{"id":"https://openalex.org/W7204839710","doi":"https://doi.org/10.1109/icera72709.2026.11666760","title":"Analyzing Public Perception Toward Artificial Intelligence Educational Content on YouTube Using TF-IDF and Support Vector Machine","display_name":"Analyzing Public Perception Toward Artificial Intelligence Educational Content on YouTube Using TF-IDF and Support Vector Machine","publication_year":2026,"publication_date":"2026-07-01","ids":{"openalex":"https://openalex.org/W7204839710","doi":"https://doi.org/10.1109/icera72709.2026.11666760"},"language":"en","primary_location":{"id":"doi:10.1109/icera72709.2026.11666760","doi":"https://doi.org/10.1109/icera72709.2026.11666760","is_oa":true,"landing_page_url":"https://doi.org/10.1109/icera72709.2026.11666760","pdf_url":null,"source":{"id":"https://openalex.org/S7407087252","display_name":"International Conference on Electronics Representation and Algorithm (ICERA)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"conference"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2026 5th International Conference on Electronics Representation and Algorithm (ICERA)","raw_type":"proceedings-article"},"volume":null,"type":"conference-paper","is_traditional":true,"indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"hybrid","oa_url":"https://doi.org/10.1109/icera72709.2026.11666760","any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5117361186","display_name":"Mikhael Victor Siagian","orcid":null,"observed_orcids":[]},"institutions":[{"id":"https://openalex.org/I166073570","display_name":"Binus University","ror":"https://ror.org/03zmf4s77","country_code":"ID","type":"education","lineage":["https://openalex.org/I166073570"]}],"countries":["ID"],"is_corresponding":false,"raw_author_name":"Mikhael Victor Siagian","raw_affiliation_strings":["Bina Nusantara University"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Bina Nusantara University","institution_ids":["https://openalex.org/I166073570"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5148819603","display_name":"Kimberley Gretchell Maydelline Sitanggang","orcid":null,"observed_orcids":[]},"institutions":[{"id":"https://openalex.org/I166073570","display_name":"Binus University","ror":"https://ror.org/03zmf4s77","country_code":"ID","type":"education","lineage":["https://openalex.org/I166073570"]}],"countries":["ID"],"is_corresponding":false,"raw_author_name":"Kimberley Gretchell Maydelline Sitanggang","raw_affiliation_strings":["Bina Nusantara University"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Bina Nusantara University","institution_ids":["https://openalex.org/I166073570"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5010939676","display_name":"Eko Setyo Purwanto","orcid":"https://orcid.org/0000-0001-7456-3365","observed_orcids":["https://orcid.org/0000-0001-7456-3365"]},"institutions":[{"id":"https://openalex.org/I166073570","display_name":"Binus University","ror":"https://ror.org/03zmf4s77","country_code":"ID","type":"education","lineage":["https://openalex.org/I166073570"]}],"countries":["ID"],"is_corresponding":false,"raw_author_name":"Eko Setyo Purwanto","raw_affiliation_strings":["Bina Nusantara University"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Bina Nusantara University","institution_ids":["https://openalex.org/I166073570"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5020940943","display_name":"Ghinaa Zain Nabiilah","orcid":"https://orcid.org/0000-0001-7638-7449","observed_orcids":["https://orcid.org/0000-0001-7638-7449"]},"institutions":[{"id":"https://openalex.org/I166073570","display_name":"Binus University","ror":"https://ror.org/03zmf4s77","country_code":"ID","type":"education","lineage":["https://openalex.org/I166073570"]}],"countries":["ID"],"is_corresponding":false,"raw_author_name":"Ghinaa Zain Nabiilah","raw_affiliation_strings":["Bina Nusantara University"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Bina Nusantara University","institution_ids":["https://openalex.org/I166073570"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I166073570"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.0,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"364","last_page":"369"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10664","display_name":"Sentiment Analysis and Opinion Mining","score":1.0,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},"topics":[{"id":"https://openalex.org/T10664","display_name":"Sentiment