{"id":"https://openalex.org/W4389888605","doi":"https://doi.org/10.1109/iceei59426.2023.10346226","title":"Analysing Imbalanced Dataset for Postgraduate Student Dropout Using Predictive Analytics","display_name":"Analysing Imbalanced Dataset for Postgraduate Student Dropout Using Predictive Analytics","publication_year":2023,"publication_date":"2023-10-10","ids":{"openalex":"https://openalex.org/W4389888605","doi":"https://doi.org/10.1109/iceei59426.2023.10346226"},"language":"en","primary_location":{"id":"doi:10.1109/iceei59426.2023.10346226","is_oa":false,"landing_page_url":"http://dx.doi.org/10.1109/iceei59426.2023.10346226","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2023 International Conference on Electrical Engineering and Informatics (ICEEI)","raw_type":"proceedings-article"},"type":"article","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/A5114109954","display_name":"Mohamad Akmal Bin Mohd Rosly","orcid":null},"institutions":[{"id":"https://openalex.org/I4210140655","display_name":"Jabatan Perkhidmatan Awam Malaysia","ror":"https://ror.org/04kpqhb39","country_code":"MY","type":"government","lineage":["https://openalex.org/I4210140655"]}],"countries":["MY"],"is_corresponding":true,"raw_author_name":"Mohamad Akmal Bin Mohd Rosly","raw_affiliation_strings":["Lorong Selangor Pusat Komersial Gaya Pusat Bandar Melawati,DPO House, B2-G,Kuala Lumpur,Malaysia,53100"],"affiliations":[{"raw_affiliation_string":"Lorong Selangor Pusat Komersial Gaya Pusat Bandar Melawati,DPO House, B2-G,Kuala Lumpur,Malaysia,53100","institution_ids":["https://openalex.org/I4210140655"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5061842277","display_name":"Suhaila Zainudin","orcid":"https://orcid.org/0000-0003-2352-5312"},"institutions":[{"id":"https://openalex.org/I4210140655","display_name":"Jabatan Perkhidmatan Awam Malaysia","ror":"https://ror.org/04kpqhb39","country_code":"MY","type":"government","lineage":["https://openalex.org/I4210140655"]}],"countries":["MY"],"is_corresponding":false,"raw_author_name":"Suhaila Zainudin","raw_affiliation_strings":["Lorong Selangor Pusat Komersial Gaya Pusat Bandar Melawati,DPO House, B2-G,Kuala Lumpur,Malaysia,53100"],"affiliations":[{"raw_affiliation_string":"Lorong Selangor Pusat Komersial Gaya Pusat Bandar Melawati,DPO House, B2-G,Kuala Lumpur,Malaysia,53100","institution_ids":["https://openalex.org/I4210140655"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5002533166","display_name":"Junaidah Mohamed Kassim","orcid":null},"institutions":[{"id":"https://openalex.org/I4210140655","display_name":"Jabatan Perkhidmatan Awam Malaysia","ror":"https://ror.org/04kpqhb39","country_code":"MY","type":"government","lineage":["https://openalex.org/I4210140655"]}],"countries":["MY"],"is_corresponding":false,"raw_author_name":"Junaidah Mohamed Kassim","raw_affiliation_strings":["Lorong Selangor Pusat Komersial Gaya Pusat Bandar Melawati,DPO House, B2-G,Kuala Lumpur,Malaysia,53100"],"affiliations":[{"raw_affiliation_string":"Lorong Selangor Pusat Komersial Gaya Pusat Bandar Melawati,DPO House, B2-G,Kuala Lumpur,Malaysia,53100","institution_ids":["https://openalex.org/I4210140655"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5114109954"],"corresponding_institution_ids":["https://openalex.org/I4210140655"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.17440484,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"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/T11652","display_name":"Imbalanced Data Classification Techniques","score":0.9983999729156494,"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"}},"topics":[{"id":"https://openalex.org/T11652","display_name":"Imbalanced Data Classification Techniques","score":0.9983999729156494,"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/T11512","display_name":"Anomaly Detection Techniques and Applications","score":0.9790999889373779,"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/T11396","display_name":"Artificial Intelligence in Healthcare","score":0.973800003528595,"subfield":{"id":"https://openalex.org/subfields/3605","display_name":"Health Information Management"},"field":{"id":"https://openalex.org/fields/36","display_name":"Health Professions"},"domain":{"id":"https://openalex.org/domains/4","display_name":"Health Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/dropout","display_name":"Dropout (neural networks)","score":0.8824635744094849},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.658565878868103},{"id":"https://openalex.org/keywords/predictive-analytics","display_name":"Predictive analytics","score":0.6568405628204346},{"id":"https://openalex.org/keywords/learning-analytics","display_name":"Learning