{"id":"https://openalex.org/W7225464754","doi":"https://doi.org/10.35870/jtik.v11i1.8767","title":"Analisis Perbandingan Kinerja Arsitektur CNN Dan Transformer dalam Klasifikasi Gambar AI-Generated dan Gambar Asli","display_name":"Analisis Perbandingan Kinerja Arsitektur CNN Dan Transformer dalam Klasifikasi Gambar AI-Generated dan Gambar Asli","publication_year":2026,"publication_date":"2026-11-08","ids":{"openalex":"https://openalex.org/W7225464754","doi":"https://doi.org/10.35870/jtik.v11i1.8767"},"language":"en","primary_location":{"id":"doi:10.35870/jtik.v11i1.8767","doi":"https://doi.org/10.35870/jtik.v11i1.8767","is_oa":true,"landing_page_url":"https://doi.org/10.35870/jtik.v11i1.8767","pdf_url":"https://journal.lembagakita.com/jtik/article/download/8767/5441","source":{"id":"https://openalex.org/S4210198648","display_name":"Jurnal JTIK (Jurnal Teknologi Informasi dan Komunikasi)","issn_l":"2580-1643","issn":["2580-1643"],"is_oa":true,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Jurnal JTIK (Jurnal Teknologi Informasi dan Komunikasi)","raw_type":"journal-article"},"volume":null,"type":"article","is_traditional":true,"indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"diamond","oa_url":"https://journal.lembagakita.com/jtik/article/download/8767/5441","any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5161245890","display_name":"David Yus Humisar Silalahi","orcid":null,"observed_orcids":[]},"institutions":[{"id":"https://openalex.org/I4210143643","display_name":"Universitas Krisnadwipayana","ror":"https://ror.org/03y211p39","country_code":"ID","type":"education","lineage":["https://openalex.org/I4210143643"]}],"countries":["ID"],"is_corresponding":true,"raw_author_name":"David Yus Humisar Silalahi","raw_affiliation_strings":["Universitas Krisnadwipayana"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Universitas Krisnadwipayana","institution_ids":["https://openalex.org/I4210143643"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100410848","display_name":"Junaidi Junaidi","orcid":"https://orcid.org/0000-0001-5039-5395","observed_orcids":["https://orcid.org/0000-0001-5039-5395","https://orcid.org/0009-0005-5500-0475"]},"institutions":[{"id":"https://openalex.org/I4210143643","display_name":"Universitas Krisnadwipayana","ror":"https://ror.org/03y211p39","country_code":"ID","type":"education","lineage":["https://openalex.org/I4210143643"]}],"countries":["ID"],"is_corresponding":false,"raw_author_name":"Junaidi Junaidi","raw_affiliation_strings":["Universitas Krisnadwipayana"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Universitas Krisnadwipayana","institution_ids":["https://openalex.org/I4210143643"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5099081811","display_name":"Eka Ari Endrawati","orcid":null,"observed_orcids":[]},"institutions":[{"id":"https://openalex.org/I4210143643","display_name":"Universitas Krisnadwipayana","ror":"https://ror.org/03y211p39","country_code":"ID","type":"education","lineage":["https://openalex.org/I4210143643"]}],"countries":["ID"],"is_corresponding":false,"raw_author_name":"Eka Ari Endrawati","raw_affiliation_strings":["Universitas Krisnadwipayana"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Universitas Krisnadwipayana","institution_ids":["https://openalex.org/I4210143643"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":["https://openalex.org/A5161245890"],"corresponding_institution_ids":["https://openalex.org/I4210143643"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":true,"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":"11","issue":"1","first_page":"292","last_page":"304"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T12357","display_name":"Digital Media Forensic Detection","score":0.9990000128746033,"subfield":{"id":"https://openalex.org/subfields/1707","display_name":"Computer Vision and Pattern Recognition"},"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/T12357","display_name":"Digital Media Forensic Detection","score":0.9990000128746033,"subfield":{"id":"https://openalex.org/subfields/1707","display_name":"Computer Vision and Pattern Recognition"},"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/T10036","display_name":"Advanced Neural Network Applications","score":0.0007999999797903001,"subfield":{"id":"https://openalex.org/subfields/1707","display_name":"Computer Vision and Pattern