{"id":"https://openalex.org/W7127045605","doi":"https://doi.org/10.59356/smart-techno.v8i1.173","title":"Predicting Employee Attrition Using the Random Forest Algorithm Based on IBM HR Analytics Data","display_name":"Predicting Employee Attrition Using the Random Forest Algorithm Based on IBM HR Analytics Data","publication_year":2026,"publication_date":"2026-01-14","ids":{"openalex":"https://openalex.org/W7127045605","doi":"https://doi.org/10.59356/smart-techno.v8i1.173"},"language":"en","primary_location":{"id":"doi:10.59356/smart-techno.v8i1.173","doi":"https://doi.org/10.59356/smart-techno.v8i1.173","is_oa":true,"landing_page_url":"https://doi.org/10.59356/smart-techno.v8i1.173","pdf_url":null,"source":{"id":"https://openalex.org/S4387288611","display_name":"Smart Techno (Smart Technology Informatics and Technopreneurship)","issn_l":"2541-0679","issn":["2541-0679"],"is_oa":true,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":"https://openalex.org/I4405276252","host_organization_name":"Universitas Primakara","host_organization_lineage":["https://openalex.org/I4405276252"],"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":"Smart Techno (Smart Technology, Informatics and Technopreneurship)","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://doi.org/10.59356/smart-techno.v8i1.173","any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5124706753","display_name":"Putu Satya Saputra","orcid":null,"observed_orcids":[]},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Putu Satya Saputra","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5061339716","display_name":"I Putu Gede Abdi Sudiatmika","orcid":"https://orcid.org/0000-0002-4532-5170","observed_orcids":["https://orcid.org/0000-0002-4532-5170"]},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"I Putu Gede Abdi Sudiatmika","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5124695303","display_name":"Ni Putu Meiling Utami","orcid":null,"observed_orcids":[]},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Ni Putu Meiling Utami","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5124710071","display_name":"I Putu Okta Priyana","orcid":null,"observed_orcids":[]},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"I Putu Okta Priyana","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":0,"corresponding_author_ids":[],"corresponding_institution_ids":[],"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":"8","issue":"1","first_page":"51","last_page":"62"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T13812","display_name":"AI and HR Technologies","score":0.9990000128746033,"subfield":{"id":"https://openalex.org/subfields/1407","display_name":"Organizational Behavior and Human Resource Management"},"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/T13812","display_name":"AI and HR Technologies","score":0.9990000128746033,"subfield":{"id":"https://openalex.org/subfields/1407","display_name":"Organizational Behavior and Human Resource Management"},"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/T12384","display_name":"Customer churn and segmentation","score":0.0008999999845400453,"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":9.999999747378752e-05,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/employee-attrition","display_name":"employee attrition","score":0.9240000247955322},{"id":"https://openalex.org/keywords/employee-turnover-prediction","display_name":"employee turnover prediction","score":0.9610000252723694},{"id":"https://openalex.org/keywords/random-forest","display_name":"random forest","score":0.8560000061988831},{"id":"https://openalex.org/keywords/smote","display_name":"SMOTE","score":0.8230000138282776},{"id":"https://openalex.org/keywords/class-imbalance","display_name":"class imbalance","score":0.9869999885559082},{"id":"https://openalex.org/keywords/feature-importance","display_name":"feature importance","score":0.7710000276565552},{"id":"https://openalex.org/keywords/monthly-income","display_name":"monthly