analytics","score":0.596165657043457},{"id":"https://openalex.org/keywords/analytics","display_name":"Analytics","score":0.5728235840797424},{"id":"https://openalex.org/keywords/data-science","display_name":"Data science","score":0.43476057052612305},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.40121668577194214},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.33270519971847534}],"concepts":[{"id":"https://openalex.org/C2776145597","wikidata":"https://www.wikidata.org/wiki/Q25339462","display_name":"Dropout (neural networks)","level":2,"score":0.8824635744094849},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.658565878868103},{"id":"https://openalex.org/C83209312","wikidata":"https://www.wikidata.org/wiki/Q1053367","display_name":"Predictive analytics","level":2,"score":0.6568405628204346},{"id":"https://openalex.org/C2777648619","wikidata":"https://www.wikidata.org/wiki/Q2845208","display_name":"Learning analytics","level":2,"score":0.596165657043457},{"id":"https://openalex.org/C79158427","wikidata":"https://www.wikidata.org/wiki/Q485396","display_name":"Analytics","level":2,"score":0.5728235840797424},{"id":"https://openalex.org/C2522767166","wikidata":"https://www.wikidata.org/wiki/Q2374463","display_name":"Data science","level":1,"score":0.43476057052612305},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.40121668577194214},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.33270519971847534}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/iceei59426.2023.10346226","is_oa":false,"landing_page_url":"http://dx.doi.org/10.1109/iceei59426.2023.10346226","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2023 International Conference on Electrical Engineering and Informatics (ICEEI)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[{"id":"https://openalex.org/F4320322699","display_name":"Universiti Kebangsaan Malaysia","ror":"https://ror.org/00bw8d226"}],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":9,"referenced_works":["https://openalex.org/W2529456000","https://openalex.org/W2810022699","https://openalex.org/W2810983069","https://openalex.org/W2887019297","https://openalex.org/W2936400010","https://openalex.org/W2940665716","https://openalex.org/W2945235140","https://openalex.org/W3038433657","https://openalex.org/W4210702494"],"related_works":["https://openalex.org/W3122673936","https://openalex.org/W1987827786","https://openalex.org/W2799586942","https://openalex.org/W4367360465","https://openalex.org/W2504091800","https://openalex.org/W2331775400","https://openalex.org/W2816728186","https://openalex.org/W2804624249","https://openalex.org/W2570647323","https://openalex.org/W2560130217"],"abstract_inverted_index":{"Higher":[0],"educational":[1],"institutions":[2],"face":[3],"problems":[4],"with":[5],"increasing":[6],"student":[7],"dropout":[8,32],"rates.":[9],"Past":[10],"research":[11],"proposed":[12],"multiple":[13],"approaches,":[14],"such":[15],"as":[16],"data":[17,52,95],"mining,":[18],"to":[19,28,98,103],"solve":[20],"this":[21],"issue.":[22],"However,":[23],"the":[24,64,93,100,129,139,144,149,157,166],"prediction":[25],"model's":[26],"ability":[27],"determine":[29],"university":[30],"students'":[31],"rate":[33],"is":[34,59,66,75,138],"still":[35],"in":[36,96],"its":[37],"infancy.":[38],"We":[39],"explored":[40],"various":[41],"feature":[42],"selection":[43],"strategies":[44],"(filter,":[45],"wrapper":[46,167],"and":[47,61,123,159,162],"embedded)":[48],"on":[49,92],"postgraduate":[50],"studies":[51],"from":[53,105],"a":[54,106],"public":[55],"university.":[56],"The":[57,72,84,132],"dataset":[58],"imbalanced,":[60],"most":[62],"of":[63,153],"class":[65,74],"students":[67,77],"who":[68,78],"finished":[69],"their":[70,82],"studies.":[71,83],"minority":[73],"for":[76,128,143],"have":[79],"not":[80],"completed":[81],"study":[85],"applied":[86],"an":[87],"oversampling":[88],"method":[89,161,168],"named":[90],"SMOTE":[91],"imbalanced":[94],"training":[97],"enable":[99],"classification":[101,141],"algorithm":[102,142],"learn":[104],"balanced":[107],"dataset,":[108],"thus":[109],"minimising":[110],"overfitting.":[111],"Five":[112],"supervised":[113],"algorithms,":[114],"Decision":[115],"Tree,":[116],"Random":[117,136,146],"Forest,":[118],"Naive":[119],"Bayes,":[120],"Multi-Layer":[121],"Perceptron":[122],"Logistic":[124],"Regression,":[125],"were":[126],"evaluated":[127],"predictive":[130],"model.":[131],"result":[133],"shows":[134],"that":[135],"Forest":[137,147],"best":[140],"dataset.":[145],"produces":[148],"highest":[150],"accuracy":[151],"value":[152],"100%":[154],"when":[155,164,169],"using":[156,165,170],"filter":[158],"embedded":[160],"99.878%":[163],"test":[171],"data.":[172]},"counts_by_year":[],"updated_date":"2025-12-25T23:11:45.687758","created_date":"2025-10-10T00:00:00"}
