{"id":"https://openalex.org/W4315926596","doi":"https://doi.org/10.1109/icic56845.2022.10007001","title":"A PSO-GBR Solution for Association Rule Optimization on Supermarket Sales","display_name":"A PSO-GBR Solution for Association Rule Optimization on Supermarket Sales","publication_year":2022,"publication_date":"2022-12-08","ids":{"openalex":"https://openalex.org/W4315926596","doi":"https://doi.org/10.1109/icic56845.2022.10007001"},"language":"en","primary_location":{"id":"doi:10.1109/icic56845.2022.10007001","doi":"https://doi.org/10.1109/icic56845.2022.10007001","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icic56845.2022.10007001","pdf_url":null,"source":{"id":"https://openalex.org/S4306498392","display_name":"International Conference on Informatics and Computing (ICIC)","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":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2022 Seventh International Conference on Informatics and Computing (ICIC)","raw_type":"proceedings-article"},"volume":null,"type":"conference-paper","is_traditional":true,"indexed_in":["crossref"],"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/A5067501352","display_name":"Syafrial Fachri Pane","orcid":"https://orcid.org/0000-0001-5119-3808","observed_orcids":["https://orcid.org/0000-0001-5119-3808"]},"institutions":[{"id":"https://openalex.org/I862893732","display_name":"Telkom University","ror":"https://ror.org/0004wsx81","country_code":"ID","type":"education","lineage":["https://openalex.org/I862893732"]}],"countries":["ID"],"is_corresponding":false,"raw_author_name":"Syafrial Fachri Pane","raw_affiliation_strings":["Advanced and Creative Networks Research Center, Telkom University, Bandung, Indonesia"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Advanced and Creative Networks Research Center, Telkom University, Bandung, Indonesia","institution_ids":["https://openalex.org/I862893732"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5025084403","display_name":"Aji Gautama Putrada","orcid":"https://orcid.org/0000-0001-8677-0177","observed_orcids":["https://orcid.org/0000-0001-8677-0177"]},"institutions":[{"id":"https://openalex.org/I862893732","display_name":"Telkom University","ror":"https://ror.org/0004wsx81","country_code":"ID","type":"education","lineage":["https://openalex.org/I862893732"]}],"countries":["ID"],"is_corresponding":false,"raw_author_name":"Aji Gautama Putrada","raw_affiliation_strings":["Advanced and Creative Networks Research Center, Telkom University, Bandung, Indonesia"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Advanced and Creative Networks Research Center, Telkom University, Bandung, Indonesia","institution_ids":["https://openalex.org/I862893732"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5007745107","display_name":"Nur Alamsyah","orcid":"https://orcid.org/0009-0003-4235-5512","observed_orcids":["https://orcid.org/0009-0003-4235-5512"]},"institutions":[{"id":"https://openalex.org/I862893732","display_name":"Telkom University","ror":"https://ror.org/0004wsx81","country_code":"ID","type":"education","lineage":["https://openalex.org/I862893732"]}],"countries":["ID"],"is_corresponding":false,"raw_author_name":"Nur Alamsyah","raw_affiliation_strings":["Advanced and Creative Networks Research Center, Telkom University, Bandung, Indonesia"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Advanced and Creative Networks Research Center, Telkom University, Bandung, Indonesia","institution_ids":["https://openalex.org/I862893732"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5032804843","display_name":"Mohamad Nurkamal Fauzan","orcid":"https://orcid.org/0000-0002-2371-1820","observed_orcids":["https://orcid.org/0000-0002-2371-1820"]},"institutions":[{"id":"https://openalex.org/I862893732","display_name":"Telkom University","ror":"https://ror.org/0004wsx81","country_code":"ID","type":"education","lineage":["https://openalex.org/I862893732"]}],"countries":["ID"],"is_corresponding":false,"raw_author_name":"Mohamad Nurkamal Fauzan","raw_affiliation_strings":["Advanced and Creative Networks Research Center, Telkom University, Bandung, Indonesia"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Advanced and Creative Networks Research Center, Telkom