{"id":"https://openalex.org/W1587130653","doi":null,"title":"Pencocokan Citra Berbasis Scale Invariant Feature Transform (SIFT) Menggunakan Arc Cosinus","display_name":"Pencocokan Citra Berbasis Scale Invariant Feature Transform (SIFT) Menggunakan Arc Cosinus","publication_year":2013,"publication_date":"2013-01-01","ids":{"openalex":"https://openalex.org/W1587130653","mag":"1587130653"},"language":"id","primary_location":{"id":"mag:1587130653","doi":null,"is_oa":false,"landing_page_url":"http://eprints.dinus.ac.id/12478/","pdf_url":null,"source":null,"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":null},"volume":null,"type":"article","is_traditional":true,"indexed_in":[],"open_access":{"is_oa":false,"oa_status":"closed","oa_url":null,"any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5039333580","display_name":"Setiyawan Agus","orcid":null,"observed_orcids":[]},"institutions":[],"countries":[],"is_corresponding":true,"raw_author_name":"Setiyawan Agus","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":0,"corresponding_author_ids":["https://openalex.org/A5039333580"],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":8,"citation_normalized_percentile":{"value":0.0,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":87,"max":97},"biblio":{"volume":null,"issue":null,"first_page":null,"last_page":null},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10627","display_name":"Advanced Image and Video Retrieval Techniques","score":0.9984999895095825,"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/T10627","display_name":"Advanced Image and Video Retrieval Techniques","score":0.9984999895095825,"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/T10824","display_name":"Image Retrieval and Classification Techniques","score":0.0010999999940395355,"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/T12983","display_name":"Satellite Image Processing and Photogrammetry","score":0.0,"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/image-matching","display_name":"image matching","score":0.9940000176429749},{"id":"https://openalex.org/keywords/sift","display_name":"SIFT","score":0.6700000166893005},{"id":"https://openalex.org/keywords/scale-invariant-feature-transform","display_name":"scale-invariant feature transform","score":0.8740000128746033},{"id":"https://openalex.org/keywords/keypoint-matching","display_name":"feature point matching","score":0.7459999918937683},{"id":"https://openalex.org/keywords/image-compression","display_name":"image compression","score":0.5960000157356262},{"id":"https://openalex.org/keywords/rotation-invariance","display_name":"rotation invariance","score":0.8510000109672546},{"id":"https://openalex.org/keywords/scale-invariance","display_name":"scale invariance","score":0.9710000157356262},{"id":"https://openalex.org/keywords/viewpoint-change","display_name":"perspective shift","score":0.5490000247955322},{"id":"https://openalex.org/keywords/feature-extraction","display_name":"feature extraction","score":0.7950000166893005}],"concepts":[{"id":"https://openalex.org/C2986492983","wikidata":"https://www.wikidata.org/wiki/Q861092","display_name":"Image matching","level":3,"score":0.4546745717525482},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.44555217027664185},{"id":"https://openalex.org/C61265191","wikidata":"https://www.wikidata.org/wiki/Q767770","display_name":"Scale-invariant feature transform","level":3,"score":0.42209097743034363},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.37773993611335754},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.3420564830303192},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.30684399604797363},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.12143748998641968},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.10204681754112244}],"mesh":[],"locations_count":1,"locations":[{"id":"mag:1587130653","doi":null,"is_oa":false,"landing_page_url":"http://eprints.dinus.ac.id/12478/","pdf_url":null,"source":null,"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":null}],"best_oa_location":null,"sustainable_development_goals":[],"study_designs":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":6,"referenced_works":["https://openalex.org/W1850946566","https://openalex.org/W2109975807","https://openalex.org/W2124386111","https://openalex.org/W2146020873","https://openalex.org/W2151103935","https://openalex.org/W2165497495"],"related_works":["https://openalex.org/W4311390234","https://openalex.org/W3013113566","https://openalex.org/W2289828431","https://openalex.org/W3026964745","https://openalex.org/W2592232985","https://openalex.org/W2607819435","https://openalex.org/W2364930314","https://openalex.org/W2907059419","https://openalex.org/W2393101288","https://openalex.org/W1185157627"],"abstract_inverted_index":{"Dalam":[0],"aktivitas":[1],"pencocokan":[2,86,191,199],"citra":[3,10,19,32,87,103,136,140,150,192,200],"(image":[4],"matching)":[5],"terdapat":[6,69],"resiko":[7],"dimana":[8],"suatu":[9],"tidak":[11],"memiliki":[12,60,196],"fitur":[13,28,43],"khusus":[14,29,64],"yangdigunakan":[15],"sebagai":[16],"pembeda":[17],"dengan":[18,91,104,118,142,172,193,201],"yang":[20,25,33,59,68,81,97],"lain.":[21],"Diperlukan":[22],"Ekstraksi":[23],"Fitur":[24],"dapat":[26],"menghasilkan":[27,57],"pada":[30,65,70,88,99,198],"tiap":[31,66,71],"diolah.":[34],"Tujuan":[35],"dari":[36,44,134],"penelitian":[37,89,100],"ini":[38,101],"adalah":[39,170],"menerapkan":[40],"hasil":[41,147,189],"ekstraksi":[42],"algoritma":[45],"SIFT":[46,56],"untuk":[47,152,167],"keperluan":[48],"image":[49],"matching":[50],"menggunakan":[51,77],"metode":[52,78],"Arc":[53,79,194],"Cosine.":[54],"Algoritma":[55],"keypoint":[58,67],"ciri":[61,63],"\u2013":[62],"citra.":[72],"Keypoint":[73],"tersebut":[74,145],"kemudian":[75],"diolah":[76],"Cosine":[80,195],"merupakan":[82],"inverse":[83],"trigonometri":[84],"dalam":[85],"ini,":[90],"beberapa":[92,119,143],"parameter":[93,144,202],"pengujian.":[94],"(misalnya).":[95],"Pengujian":[96],"dilakukan":[98],"meliputi":[102],"4":[105],"jenis":[106],"kompresi,":[107],"yaitu":[108],"PNG,":[109],"BMP,":[110,175],"JPEG,":[111],"dan":[112,129,138,162,177,185],"PGM,":[113],"masing":[114,115,179,180],"kompresi":[116],"diuji":[117],"parameter,":[120],"antara":[121],"lain":[122],":":[123],"perubahan":[124,127,130,153,158,163],"sudut":[125,154],"pandang,":[126],"skala,":[128],"rotasi.":[131],"Hasil":[132],"pengujian":[133],"100":[135],"training":[137],"1500":[139],"testing":[141],"didapatkan":[146],"rata":[148,149],"cocok":[151],"pandang":[155],"33,498":[156],"%,":[157,166,182],"skala":[159],"60,236":[160],"%":[161,184,187],"rotasi":[164,203],"82,779":[165],"compresi":[168],"terbaik":[169],"PNG":[171],"59,078%,":[173],"diikuti":[174],"PGM":[176],"JPEG":[178],"59,05":[181],"58,12":[183],"58,51":[186],"Dari":[188],"tersebut,":[190],"keunggulan":[197]},"counts_by_year":[{"year":2024,"cited_by_count":1},{"year":2022,"cited_by_count":2},{"year":2021,"cited_by_count":1},{"year":2019,"cited_by_count":3},{"year":2017,"cited_by_count":1}],"updated_date":"2025-11-06T04:12:42.849631","created_date":"2016-06-24T00:00:00"}
