{"id":"https://openalex.org/W3211433175","doi":"https://doi.org/10.34288/jri.v3i4.241","title":"COMPARATIVE STUDY OF FUZZY C-MEANS AND K-MEANS ALGORITHM FOR GROUPING CUSTOMER POTENTIAL IN BRAND LIMBACK","display_name":"COMPARATIVE STUDY OF FUZZY C-MEANS AND K-MEANS ALGORITHM FOR GROUPING CUSTOMER POTENTIAL IN BRAND LIMBACK","publication_year":2021,"publication_date":"2021-09-02","ids":{"openalex":"https://openalex.org/W3211433175","doi":"https://doi.org/10.34288/jri.v3i4.241","mag":"3211433175"},"language":"en","primary_location":{"id":"doi:10.34288/jri.v3i4.241","doi":"https://doi.org/10.34288/jri.v3i4.241","is_oa":true,"landing_page_url":"https://doi.org/10.34288/jri.v3i4.241","pdf_url":"https://ejournal.kresnamediapublisher.com/index.php/jri/article/download/241/109","source":{"id":"https://openalex.org/S4210216188","display_name":"Jurnal Riset Informatika","issn_l":"2656-1735","issn":["2656-1735","2656-1743"],"is_oa":true,"is_in_doaj":true,"is_core":false,"listed_in":["doaj"],"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"journal"},"license":"cc-by-nc","license_id":"https://openalex.org/licenses/cc-by-nc","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Jurnal Riset Informatika","raw_type":"journal-article"},"volume":null,"type":"article","is_traditional":true,"indexed_in":["crossref","doaj"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://ejournal.kresnamediapublisher.com/index.php/jri/article/download/241/109","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5060356116","display_name":"Difa Lazuardi Aditya","orcid":null,"observed_orcids":[]},"institutions":[{"id":"https://openalex.org/I177671886","display_name":"Mercu Buana University","ror":"https://ror.org/00qjgk605","country_code":"ID","type":"education","lineage":["https://openalex.org/I177671886"]}],"countries":["ID"],"is_corresponding":false,"raw_author_name":"Difa Lazuardi Aditya","raw_affiliation_strings":["Universitas Mercu Buana"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Universitas Mercu Buana","institution_ids":["https://openalex.org/I177671886"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5088025112","display_name":"Devi Fitrianah","orcid":"https://orcid.org/0000-0001-5029-8149","observed_orcids":["https://orcid.org/0000-0001-5029-8149"]},"institutions":[{"id":"https://openalex.org/I177671886","display_name":"Mercu Buana University","ror":"https://ror.org/00qjgk605","country_code":"ID","type":"education","lineage":["https://openalex.org/I177671886"]}],"countries":["ID"],"is_corresponding":false,"raw_author_name":"Devi Fitrianah","raw_affiliation_strings":["Universitas Mercu Buana"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Universitas Mercu Buana","institution_ids":["https://openalex.org/I177671886"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I177671886"],"apc_list":{"value":500000,"currency":"IDR","value_usd":32},"apc_paid":{"value":500000,"currency":"IDR","value_usd":32},"fwci":2.1196,"has_fulltext":true,"cited_by_count":16,"citation_normalized_percentile":{"value":0.887152,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":98},"biblio":{"volume":"3","issue":"4","first_page":"327","last_page":"334"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T12384","display_name":"Customer churn and segmentation","score":0.9556000232696533,"subfield":{"id":"https://openalex.org/subfields/1406","display_name":"Marketing"},"field":{"id":"https://openalex.org/fields/14","display_name":"Business, Management and Accounting"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}},"topics":[{"id":"https://openalex.org/T12384","display_name":"Customer churn and segmentation","score":0.9556000232696533,"subfield":{"id":"https://openalex.org/subfields/1406","display_name":"Marketing"},"field":{"id":"https://openalex.org/fields/14","display_name":"Business, Management and Accounting"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}},{"id":"https://openalex.org/T13373","display_name":"Data Mining and Machine Learning Applications","score":0.0348999984562397,"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/T10637","display_name":"Advanced Clustering Algorithms Research","score":0.00860000029206276,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/customer-segmentation","display_name":"customer segmentation","score":0.9649999737739563},{"id":"https://openalex.org/keywords/fuzzy-c-means-clustering","display_name":"fuzzy c-means clustering","score":0.8740000128746033},{"id":"https://openalex.org/keywords/k-means-clustering","display_name":"K-means