University, Bandung, Indonesia","institution_ids":["https://openalex.org/I862893732"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I862893732"],"apc_list":null,"apc_paid":null,"fwci":1.8984,"has_fulltext":false,"cited_by_count":12,"citation_normalized_percentile":{"value":0.87222175,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":91,"max":98},"biblio":{"volume":null,"issue":null,"first_page":"1","last_page":"6"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T13373","display_name":"Data Mining and Machine Learning Applications","score":0.875,"subfield":{"id":"https://openalex.org/subfields/1710","display_name":"Information Systems"},"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/T13373","display_name":"Data Mining and Machine Learning Applications","score":0.875,"subfield":{"id":"https://openalex.org/subfields/1710","display_name":"Information Systems"},"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/T10100","display_name":"Metaheuristic Optimization Algorithms Research","score":0.05090000107884407,"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/T11918","display_name":"Forecasting Techniques and Applications","score":0.025499999523162842,"subfield":{"id":"https://openalex.org/subfields/1803","display_name":"Management Science and Operations Research"},"field":{"id":"https://openalex.org/fields/18","display_name":"Decision Sciences"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/particle-swarm-optimization","display_name":"particle swarm optimization","score":0.9269999861717224},{"id":"https://openalex.org/keywords/gradient-boosting-regression","display_name":"gradient boosting regression","score":0.9990000128746033},{"id":"https://openalex.org/keywords/supermarket-sales","display_name":"supermarket sales","score":0.9419999718666077},{"id":"https://openalex.org/keywords/sales-optimization","display_name":"sales optimization","score":0.5509999990463257},{"id":"https://openalex.org/keywords/cost-function-minimization","display_name":"cost function minimization","score":0.6809999942779541},{"id":"https://openalex.org/keywords/linear-regression-comparison","display_name":"linear regression comparison","score":0.6349999904632568}],"concepts":[{"id":"https://openalex.org/C85617194","wikidata":"https://www.wikidata.org/wiki/Q2072794","display_name":"Particle swarm optimization","level":2,"score":0.7219785451889038},{"id":"https://openalex.org/C185798385","wikidata":"https://www.wikidata.org/wiki/Q1161707","display_name":"Benchmark (surveying)","level":2,"score":0.6664441823959351},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6430600881576538},{"id":"https://openalex.org/C46686674","wikidata":"https://www.wikidata.org/wiki/Q466303","display_name":"Boosting (machine learning)","level":2,"score":0.6409370303153992},{"id":"https://openalex.org/C83546350","wikidata":"https://www.wikidata.org/wiki/Q1139051","display_name":"Regression","level":2,"score":0.517639696598053},{"id":"https://openalex.org/C152877465","wikidata":"https://www.wikidata.org/wiki/Q208042","display_name":"Regression analysis","level":2,"score":0.507174015045166},{"id":"https://openalex.org/C141404830","wikidata":"https://www.wikidata.org/wiki/Q2823869","display_name":"AdaBoost","level":3,"score":0.49608978629112244},{"id":"https://openalex.org/C193524817","wikidata":"https://www.wikidata.org/wiki/Q386780","display_name":"Association rule learning","level":2,"score":0.4885305166244507},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.47957342863082886},{"id":"https://openalex.org/C48921125","wikidata":"https://www.wikidata.org/wiki/Q10861030","display_name":"Linear regression","level":2,"score":0.43124499917030334},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3578068017959595},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.3350524604320526},{"id":"https://openalex.org/C12267149","wikidata":"https://www.wikidata.org/wiki/Q282453","display_name":"Support vector machine","level":2,"score":0.22833037376403809},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.22342538833618164},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.17517414689064026},{"id":"https://openalex.org/C205649164","wikidata":"https://www.wikidata.org/wiki/Q1071","display_name":"Geography","level":0,"score":0.0},{"id":"https://openalex.org/C13280743","wikidata":"https://www.wikidata.org/wiki/Q131089","display_name":"Geodesy","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/icic56845.2022.10007001","doi":"https://doi.org/10.1109/icic56845.2022.10007001","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icic56845.2022.10007001","pdf_url":null,"source":{"id":"https://openalex.org/S4306498392","display_name":"International