{"id":"https://openalex.org/W4281617896","doi":"https://doi.org/10.1145/3524304.3524337","title":"Revenue Prediction for Malaysian Federal Government Using Machine Learning Technique","display_name":"Revenue Prediction for Malaysian Federal Government Using Machine Learning Technique","publication_year":2022,"publication_date":"2022-02-24","ids":{"openalex":"https://openalex.org/W4281617896","doi":"https://doi.org/10.1145/3524304.3524337"},"language":"en","primary_location":{"id":"doi:10.1145/3524304.3524337","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3524304.3524337","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2022 11th International Conference on Software and Computer Applications","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/A5060124699","display_name":"Nadzira Noor","orcid":null},"institutions":[{"id":"https://openalex.org/I203899302","display_name":"Universiti Teknologi Petronas","ror":"https://ror.org/048g2sh07","country_code":"MY","type":"education","lineage":["https://openalex.org/I203899302"]}],"countries":["MY"],"is_corresponding":true,"raw_author_name":"Nadzira Noor","raw_affiliation_strings":["Department of Computer and Information Sciences, Universiti Teknologi Petronas (UTP), Malaysia"],"affiliations":[{"raw_affiliation_string":"Department of Computer and Information Sciences, Universiti Teknologi Petronas (UTP), Malaysia","institution_ids":["https://openalex.org/I203899302"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5019616714","display_name":"Aliza Sarlan","orcid":"https://orcid.org/0000-0002-6342-9993"},"institutions":[{"id":"https://openalex.org/I203899302","display_name":"Universiti Teknologi Petronas","ror":"https://ror.org/048g2sh07","country_code":"MY","type":"education","lineage":["https://openalex.org/I203899302"]}],"countries":["MY"],"is_corresponding":false,"raw_author_name":"Aliza Sarlan","raw_affiliation_strings":["Department of Computer and Information Sciences, Universiti Teknologi Petronas (UTP), Malaysia"],"affiliations":[{"raw_affiliation_string":"Department of Computer and Information Sciences, Universiti Teknologi Petronas (UTP), Malaysia","institution_ids":["https://openalex.org/I203899302"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5028915945","display_name":"Norshakirah Aziz","orcid":"https://orcid.org/0000-0001-5563-0286"},"institutions":[{"id":"https://openalex.org/I203899302","display_name":"Universiti Teknologi Petronas","ror":"https://ror.org/048g2sh07","country_code":"MY","type":"education","lineage":["https://openalex.org/I203899302"]}],"countries":["MY"],"is_corresponding":false,"raw_author_name":"Norshakirah Aziz","raw_affiliation_strings":["Department of Computer and Information Sciences, Universiti Teknologi Petronas (UTP), Malaysia"],"affiliations":[{"raw_affiliation_string":"Department of Computer and Information Sciences, Universiti Teknologi Petronas (UTP), Malaysia","institution_ids":["https://openalex.org/I203899302"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5060124699"],"corresponding_institution_ids":["https://openalex.org/I203899302"],"apc_list":null,"apc_paid":null,"fwci":1.515,"has_fulltext":false,"cited_by_count":5,"citation_normalized_percentile":{"value":0.85830129,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":90,"max":96},"biblio":{"volume":null,"issue":null,"first_page":"143","last_page":"148"},"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":0.9785000085830688,"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":0.9785000085830688,"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/T11326","display_name":"Stock Market Forecasting Methods","score":0.9776999950408936,"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"}},{"id":"https://openalex.org/T11918","display_name":"Forecasting Techniques and Applications","score":0.9674999713897705,"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/revenue","display_name":"Revenue","score":0.8022646307945251},{"id":"https://openalex.org/keywords/random-forest","display_name":"Random forest","score":0.6142900586128235},{"id":"https://openalex.org/keywords/revenue-assurance","display_name":"Revenue assurance","score":0.5583171248435974},{"id":"https://openalex.org/keywords/government-revenue","display_name":"Government revenue","score":0.5217353105545044},{"id":"https://openalex.org/keywords/government","display_name":"Government (linguistics)","score":0.47463950514793396},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.46427056193351746},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.4548341929912567},{"id":"https://openalex.org/keywords/linear-regression","display_name":"Linear regression","score":0.4424588680267334},{"id":"https://openalex.org/keywords/finance","display_name":"Finance","score":0.3729982078075409},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.36732369661331177},{"id":"https://openalex.org/keywords/business","display_name":"Business","score":0.36397117376327515},{"id":"https://openalex.org/keywords/revenue-model","display_name":"Revenue