income","score":0.8349999785423279},{"id":"https://openalex.org/keywords/job-satisfaction","display_name":"job satisfaction","score":1.0},{"id":"https://openalex.org/keywords/employee-retention-strategies","display_name":"employee retention strategies","score":0.7519999742507935}],"concepts":[{"id":"https://openalex.org/C2780553607","wikidata":"https://www.wikidata.org/wiki/Q684868","display_name":"Attrition","level":2,"score":0.930899977684021},{"id":"https://openalex.org/C70388272","wikidata":"https://www.wikidata.org/wiki/Q5968558","display_name":"IBM","level":2,"score":0.6863999962806702},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6789000034332275},{"id":"https://openalex.org/C79158427","wikidata":"https://www.wikidata.org/wiki/Q485396","display_name":"Analytics","level":2,"score":0.5654000043869019},{"id":"https://openalex.org/C2778139618","wikidata":"https://www.wikidata.org/wiki/Q13440398","display_name":"Workforce","level":2,"score":0.548799991607666},{"id":"https://openalex.org/C169258074","wikidata":"https://www.wikidata.org/wiki/Q245748","display_name":"Random forest","level":2,"score":0.5232999920845032},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.39469999074935913},{"id":"https://openalex.org/C2522767166","wikidata":"https://www.wikidata.org/wiki/Q2374463","display_name":"Data science","level":1,"score":0.36570000648498535},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3594000041484833},{"id":"https://openalex.org/C138602881","wikidata":"https://www.wikidata.org/wiki/Q2709591","display_name":"Confusion matrix","level":2,"score":0.35260000824928284},{"id":"https://openalex.org/C12725497","wikidata":"https://www.wikidata.org/wiki/Q810247","display_name":"Baseline (sea)","level":2,"score":0.3336000144481659},{"id":"https://openalex.org/C2781140086","wikidata":"https://www.wikidata.org/wiki/Q557945","display_name":"Confusion","level":2,"score":0.3095000088214874},{"id":"https://openalex.org/C133462117","wikidata":"https://www.wikidata.org/wiki/Q4929239","display_name":"Data collection","level":2,"score":0.3061999976634979},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.289000004529953},{"id":"https://openalex.org/C121017731","wikidata":"https://www.wikidata.org/wiki/Q11661","display_name":"Information technology","level":2,"score":0.2865999937057495},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.28380000591278076},{"id":"https://openalex.org/C75684735","wikidata":"https://www.wikidata.org/wiki/Q858810","display_name":"Big data","level":2,"score":0.2662000060081482},{"id":"https://openalex.org/C77088390","wikidata":"https://www.wikidata.org/wiki/Q8513","display_name":"Database","level":1,"score":0.26339998841285706},{"id":"https://openalex.org/C84525736","wikidata":"https://www.wikidata.org/wiki/Q831366","display_name":"Decision tree","level":2,"score":0.26330000162124634},{"id":"https://openalex.org/C26077564","wikidata":"https://www.wikidata.org/wiki/Q1910318","display_name":"Turnover","level":2,"score":0.2599000036716461},{"id":"https://openalex.org/C195094911","wikidata":"https://www.wikidata.org/wiki/Q14167904","display_name":"Process management","level":1,"score":0.2574000060558319},{"id":"https://openalex.org/C3019136120","wikidata":"https://www.wikidata.org/wiki/Q2462428","display_name":"Two step","level":2,"score":0.2567000091075897},{"id":"https://openalex.org/C2777655877","wikidata":"https://www.wikidata.org/wiki/Q3475740","display_name":"Design science research","level":3,"score":0.2542000114917755}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.59356/smart-techno.v8i1.173","doi":"https://doi.org/10.59356/smart-techno.v8i1.173","is_oa":true,"landing_page_url":"https://doi.org/10.59356/smart-techno.v8i1.173","pdf_url":null,"source":{"id":"https://openalex.org/S4387288611","display_name":"Smart Techno (Smart Technology Informatics and Technopreneurship)","issn_l":"2541-0679","issn":["2541-0679"],"is_oa":true,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":"https://openalex.org/I4405276252","host_organization_name":"Universitas