Recognition"},"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/T10775","display_name":"Generative Adversarial Networks and Image Synthesis","score":9.999999747378752e-05,"subfield":{"id":"https://openalex.org/subfields/1707","display_name":"Computer Vision and Pattern Recognition"},"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/ai-generated-image-detection","display_name":"AI-generated image detection","score":0.9010000228881836},{"id":"https://openalex.org/keywords/deepfakes","display_name":"deepfakes","score":0.6209999918937683},{"id":"https://openalex.org/keywords/image-classification","display_name":"image classification","score":0.49900001287460327},{"id":"https://openalex.org/keywords/convolutional-neural-networks","display_name":"convolutional neural networks","score":0.9309999942779541},{"id":"https://openalex.org/keywords/vision-transformer","display_name":"vision transformer","score":0.9309999942779541},{"id":"https://openalex.org/keywords/resnet-50","display_name":"ResNet50","score":0.765999972820282},{"id":"https://openalex.org/keywords/mobilenetv2","display_name":"MobileNetV2","score":1.0},{"id":"https://openalex.org/keywords/deit","display_name":"DeiT","score":0.984000027179718},{"id":"https://openalex.org/keywords/binary-classification","display_name":"binary classification","score":0.3540000021457672}],"concepts":[{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.710099995136261},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6776000261306763},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.5570999979972839},{"id":"https://openalex.org/C66322947","wikidata":"https://www.wikidata.org/wiki/Q11658","display_name":"Transformer","level":3,"score":0.5278000235557556},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.4894999861717224},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.38580000400543213},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.33570000529289246},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.3319999873638153},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.3163999915122986},{"id":"https://openalex.org/C81669768","wikidata":"https://www.wikidata.org/wiki/Q2359161","display_name":"Precision and recall","level":2,"score":0.31360000371932983},{"id":"https://openalex.org/C2781140086","wikidata":"https://www.wikidata.org/wiki/Q557945","display_name":"Confusion","level":2,"score":0.3082999885082245},{"id":"https://openalex.org/C51632099","wikidata":"https://www.wikidata.org/wiki/Q3985153","display_name":"Training set","level":2,"score":0.3073999881744385},{"id":"https://openalex.org/C75294576","wikidata":"https://www.wikidata.org/wiki/Q5165192","display_name":"Contextual image classification","level":3,"score":0.3003000020980835},{"id":"https://openalex.org/C177212765","wikidata":"https://www.wikidata.org/wiki/Q627335","display_name":"Workflow","level":2,"score":0.2623000144958496},{"id":"https://openalex.org/C136886441","wikidata":"https://www.wikidata.org/wiki/Q926129","display_name":"Normalization (sociology)","level":2,"score":0.2578999996185303},{"id":"https://openalex.org/C2776436953","wikidata":"https://www.wikidata.org/wiki/Q5163215","display_name":"Consistency (knowledge bases)","level":2,"score":0.2574000060558319}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.35870/jtik.v11i1.8767","doi":"https://doi.org/10.35870/jtik.v11i1.8767","is_oa":true,"landing_page_url":"https://doi.org/10.35870/jtik.v11i1.8767","pdf_url":"https://journal.lembagakita.com/jtik/article/download/8767/5441","source":{"id":"https://openalex.org/S4210198648","display_name":"Jurnal JTIK (Jurnal Teknologi Informasi dan Komunikasi)","issn_l":"2580-1643","issn":["2580-1643"],"is_oa":true,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Jurnal JTIK (Jurnal Teknologi Informasi dan Komunikasi)","raw_type":"journal-article"}],"best_oa_location":{"id":"doi:10.35870/jtik.v11i1.8767","doi":"https://doi.org/10.35870/jtik.v11i1.8767","is_oa":true,"landing_page_url":"https://doi.org/10.35870/jtik.v11i1.8767","pdf_url":"https://journal.lembagakita.com/jtik/article/download/8767/5441","source":{"id":"https://openalex.org/S4210198648","display_name":"Jurnal JTIK (Jurnal Teknologi Informasi dan Komunikasi)","issn_l":"2580-1643","issn":["2580-1643"],"is_oa":true,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Jurnal JTIK (Jurnal Teknologi Informasi dan