats.ac.id) |  |\n| --- | --- | --- |\n| Informasi Pelaksanaan :\u003Cbr>SNESTIK V-Surabaya, 26 April 2025\u003Cbr>Fakultas Teknik Elektro dan Teknologi Informasi, Institut Teknologi Adhi Tama Surabaya\u003Cbr>Informasi Artikel:\u003Cbr>DOI : 10.31284/p.snestik.2025.6901\u003Cbr>Prosiding ISSN 2775-5126 |  |  |\n| Fakultas Teknik Elektro dan Teknologi Informasi-Institut Teknologi Adhi Tama Surabaya Gedung A-ITATS, Jl. Arief Rachman Hakim 100 Surabaya 60117 Telp. (031) 5945043 [Email : snestik@itats.ac.id](Email : snestik@itats.ac.id) |  |  |\n\nPengenalan Sinyal Otak Berbasis Machine Learning untuk Aktivasi Lampu Sen Otomatis pada Kendaraan Bermotor (Kasus Ibu-Ibu di  \nIndonesia)  \nM Budi Hartanto, Iin Marliana, Arie Setya Putra, Fadhilah Dirayati, Rosyana Fitria Purnomo  \nUniversitas Mitra Indonesia Bandar Lampunge-mail: [budi.hartanto@umitra.ac.id](budi.hartanto@umitra.ac.id)  \nABSTRACT  \nThis study proposes an automatic turn signal activation system for motor vehicles based on brain signals using a machine learning approach, with a focus on rider behavior, particularly among Indonesian mothers. The problem addressed is riders’ negligence in signaling when turning. EEG signal data were collected from 100 riders, divided into several groups, and processed to train machine learning models. Three machine learning algorithms used in this study are Support Vector Machine (SVM), K-Nearest Neighbors (KNN), and Random Forest. The experimental results show that the system effectively recognizes brain signal patterns with the highest accuracy of 92%, sensitivity of 89%, and specificity of 91%. The implementation of this system is expected to reduce the risk of accidents caused by riders' negligence in signaling when turning. Keywords: Automation; dynamic system; embedded system; brain signals; driving safety.  \nABSTRAK  \nAbstrak penelitian ini mengusulkan sistem otomatisasi aktivasi sinyal belok pada kendaraan bermotor berbasis sinyal otak menggunakan pendekatan pembelajaran mesin, dengan fokus pada perilaku pengendara, khususnya ibu-ibu di Indonesia. Masalah yang diangkat adalah kelalaian pengendara dalam memberikan sinyal saat berbelok. Data sinyal EEG dikumpulkan dari 100 pengendara, yang terbagi dalam beberapa kelompok, dan diproses untuk melatih model pembelajaran mesin. Tiga algoritma machine learning yang digunakan dalam penelitian ini adalah Support Vector Machine (SVM), K-Nearest Neighbors (KNN), dan Random Forest. Hasil eksperimen menunjukkan bahwa sistem ini berhasil mengenali pola sinyal otak denganakurasi tertinggi sebesar 92%, sensitivitas 89%, dan spesifisitas 91% . Dengan implementasi sistem  \nini, diharapkan dapat mengurangi risiko kecelakaan akibat kelalaian pengendara dalam memberikan sinyal saat berbelok.  \nKata kunci: Otomasi; sistem dinamis; sistem tertanam; sinyal otak; keselamatan berkendara.  \nPENDAHULUAN  \nKeselamatan berlalu lintas adalah isu yang sangat penting dalam dunia transportasi, terutama di negara berkembang seperti Indonesia. Berdasarkan data dari[1], salah satu penyebabutama kecelakaan adalah kelalaian pengendara dalam memberikan sinyal saat akan berbelokatau berpindah jalur. Masalah ini sering ditemui pada pengendara sepeda motor, termasukibu-ibu, yang cenderung kurang memperhatikan penggunaan lampu sen [2] . Kelalaian ini menunjukkan adanya kebutuhan mendesak akan solusi inovatif untuk meningkatkan keselamatan berkendara, terutama pada pengendara sepeda motor yang sering kali tidak memberikan sinyal saat berbelok atau berpindah jalur.  \nBeberapa penelitian sebelumnya telah membahas penerapan teknologi berbasis sinyal otakuntuk berbagai aplikasi. Dalam bidang kontrol perangkat, sinyal otak telah terbukti efektif untuk mengendalikan prostetik dan perangkat eksternal lainnya [3] . Di sektor otomotif, beberapa","cbCaihBga8ZafoPP","https://ap.wps.com/l/cbCaihBga8ZafoPP","pdf",1410890,6,1,12,"Indonesian","id",113,"# Pendahuluan\n## Latar belakang keselamatan berlalu lintas dan kelalaian sinyal\n## Keterbatasan aplikasi sinyal otak pada sistem otomotif\n## Tujuan penelitian dan kontribusi\n# Sistem yang Diusulkan\n## Perangkat EEG untuk deteksi sinyal otak\n## Algoritma machine learning: SVM, KNN, Random Forest","[{\"question\":\"Apa masalah utama yang diangkat dalam penelitian ini?\",\"answer\":\"Penelitian menyoroti kelalaian pengendara dalam memberikan sinyal saat berbelok atau berpindah jalur, yang sering terjadi pada pengendara sepeda motor.\"},{\"question\":\"Bagaimana sistem mengaktifkan lampu sen otomatis?\",\"answer\":\"Sistem menggunakan perangkat EEG untuk mendeteksi sinyal otak pengendara, kemudian memprosesnya dengan algoritma machine learning untuk mengenali pola.\"},{\"question\":\"Algoritma machine learning apa saja yang digunakan dan bagaimana performanya?\",\"answer\":\"Model menggunakan SVM, KNN, dan Random Forest. Hasil menunjukkan akurasi tertinggi 92% dengan sensitivitas 89% dan spesifisitas 91%.\"}]","Pengenalan Sinyal Otak Berbasis Machine Learning untuk Aktivasi Lampu Sen Otomatis pada Kendaraan Bermotor - (Kasus Ibu-Ibu di Indonesia) | PDF",1785730259,18,{"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},"introduction-to-brain-signals-based-on-machine-learning-for-automatic-turn-signal-activation-on-motor-vehicles-case-of-mothers-in-indonesia","",{"@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/introduction-to-brain-signals-based-on-machine-learning-for-automatic-turn-signal-activation-on-motor-vehicles-case-of-mothers-in-indonesia/120471/",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","2026-08-03",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},"Apa masalah utama yang diangkat dalam penelitian ini?","Question",{"text":77,"@type":78},"Penelitian menyoroti kelalaian pengendara dalam memberikan sinyal saat berbelok atau berpindah jalur, yang sering terjadi pada pengendara sepeda motor.","Answer",{"name":80,"@type":75,"acceptedAnswer":81},"Bagaimana sistem mengaktifkan lampu sen otomatis?",{"text":82,"@type":78},"Sistem menggunakan perangkat EEG untuk mendeteksi sinyal otak pengendara, kemudian memprosesnya dengan algoritma machine learning untuk mengenali pola.",{"name":84,"@type":75,"acceptedAnswer":85},"Algoritma machine learning apa saja yang digunakan dan bagaimana performanya?",{"text":86,"@type":78},"Model menggunakan SVM, KNN, dan Random Forest. 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