[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-121521-id":3,"doc-seo-121521-113":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},121521,962085571259,"Theodora","https://ap-avatar.wpscdn.com/davatar_29158cc5080c5b710cf443261637dec0",54,"Penelitian & Laporan","Klasifikasi Sinyal EEG Subband Beta untuk Identifikasi Persepsi Rasa Manis dan Asam menggunakan Algoritma - Machine Learning","Aktivitas gelombang otak (EEG) digunakan untuk mengenali respons manusia terhadap stimulus sensorik, termasuk persepsi rasa. Penelitian ini mengklasifikasikan respons otak terhadap dua jenis rasa, yaitu manis (susu) dan asam (lemon), memakai sinyal EEG pada subband Beta (12–25 Hz) dengan pendekatan machine learning. Data direkam dari empat kanal (T3, T4, CP1, CP2), diekstraksi dengan fitur MAV dan VAR, serta dibandingkan menggunakan SVM, KNN, dan Decision Tree untuk memperoleh performa terbaik dan kontribusi pada sistem EEG-based Taste Recognition.","Techno.COM, Vol. 24, No. 4, November 2025: 1383-1394  \nKlasifikasi Sinyal EEG Subband Beta untuk Identifikasi Persepsi Rasa Manis dan Asam Menggunakan Algoritma  \nMachine Learning  \nClassification of EEG Beta Subband Signals for Identifying Sweet and Sour Taste Perception Using Machine Learning Algorithms  \nMarianus Yakobus Lili Lejap*1, Silvester Tena2, Bima Gerry Pratama3  \n1,2Teknik Elektro, Universitas Nusa Cendana, 3Teknik Elektro, Politeknik Negeri Bandung [E-mail : marianus.lejap@staf.undana.ac.id](E-mail : marianus.lejap@staf.undana.ac.id) *1, [siltena@staf.undana.ac.id](siltena@staf.undana.ac.id2)[2](siltena@staf.undana.ac.id2)[ ](siltena@staf.undana.ac.id2)[bima.pratama@polban.ac.id](bima.pratama@polban.ac.id3)[3](bima.pratama@polban.ac.id3)  \nReceived 31 October 2025; Revised 22 November 2025; Accepted 25 November 2025  \nAbstrak—Aktivitas gelombang otak (EEG) dapat digunakan untuk mengenali respons manusia terhadap stimulus sensorik, termasuk persepsi rasa. Penelitian ini bertujuan untuk mengklasifikasikan aktivitas otak terhadap dua jenis stimulus rasa, yaitu rasa manis (Susu) dan rasa asam (Lemon), menggunakan sinyal EEG pada subband Beta (12–25 Hz) dengan pendekatan machine learning. Penelitian ini merupakan pengembangan dari studi sebelumnya yang hanya menampilkan visualisasi topografi otak (brain heatmap), dengan menambahkan analisis klasifikasi otomatis berbasis kecerdasan buatan. Data EEG direkam dari empat kanal utama, yaitu T3, T4, CP1, dan CP2, kemudian diekstraksi menggunakan dua fitur utama: Mean Absolute Value (MAV) dan Variance (VAR) . Total data yang digunakan sebanyak 10.644 potong data (3.550 Susu dan 7.094 Lemon) . Tiga algoritma machine learning digunakan untuk membandingkan performa klasifikasi, yaitu Support Vector Machine (SVM), K-Nearest Neighbor (KNN), dan Decision Tree (DT). Hasil pengujian menunjukkan bahwa Decision Tree menghasilkan performaterbaik dengan akurasi 84,0%, F1-score 0,727, dan ROC AUC 0,789, diikuti oleh KNN denganakurasi 76,6% . Model SVM linear menunjukkan performa terendah akibat ketidakseimbangan data dan distribusi non-linear. Hasil ini membuktikan bahwa fitur EEG pada subband Beta dapat digunakan untuk membedakan stimulus rasa manis dan asam secara objektif. Penelitian ini memberikan kontribusi terhadap pengembangan sistem EEG-based Taste Recognition dan membuka peluang penerapan dalam bidang neurogastronomi serta Brain–Computer Interface (BCI) .  \nKata kunci: EEG, subband Beta, klasifikasi rasa, machine learning, Decision Tree.  \nAbstract -Brain wave activity (EEG) can be used to recognize human responses to sensory stimuli, including taste perception. This study aims to classify brain activity corresponding to two taste stimuli, sweet (milk) and sour (lemon), using EEG signals in the Beta subband (12–25 Hz) through a machine learning approach. This work extends a previous study that visualized EEG topography (brain heatmap) by introducing automated classification based on artificial intelligence. EEG data were recorded from four main channels (T3, T4, CP1, and CP2) and extracted using two primary features: Mean Absolute Value (MAV) and Variance (VAR). The dataset contained 10,644 chunks (3,550 sweet and 7,094 sour). Three machine learning algorithms were compared, namely Support Vector Machine (SVM), K-Nearest Neighbor (KNN), and Decision Tree (DT). Experimental results indicate that the Decision Tree achieved the best performance with an accuracy of 84.0%, F1-score 0.727, andROCAUC 0.789, followed by KNN with an accuracy of 76.6%. The linear SVM model performed poorly due to class imbalance and non-linear data distribution. These findings demonstrate that EEG Beta subband features can objectively distinguish between sweet and sour stimuli. This study contributes to the development  \n1383  \ne-ISSN : 2356-2579 | p-ISSN : 1412-2693, DOI : 10.62411/tc.v24i4.15011  \nTechno.COM, Vol. 24, No. 4, November 2025: 1383-1394  \nof EEG-based Taste Recognition systems and provides p","cbCaivaDenzMvE6A","https://ap.wps.com/l/cbCaivaDenzMvE6A","pdf",597124,3,1,12,"Indonesian","id",113,"# Pendahuluan\n## Peran sistem pengecap dan pengambilan keputusan cepat\n## Deteksi rasa dan aroma serta pengaruh terhadap preferensi dan memori\n## Studi terdahulu dan dasar aktivitas regio gustatori","[{\"question\":\"Apa tujuan utama penelitian ini?\",\"answer\":\"Mengklasifikasikan aktivitas otak terhadap stimulus rasa manis dan asam menggunakan sinyal EEG subband Beta dengan pendekatan machine learning.\"},{\"question\":\"Data EEG diambil dari kanal apa dan fitur apa yang diekstraksi?\",\"answer\":\"EEG direkam dari empat kanal utama T3, T4, CP1, dan CP2, lalu diekstraksi menggunakan dua fitur utama yaitu Mean Absolute Value (MAV) dan Variance (VAR).\"},{\"question\":\"Algoritma mana yang menghasilkan performa terbaik, dan berapa metrik utamanya?\",\"answer\":\"Decision Tree memberikan performa terbaik dengan akurasi 84,0%, F1-score 0,727, dan ROC AUC 0,789, diikuti KNN dengan akurasi 76,6%.\"}]","Klasifikasi Sinyal EEG Subband Beta untuk Identifikasi Persepsi Rasa Manis dan Asam menggunakan Algoritma - Machine Learning | PDF",1785736073,18,{"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":87,"head_meta":89,"extra_data":91,"updated_unix":29},"classification-of-eeg-beta-subband-signals-for-identifying-sweet-and-sour-taste-perception-using-algorithms-machine-learning","",{"@graph":37,"@context":86},[38,54,69],{"@type":39,"itemListElement":40},"BreadcrumbList",[41,45,49,51],{"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":20},"https://docshare.wps.com/id/document/penelitian-laporan/",{"item":52,"name":13,"@type":44,"position":53},"https://docshare.wps.com/id/document/classification-of-eeg-beta-subband-signals-for-identifying-sweet-and-sour-taste-perception-using-algorithms-machine-learning/121521/",4,{"url":52,"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","2026-08-15","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},"Apa tujuan utama penelitian ini?","Question",{"text":76,"@type":77},"Mengklasifikasikan aktivitas otak terhadap stimulus rasa manis dan asam menggunakan sinyal EEG subband Beta dengan pendekatan machine learning.","Answer",{"name":79,"@type":74,"acceptedAnswer":80},"Data EEG diambil dari kanal apa dan fitur apa yang diekstraksi?",{"text":81,"@type":77},"EEG direkam dari empat kanal utama T3, T4, CP1, dan CP2, lalu diekstraksi menggunakan dua fitur utama yaitu Mean Absolute Value (MAV) dan Variance (VAR).",{"name":83,"@type":74,"acceptedAnswer":84},"Algoritma mana yang menghasilkan performa terbaik, dan berapa metrik utamanya?",{"text":85,"@type":77},"Decision Tree memberikan performa terbaik dengan akurasi 84,0%, F1-score 0,727, dan ROC AUC 0,789, diikuti KNN dengan akurasi 76,6%.","https://schema.org",{"og:url":52,"og:type":88,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":90,"canonical":52},"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"]