clustering","score":0.9990000128746033},{"id":"https://openalex.org/keywords/rfm-analysis","display_name":"RFM analysis","score":0.9750000238418579},{"id":"https://openalex.org/keywords/customer-potential","display_name":"customer potential","score":0.48399999737739563},{"id":"https://openalex.org/keywords/clustering-evaluation","display_name":"cluster validation","score":0.7360000014305115},{"id":"https://openalex.org/keywords/silhouette-score","display_name":"silhouette coefficient","score":0.9449999928474426},{"id":"https://openalex.org/keywords/davies-bouldin-index","display_name":"Davies-Bouldin index","score":0.9760000109672546}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.549049437046051},{"id":"https://openalex.org/C58166","wikidata":"https://www.wikidata.org/wiki/Q224821","display_name":"Fuzzy logic","level":2,"score":0.5036258101463318},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.47176530957221985},{"id":"https://openalex.org/C207968372","wikidata":"https://www.wikidata.org/wiki/Q310401","display_name":"k-means clustering","level":3,"score":0.46343833208084106},{"id":"https://openalex.org/C43595421","wikidata":"https://www.wikidata.org/wiki/Q7939596","display_name":"Voice of the customer","level":5,"score":0.45778998732566833},{"id":"https://openalex.org/C130721881","wikidata":"https://www.wikidata.org/wiki/Q1146253","display_name":"Customer lifetime value","level":5,"score":0.45149654150009155},{"id":"https://openalex.org/C125308379","wikidata":"https://www.wikidata.org/wiki/Q363057","display_name":"Market segmentation","level":2,"score":0.44650745391845703},{"id":"https://openalex.org/C164866538","wikidata":"https://www.wikidata.org/wiki/Q367351","display_name":"Cluster (spacecraft)","level":2,"score":0.4283585548400879},{"id":"https://openalex.org/C89600930","wikidata":"https://www.wikidata.org/wiki/Q1423946","display_name":"Segmentation","level":2,"score":0.41481342911720276},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.41440674662590027},{"id":"https://openalex.org/C75949130","wikidata":"https://www.wikidata.org/wiki/Q848010","display_name":"Database transaction","level":2,"score":0.410758376121521},{"id":"https://openalex.org/C73555534","wikidata":"https://www.wikidata.org/wiki/Q622825","display_name":"Cluster analysis","level":2,"score":0.37799373269081116},{"id":"https://openalex.org/C140781008","wikidata":"https://www.wikidata.org/wiki/Q1221081","display_name":"Service quality","level":3,"score":0.2756161689758301},{"id":"https://openalex.org/C101276457","wikidata":"https://www.wikidata.org/wiki/Q5196474","display_name":"Customer retention","level":4,"score":0.26503026485443115},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.23450148105621338},{"id":"https://openalex.org/C162853370","wikidata":"https://www.wikidata.org/wiki/Q39809","display_name":"Marketing","level":1,"score":0.21093004941940308},{"id":"https://openalex.org/C144133560","wikidata":"https://www.wikidata.org/wiki/Q4830453","display_name":"Business","level":0,"score":0.1953831911087036},{"id":"https://openalex.org/C77088390","wikidata":"https://www.wikidata.org/wiki/Q8513","display_name":"Database","level":1,"score":0.11635106801986694},{"id":"https://openalex.org/C2780378061","wikidata":"https://www.wikidata.org/wiki/Q25351891","display_name":"Service (business)","level":2,"score":0.09434357285499573},{"id":"https://openalex.org/C199360897","wikidata":"https://www.wikidata.org/wiki/Q9143","display_name":"Programming language","level":1,"score":0.0}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.34288/jri.v3i4.241","doi":"https://doi.org/10.34288/jri.v3i4.241","is_oa":true,"landing_page_url":"https://doi.org/10.34288/jri.v3i4.241","pdf_url":"https://ejournal.kresnamediapublisher.com/index.php/jri/article/download/241/109","source":{"id":"https://openalex.org/S4210216188","display_name":"Jurnal Riset Informatika","issn_l":"2656-1735","issn":["2656-1735","2656-1743"],"is_oa":true,"is_in_doaj":true,"is_core":false,"listed_in":["doaj"],"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"journal"},"license":"cc-by-nc","license_id":"https://openalex.org/licenses/cc-by-nc","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Jurnal Riset Informatika","raw_type":"journal-article"},{"id":"pmh:oai:doaj.org/article:59a3201038284a88bc5d65bb7b0adcfb","doi":null,"is_oa":true,"landing_page_url":"https://doaj.org/article/59a3201038284a88bc5d65bb7b0adcfb","pdf_url":null,"source":{"id":"https://openalex.org/S4306401280","display_name":"DOAJ (DOAJ: Directory of Open Access