Komunikasi)","raw_type":"journal-article"},"sustainable_development_goals":[],"study_designs":[],"awards":[],"funders":[],"has_content":{"grobid_xml":true,"pdf":true},"content_urls":{"pdf":"https://content.openalex.org/works/W7225464754.pdf","grobid_xml":"https://content.openalex.org/works/W7225464754.grobid-xml"},"referenced_works_count":21,"referenced_works":["https://openalex.org/W3094502228","https://openalex.org/W4211008685","https://openalex.org/W4213449918","https://openalex.org/W4324135515","https://openalex.org/W4375955734","https://openalex.org/W4386246256","https://openalex.org/W4389370799","https://openalex.org/W4391110866","https://openalex.org/W4391404116","https://openalex.org/W4392793205","https://openalex.org/W4395661501","https://openalex.org/W4404937575","https://openalex.org/W4407144055","https://openalex.org/W4407450187","https://openalex.org/W4412422061","https://openalex.org/W4412754844","https://openalex.org/W4414727103","https://openalex.org/W4415355444","https://openalex.org/W7125630923","https://openalex.org/W7151412836","https://openalex.org/W7160862157"],"related_works":["https://openalex.org/W7147141262","https://openalex.org/W4414730325","https://openalex.org/W7216182481","https://openalex.org/W4412422061","https://openalex.org/W4410245415","https://openalex.org/W7164545959","https://openalex.org/W7146975843","https://openalex.org/W4414551257","https://openalex.org/W7107950663","https://openalex.org/W4409747022"],"abstract_inverted_index":{"The":[0,90,123,155,206],"advancement":[1],"of":[2,10,32,45,62,167,170,173,177,194,202,218],"generative":[3],"artificial":[4],"intelligence":[5],"has":[6],"enabled":[7],"the":[8,43,59,95,127,161,187,203,226],"creation":[9],"increasingly":[11],"realistic":[12],"facial":[13],"images":[14,64,106,113],"that":[15,158,201],"are":[16],"difficult":[17],"to":[18],"distinguish":[19],"from":[20,94],"authentic":[21],"images,":[22,221],"thereby":[23],"creating":[24],"a":[25,109,213],"need":[26],"for":[27,58,114,117,121],"automated":[28],"classification":[29,61,153,228],"methods":[30],"capable":[31,217],"maintaining":[33],"both":[34],"accuracy":[35,166,193],"and":[36,56,65,73,78,82,98,119,132,139,152,175,183,224],"consistency":[37],"in":[38,108,212],"detection.":[39],"This":[40],"study":[41],"compares":[42],"performance":[44,142],"two":[46],"deep":[47],"learning":[48],"architecture":[49],"groups,":[50],"namely":[51],"Convolutional":[52],"Neural":[53],"Networks":[54],"(CNNs)":[55],"Transformers,":[57],"binary":[60],"AI-generated":[63],"real":[66],"images.":[67],"Four":[68],"models":[69,189],"were":[70],"evaluated:":[71],"ResNet50":[72],"MobileNetV2":[74],"as":[75,87],"CNN":[76],"representatives,":[77],"Vision":[79],"Transformer":[80,85,88],"(ViT)":[81],"Data-efficient":[83],"Image":[84],"(DeiT)":[86],"representatives.":[89],"dataset":[91,101],"was":[92,143,197,209],"obtained":[93],"140k":[96],"Real":[97],"Fake":[99],"Faces":[100],"on":[102],"Kaggle,":[103],"with":[104,164,231],"28,000":[105],"used":[107],"balanced":[110],"distribution:":[111],"20,000":[112],"training,":[115],"4,000":[116,120],"validation,":[118],"testing.":[122],"experimental":[124],"workflow":[125],"followed":[126,179],"CRISP-DM":[128],"framework,":[129],"comprising":[130],"business":[131],"data":[133,135],"understanding,":[134],"preparation,":[136],"modeling,":[137],"evaluation,":[138],"deployment.":[140],"Model":[141],"assessed":[144],"using":[145],"accuracy,":[146],"precision,":[147],"recall,":[148],"F1-score,":[149],"confusion":[150],"matrix,":[151],"report.":[154],"results":[156],"show":[157],"DeiT":[159,207],"achieved":[160,190],"best":[162],"performance,":[163],"an":[165,191],"86.98%,":[168],"precision":[169],"86.81%,":[171],"recall":[172],"87.20%,":[174],"F1-score":[176],"87.00%,":[178],"by":[180],"MobileNetV2,":[181],"ViT,":[182],"ResNet50.":[184],"In":[185],"aggregate,":[186],"Transformer-based":[188],"average":[192],"79.5%,":[195],"which":[196],"7%":[198],"higher":[199],"than":[200],"CNN-based":[204],"models.":[205],"model":[208],"subsequently":[210],"implemented":[211],"Gradio-based":[214],"prototype":[215],"application":[216],"receiving":[219],"user-uploaded":[220],"performing":[222],"inference,":[223],"displaying":[225],"predicted":[227],"label":[229],"along":[230],"its":[232],"confidence":[233],"score.":[234]},"counts_by_year":[],"updated_date":"2026-11-09T13:22:03.289127","created_date":"2026-11-08T00:00:00"}