Primakara","host_organization_lineage":["https://openalex.org/I4405276252"],"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":"Smart Techno (Smart Technology, Informatics and Technopreneurship)","raw_type":"journal-article"}],"best_oa_location":{"id":"doi:10.59356/smart-techno.v8i1.173","doi":"https://doi.org/10.59356/smart-techno.v8i1.173","is_oa":true,"landing_page_url":"https://doi.org/10.59356/smart-techno.v8i1.173","pdf_url":null,"source":{"id":"https://openalex.org/S4387288611","display_name":"Smart Techno (Smart Technology Informatics and Technopreneurship)","issn_l":"2541-0679","issn":["2541-0679"],"is_oa":true,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":"https://openalex.org/I4405276252","host_organization_name":"Universitas Primakara","host_organization_lineage":["https://openalex.org/I4405276252"],"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":"Smart Techno (Smart Technology, Informatics and Technopreneurship)","raw_type":"journal-article"},"sustainable_development_goals":[{"score":0.5263000130653381,"id":"https://metadata.un.org/sdg/8","display_name":"Decent work and economic growth"}],"study_designs":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":["https://openalex.org/W7222882602","https://openalex.org/W4414683847","https://openalex.org/W4411447821","https://openalex.org/W7148846294","https://openalex.org/W4389849991","https://openalex.org/W4412125857","https://openalex.org/W7169030954","https://openalex.org/W7161169015","https://openalex.org/W7171651908","https://openalex.org/W4414100051"],"abstract_inverted_index":{"The":[0,82,113,149],"phenomenon":[1],"of":[2,76,132,147,167],"employee":[3,29,56,171,187],"attrition":[4,57,127,172],"has":[5],"become":[6],"a":[7,110,144,164,177],"serious":[8],"challenge":[9],"for":[10,125,139,179],"organizations,":[11],"as":[12,176],"it":[13],"directly":[14],"affects":[15],"productivity,":[16],"recruitment":[17],"costs,":[18],"and":[19,79,100,109,159,173,185],"long-term":[20],"performance":[21,101],"stability.":[22],"Understanding":[23],"the":[24,59,65,92,117,126,130,140,168],"factors":[25,169],"that":[26,116],"lead":[27],"to":[28,44,54,64],"turnover":[30],"can":[31,174],"no":[32],"longer":[33],"rely":[34],"solely":[35],"on":[36],"manual":[37],"observation;":[38],"therefore,":[39],"data-driven":[40,186],"approaches":[41],"are":[42,154],"required":[43],"identify":[45],"hidden":[46],"patterns":[47],"within":[48],"workforce":[49],"data.":[50],"This":[51],"study":[52],"aims":[53],"predict":[55],"using":[58,91,103],"Random":[60],"Forest":[61],"algorithm":[62],"applied":[63],"IBM":[66],"HR":[67],"Analytics":[68],"Employee":[69],"Attrition":[70],"&":[71],"Performance":[72],"dataset,":[73],"which":[74],"consists":[75],"1,470":[77],"records":[78],"35":[80],"attributes.":[81],"research":[83],"stages":[84],"include":[85],"data":[86],"preprocessing,":[87],"handling":[88],"class":[89],"imbalance":[90],"Synthetic":[93],"Minority":[94],"Over-sampling":[95],"Technique":[96],"(SMOTE),":[97],"model":[98,119,136],"training,":[99],"evaluation":[102],"accuracy,":[104],"precision,":[105],"recall,":[106],"F1-score,":[107],"ROC-AUC,":[108],"confusion":[111],"matrix.":[112],"results":[114],"indicate":[115],"baseline":[118],"without":[120],"SMOTE":[121,133],"exhibits":[122],"low":[123],"recall":[124],"class,":[128,142],"whereas":[129],"application":[131],"significantly":[134],"improves":[135],"performance,":[137],"particularly":[138],"minority":[141],"achieving":[143],"final":[145],"accuracy":[146],"83.96%.":[148],"most":[150],"influential":[151],"features":[152],"identified":[153],"Stock":[155],"Option":[156],"Level,":[157],"MonthlyIncome,":[158],"JobSatisfaction.":[160],"These":[161],"findings":[162],"provide":[163],"comprehensive":[165],"understanding":[166],"influencing":[170],"serve":[175],"foundation":[178],"organizations":[180],"in":[181],"designing":[182],"more":[183],"adaptive":[184],"retention":[188],"strategies.":[189]},"counts_by_year":[],"updated_date":"2026-10-28T10:33:24.094632","created_date":"2026-02-03T00:00:00"}