Journals)","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":"repository"},"license":"cc-by-sa","license_id":"https://openalex.org/licenses/cc-by-sa","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"Jurnal Riset Informatika, Vol 3, Iss 4, Pp 327-334 (2021)","raw_type":"article"}],"best_oa_location":{"id":"doi:10.34288/jri.v3i4.241","doi":"https://doi.org/10.34288/jri.v3i4.241","is_oa":true,"landing_page_url":"https://doi.org/10.34288/jri.v3i4.241","pdf_url":"https://ejournal.kresnamediapublisher.com/index.php/jri/article/download/241/109","source":{"id":"https://openalex.org/S4210216188","display_name":"Jurnal Riset Informatika","issn_l":"2656-1735","issn":["2656-1735","2656-1743"],"is_oa":true,"is_in_doaj":true,"is_core":false,"listed_in":["doaj"],"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"journal"},"license":"cc-by-nc","license_id":"https://openalex.org/licenses/cc-by-nc","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Jurnal Riset Informatika","raw_type":"journal-article"},"sustainable_development_goals":[],"study_designs":[],"awards":[],"funders":[],"has_content":{"pdf":true,"grobid_xml":true},"content_urls":{"pdf":"https://content.openalex.org/works/W3211433175.pdf","grobid_xml":"https://content.openalex.org/works/W3211433175.grobid-xml"},"referenced_works_count":12,"referenced_works":["https://openalex.org/W2800101553","https://openalex.org/W2806526739","https://openalex.org/W2884234965","https://openalex.org/W2900477687","https://openalex.org/W2904991995","https://openalex.org/W2912896031","https://openalex.org/W2983526145","https://openalex.org/W3004321847","https://openalex.org/W3018825249","https://openalex.org/W3127422258","https://openalex.org/W4235495418","https://openalex.org/W4238667590"],"related_works":["https://openalex.org/W4250988393","https://openalex.org/W6903733506","https://openalex.org/W4311507476","https://openalex.org/W4391619674","https://openalex.org/W4412176430","https://openalex.org/W3006266789","https://openalex.org/W2891398129","https://openalex.org/W4409829095","https://openalex.org/W4386693694","https://openalex.org/W4312659713"],"abstract_inverted_index":{"The":[0,49,82,174],"customer":[1,12,26,64],"is":[2,22,80,133,139,145,151,157,163,170],"a":[3,6,134,140,146,152,158,164,171,184,194,212,222],"stakeholder":[4],"for":[5,17,63],"business,":[7],"to":[8,24,30],"maintain":[9],"and":[10,14,93,112,167,198],"increase":[11],"enthusiasm":[13],"develop":[15],"it":[16,21],"the":[18,46,60,68,74,77,90,96,101,104,109,119,180,189,199,203,208,217],"company's":[19],"performance,":[20],"necessary":[23],"do":[25],"segmentation":[27,117],"which":[28],"aims":[29],"find":[31],"out":[32],"potential":[33],"customers.":[34],"This":[35],"study":[36],"uses":[37],"purchase":[38],"transaction":[39],"data":[40],"from":[41,73,85,118],"Brand":[42],"Limback":[43],"customers":[44],"in":[45,58,103],"period":[47],"2020.":[48],"use":[50],"of":[51,71,106,177,186,196,202,214,224],"RFM":[52,75,86,120],"(Recency,":[53],"Frecuency,":[54],"Monetary)":[55],"analysis":[56],"helps":[57],"determining":[59],"attributes":[61],"used":[62],"segmentation.":[65],"To":[66],"determine":[67],"optimal":[69,128],"number":[70],"clusters":[72,107],"dataset,":[76],"Elbow":[78],"method":[79],"applied.":[81],"datasets":[83],"generated":[84],"are":[87],"grouped":[88],"using":[89,108,179,207],"Fuzzy":[91],"C-Means":[92],"K-Means":[94],"algorithms,":[95],"two":[97],"algorithms":[98],"will":[99],"compare":[100],"quality":[102],"formation":[105],"Silhoutte":[110],"Coefficient":[111],"Davies-Bouldin":[113],"Index":[114],"methods.":[115],"Customer":[116],"dataset":[121],"that":[122],"has":[123],"been":[124],"clustered":[125],"produces":[126,183,193,211,221],"7":[127],"clusters,":[129],"namely":[130],"Cluster":[131,137,143,149,155,161],"0":[132],"bronze":[135],"customer.":[136,142,148,154,160,173],"1":[138],"silver":[141],"2":[144],"gold":[147],"3":[150],"platinum":[153],"4":[156],"diamond":[159],"5":[162],"super":[165],"customer,":[166],"cluster":[168,175],"6":[169],"superstar":[172],"validation":[176,200],"k-means":[178],"silhouette":[181,209],"coefficient":[182,210],"value":[185,195,213,223],"0.934":[187],"while":[188,216],"Davies":[190,218],"bouldin":[191,219],"index":[192,220],"0.155":[197],"results":[201],"fuzzy":[204],"c-means":[205],"algorithm":[206],"0.921":[215],"0.145.":[225]},"counts_by_year":[{"year":2026,"cited_by_count":2},{"year":2025,"cited_by_count":1},{"year":2024,"cited_by_count":5},{"year":2023,"cited_by_count":6},{"year":2022,"cited_by_count":2}],"updated_date":"2026-11-04T10:54:05.112534","created_date":"2021-11-22T00:00:00"}