Analysis and Opinion Mining","score":1.0,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T12262","display_name":"Hate Speech and Cyberbullying Detection","score":0.0,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T11122","display_name":"Online Learning and Analytics","score":0.0,"subfield":{"id":"https://openalex.org/subfields/1706","display_name":"Computer Science Applications"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/public-perception-of-ai","display_name":"public perception of AI","score":0.8259999752044678},{"id":"https://openalex.org/keywords/artificial-intelligence-in-education","display_name":"artificial intelligence in education","score":0.9750000238418579},{"id":"https://openalex.org/keywords/youtube-comments","display_name":"YouTube comments","score":0.9729999899864197},{"id":"https://openalex.org/keywords/sentiment-analysis","display_name":"sentiment analysis","score":0.9819999933242798},{"id":"https://openalex.org/keywords/sentiment-classification","display_name":"sentiment classification","score":0.9150000214576721},{"id":"https://openalex.org/keywords/tf-idf","display_name":"TF-IDF","score":0.7649999856948853},{"id":"https://openalex.org/keywords/support-vector-machine","display_name":"support vector machine","score":0.996999979019165},{"id":"https://openalex.org/keywords/online-educational-content","display_name":"online educational content","score":0.5789999961853027},{"id":"https://openalex.org/keywords/audience-reception","display_name":"audience reception","score":0.5580000281333923}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5842000246047974},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5256999731063843},{"id":"https://openalex.org/C12267149","wikidata":"https://www.wikidata.org/wiki/Q282453","display_name":"Support vector machine","level":2,"score":0.5164999961853027},{"id":"https://openalex.org/C26760741","wikidata":"https://www.wikidata.org/wiki/Q160402","display_name":"Perception","level":2,"score":0.4260999858379364},{"id":"https://openalex.org/C49774154","wikidata":"https://www.wikidata.org/wiki/Q131765","display_name":"Multimedia","level":1,"score":0.40630000829696655},{"id":"https://openalex.org/C2778152352","wikidata":"https://www.wikidata.org/wiki/Q5165061","display_name":"Content (measure theory)","level":2,"score":0.3917999863624573},{"id":"https://openalex.org/C136764020","wikidata":"https://www.wikidata.org/wiki/Q466","display_name":"World Wide Web","level":1,"score":0.3587000072002411},{"id":"https://openalex.org/C162446236","wikidata":"https://www.wikidata.org/wiki/Q653137","display_name":"Content analysis","level":2,"score":0.3384999930858612},{"id":"https://openalex.org/C518677369","wikidata":"https://www.wikidata.org/wiki/Q202833","display_name":"Social media","level":2,"score":0.32850000262260437},{"id":"https://openalex.org/C204321447","wikidata":"https://www.wikidata.org/wiki/Q30642","display_name":"Natural language processing","level":1,"score":0.2892000079154968},{"id":"https://openalex.org/C16443162","wikidata":"https://www.wikidata.org/wiki/Q1068473","display_name":"Educational technology","level":2,"score":0.2851000130176544},{"id":"https://openalex.org/C23123220","wikidata":"https://www.wikidata.org/wiki/Q816826","display_name":"Information retrieval","level":1,"score":0.27219998836517334},{"id":"https://openalex.org/C26517878","wikidata":"https://www.wikidata.org/wiki/Q228039","display_name":"Key (lock)","level":2,"score":0.25529998540878296},{"id":"https://openalex.org/C2986087404","wikidata":"https://www.wikidata.org/wiki/Q15946010","display_name":"Online learning","level":2,"score":0.2526000142097473}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/icera72709.2026.11666760","doi":"https://doi.org/10.1109/icera72709.2026.11666760","is_oa":true,"landing_page_url":"https://doi.org/10.1109/icera72709.2026.11666760","pdf_url":null,"source":{"id":"https://openalex.org/S7407087252","display_name":"International Conference on Electronics Representation and Algorithm (ICERA)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"conference"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2026 5th International Conference on Electronics Representation and Algorithm (ICERA)","raw_type":"proceedings-article"}],"best_oa_location":{"id":"doi:10.1109/icera72709.2026.11666760","doi":"https://doi.org/10.1109/icera72709.2026.11666760","is_oa":true,"landing_page_url":"https://doi.org/10.1109/icera72709.2026.11666760","pdf_url":null,"source":{"id":"https://openalex.org/S7407087252","display_name":"International Conference on Electronics Representation and Algorithm (ICERA)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"conference"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2026 5th International Conference on Electronics Representation and Algorithm (ICERA)","raw_type":"proceedings-article"},"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/4","display_name":"Quality education","score":0.8278999924659729}],"study_designs":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":26,"referenced_works":["https://openalex.org/W4224468222","https://openalex.org/W4225983683","https://openalex.org/W4287883622","https://openalex.org/W4294343964","https://openalex.org/W4308897526","https://openalex.org/W4312001989","https://openalex.org/W4317743691","https://openalex.org/W4367856259","https://openalex.org/W4383265687","https://openalex.org/W4387063598","https://openalex.org/W4389372670","https://openalex.org/W4389856535","https://openalex.org/W4390650744","https://openalex.org/W4390826088","https://openalex.org/W4391021086","https://openalex.org/W4391160781","https://openalex.org/W4394858072","https://openalex.org/W4402897552","https://openalex.org/W4403212544","https://openalex.org/W4405601556","https://openalex.org/W4406264253","https://openalex.org/W4409573732","https://openalex.org/W4412610062","https://openalex.org/W4412879984","https://openalex.org/W4414822452","https://openalex.org/W4415934141"],"related_works":["https://openalex.org/W7204763278","https://openalex.org/W7128236115","https://openalex.org/W7161701116","https://openalex.org/W7170135991","https://openalex.org/W7167729817","https://openalex.org/W4412370024","https://openalex.org/W7168255466","https://openalex.org/W4394717309","https://openalex.org/W7142610607","https://openalex.org/W4414544577"],"abstract_inverted_index":{"YouTube":[0,61,171],"has":[1],"become":[2],"a":[3],"prominent":[4],"platform":[5],"for":[6,69,77,142,167,177],"the":[7,14,40,113,159,162,183],"dissemination":[8],"of":[9,16,21,43,54,60,119,123,126,130,144,161],"educational":[10,83,150],"content,":[11],"particularly":[12],"in":[13,182],"field":[15],"Artificial":[17],"Intelligence":[18],"(AI).":[19],"Analysis":[20,103],"user":[22],"comments":[23,172],"on":[24,170],"these":[25],"videos":[26],"offers":[27],"valuable":[28],"insights":[29,176],"into":[30],"public":[31,52],"perceptions":[32,53],"and":[33,72,95,128,164,173,180],"attitudes":[34],"toward":[35],"AI":[36,55,82,149,184],"education.":[37],"Due":[38],"to":[39,106],"large":[41],"volume":[42],"comments,":[44,62],"manual":[45],"analysis":[46,59,134],"is":[47,152],"impractical.":[48],"This":[49],"study":[50],"examines":[51],"education":[56,185],"through":[57,87],"sentiment":[58,139,168],"using":[63],"Term":[64],"Frequency\u2013Inverse":[65],"Document":[66],"Frequency":[67],"(TF-IDF)":[68],"feature":[70],"extraction":[71],"Support":[73],"Vector":[74],"Machine":[75],"(SVM)":[76],"classification.":[78],"Comments":[79],"collected":[80],"from":[81],"channels":[84],"were":[85],"preprocessed":[86],"data":[88],"cleaning,":[89],"case":[90],"folding,":[91],"stopword":[92],"removal,":[93],"stemming,":[94],"class":[96],"balancing,":[97],"then":[98],"explored":[99],"via":[100],"Exploratory":[101],"Data":[102],"(EDA)":[104],"prior":[105],"model":[107],"development.":[108],"Experimental":[109],"results":[110],"show":[111],"that":[112,137,148],"TF-IDF\u2013SVM":[114],"pipeline":[115],"achieved":[116],"an":[117],"accuracy":[118],"79.43%,":[120,127],"with":[121],"precision":[122],"79.76%,":[124],"recall":[125],"F1-score":[129],"79.37%.":[131],"Sentiment":[132],"distribution":[133],"further":[135],"revealed":[136],"positive":[138],"predominated,":[140],"accounting":[141],"45.2%":[143],"audience":[145],"responses,":[146],"indicating":[147],"content":[151,178],"predominantly":[153],"well":[154],"received.":[155],"These":[156],"findings":[157],"demonstrate":[158],"effectiveness":[160],"TF-IDF":[163],"SVM":[165],"combination":[166],"classification":[169],"provide":[174],"useful":[175],"creators":[179],"researchers":[181],"domain.":[186]},"counts_by_year":[],"updated_date":"2026-11-08T12:12:28.789561","created_date":"2026-09-01T00:00:00"}