Conference on Informatics and Computing (ICIC)","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":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2022 Seventh International Conference on Informatics and Computing (ICIC)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"study_designs":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":26,"referenced_works":["https://openalex.org/W2777413000","https://openalex.org/W2792290891","https://openalex.org/W2794238513","https://openalex.org/W2905464628","https://openalex.org/W2933936427","https://openalex.org/W2973971969","https://openalex.org/W2998884377","https://openalex.org/W3006398854","https://openalex.org/W3018303008","https://openalex.org/W3035060851","https://openalex.org/W3044123268","https://openalex.org/W3047731953","https://openalex.org/W3100966371","https://openalex.org/W3128565383","https://openalex.org/W3180215666","https://openalex.org/W3189032582","https://openalex.org/W3200630340","https://openalex.org/W4206550351","https://openalex.org/W4210729041","https://openalex.org/W4221012579","https://openalex.org/W4221154964","https://openalex.org/W4226062023","https://openalex.org/W4226500581","https://openalex.org/W4281776974","https://openalex.org/W4283258777","https://openalex.org/W4285263841"],"related_works":["https://openalex.org/W4402264223","https://openalex.org/W4413925663","https://openalex.org/W4400282634","https://openalex.org/W4205603451","https://openalex.org/W4399653266","https://openalex.org/W4407085625","https://openalex.org/W2968808802","https://openalex.org/W2202159429","https://openalex.org/W4405166368","https://openalex.org/W4415917655"],"abstract_inverted_index":{"In":[0],"the":[1,51,105,115,138,178,188,193,217],"era":[2],"of":[3,11,192,199,205],"big":[4],"data":[5,14,93,165],"and":[6,15,85,157,161,166],"cloud":[7],"computing,":[8],"digital":[9],"records":[10],"supermarket":[12,55],"sales":[13,56,103,128,176],"other":[16],"accompanying":[17],"factors":[18,32],"are":[19],"ubiquitous.":[20],"However,":[21],"less":[22],"than":[23,155],"optimal":[24],"WeeklySale":[25,142],"s":[26,143],"can":[27,63,101,186,225],"occur":[28],"due":[29],"to":[30,113,135,174,202,216],"several":[31],"influencing":[33],"it.":[34],"This":[35,220],"study":[36],"proposes":[37],"particle":[38],"swarm":[39],"optimization":[40,239],"with":[41,77,117,121,230],"gradient":[42],"boosting":[43],"regression":[44,60,83,124,228],"(PSO-GBR)":[45],"as":[46,181],"a":[47,59,67,98,153,182,197,203,211,227,234],"solution":[48],"for":[49,69,163,233,240],"optimizing":[50],"association":[52],"rule":[53],"in":[54,237],"based":[57],"on":[58],"model":[61,99,116,180,229],"that":[62,100,137,148,223],"predict":[64,102],"WeeklySales.":[65],"As":[66],"benchmark":[68,118],"this":[70],"research,":[71],"we":[72,96,126,224],"compare":[73],"our":[74,149],"proposed":[75,150],"GBR":[76,151,179,218],"two":[78],"legacy":[79],"prediction":[80],"methods:":[81],"linear":[82],"(LR)":[84],"AdaBoost":[86],"Regression":[87],"(ABR).":[88],"The":[89,109,131,145],"first":[90],"step":[91,111,133],"is":[92,112,134,172],"preparation.":[94],"Then":[95,120,170],"develop":[97],"from":[104,196],"dataset":[106,195],"using":[107,129,177],"GBR.":[108],"next":[110],"evaluate":[114],"methods.":[119],"an":[122],"optimum":[123,141],"method,":[125],"optimize":[127,175],"PSO.":[130],"last":[132],"show":[136,147],"method":[139],"provides":[140],"results.":[144],"results":[146],"has":[152],"higher$R^{2}$score":[154],"LR":[156],"ABR,":[158],"namely":[159],"0.95":[160],"0.94":[162],"train":[164],"test":[167],"data,":[168],"respectively.":[169],"PSO":[171,185,238],"proven":[173],"cost":[183,235],"function.":[184],"increase":[187],"ten":[189],"lowest":[190],"WeeklySales":[191],"actual":[194],"total":[198,204],"US$":[200,206],"45":[201],"2,":[207],"296.9":[208],"by":[209],"selecting":[210],"more":[212],"optimized":[213],"Department":[214],"according":[215],"prediction.":[219],"research":[221],"proves":[222],"use":[226],"good":[231],"performance":[232],"function":[236],"department":[241],"sales.":[242]},"counts_by_year":[{"year":2026,"cited_by_count":1},{"year":2024,"cited_by_count":6},{"year":2023,"cited_by_count":5}],"updated_date":"2026-11-10T11:39:43.021295","created_date":"2023-01-14T00:00:00"}