model","score":0.3328555226325989}],"concepts":[{"id":"https://openalex.org/C195487862","wikidata":"https://www.wikidata.org/wiki/Q850210","display_name":"Revenue","level":2,"score":0.8022646307945251},{"id":"https://openalex.org/C169258074","wikidata":"https://www.wikidata.org/wiki/Q245748","display_name":"Random forest","level":2,"score":0.6142900586128235},{"id":"https://openalex.org/C158561033","wikidata":"https://www.wikidata.org/wiki/Q7318015","display_name":"Revenue assurance","level":4,"score":0.5583171248435974},{"id":"https://openalex.org/C193991484","wikidata":"https://www.wikidata.org/wiki/Q2509483","display_name":"Government revenue","level":3,"score":0.5217353105545044},{"id":"https://openalex.org/C2778137410","wikidata":"https://www.wikidata.org/wiki/Q2732820","display_name":"Government (linguistics)","level":2,"score":0.47463950514793396},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.46427056193351746},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.4548341929912567},{"id":"https://openalex.org/C48921125","wikidata":"https://www.wikidata.org/wiki/Q10861030","display_name":"Linear regression","level":2,"score":0.4424588680267334},{"id":"https://openalex.org/C10138342","wikidata":"https://www.wikidata.org/wiki/Q43015","display_name":"Finance","level":1,"score":0.3729982078075409},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.36732369661331177},{"id":"https://openalex.org/C144133560","wikidata":"https://www.wikidata.org/wiki/Q4830453","display_name":"Business","level":0,"score":0.36397117376327515},{"id":"https://openalex.org/C91887955","wikidata":"https://www.wikidata.org/wiki/Q5362092","display_name":"Revenue model","level":3,"score":0.3328555226325989},{"id":"https://openalex.org/C138885662","wikidata":"https://www.wikidata.org/wiki/Q5891","display_name":"Philosophy","level":0,"score":0.0},{"id":"https://openalex.org/C41895202","wikidata":"https://www.wikidata.org/wiki/Q8162","display_name":"Linguistics","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3524304.3524337","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3524304.3524337","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2022 11th International Conference on Software and Computer Applications","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":26,"referenced_works":["https://openalex.org/W2386865585","https://openalex.org/W2529936865","https://openalex.org/W2606127768","https://openalex.org/W2610084457","https://openalex.org/W2687870606","https://openalex.org/W2726150830","https://openalex.org/W2735307196","https://openalex.org/W2735577828","https://openalex.org/W2766787778","https://openalex.org/W2771467642","https://openalex.org/W2787398400","https://openalex.org/W2889477424","https://openalex.org/W2896590301","https://openalex.org/W2902284010","https://openalex.org/W2909635048","https://openalex.org/W2922541078","https://openalex.org/W2927428734","https://openalex.org/W2933106961","https://openalex.org/W2941320143","https://openalex.org/W2946728202","https://openalex.org/W2949678561","https://openalex.org/W3097282970","https://openalex.org/W3108547760","https://openalex.org/W3198656709","https://openalex.org/W4237534115","https://openalex.org/W4401047942"],"related_works":["https://openalex.org/W2384280384","https://openalex.org/W172659435","https://openalex.org/W2383943304","https://openalex.org/W3122333652","https://openalex.org/W3209712126","https://openalex.org/W2765340130","https://openalex.org/W3170868543","https://openalex.org/W2983891759","https://openalex.org/W2062234925","https://openalex.org/W2332634355"],"abstract_inverted_index":{"Every":[0],"country":[1],"has":[2,27],"its":[3,12],"own":[4,13],"federal":[5,8,59,158],"government.":[6],"Each":[7],"government":[9,60,159],"will":[10,46],"have":[11],"financial":[14],"account":[15],"which":[16],"consist":[17],"of":[18],"revenue":[19,40,45,61,160],"and":[20,41,52,109],"expenditure.":[21],"Focusing":[22],"on":[23,99,156],"the":[24,58,88,96,115,157],"revenue,":[25,38],"it":[26,73,122,138,145],"many":[28,83],"sources":[29],"that":[30,125],"includes":[31],"three":[32],"main":[33],"categories.":[34],"They":[35],"are":[36,82],"tax":[37],"non-tax":[39],"non-revenue":[42],"receipts.":[43],"The":[44],"then":[47],"be":[48,75,92,114,154],"used":[49,155],"for":[50,135],"operational":[51],"development":[53],"purposes.":[54],"Currently":[55],"in":[56],"Malaysia,":[57],"is":[62,123,146],"only":[63],"using":[64,77],"forecasting.":[65],"This":[66],"can":[67,74,91],"cause":[68],"large":[69],"forecasting":[70],"error.":[71],"Though":[72],"overcome":[76],"predictive":[78],"analytics.":[79],"Since":[80],"there":[81],"machine":[84],"learning":[85],"methods":[86,90],"available,":[87],"appropriate":[89],"identified":[93],"to":[94,113,153],"do":[95],"prediction.":[97],"Based":[98],"previous":[100],"research,":[101],"feed":[102],"forward":[103],"neural":[104],"network":[105],"(FFNN),":[106],"random":[107,132],"forest":[108],"linear":[110,136],"regression":[111],"seems":[112],"most":[116],"suitable.":[117],"After":[118],"conducting":[119],"several":[120],"experiments,":[121],"found":[124],"FFNN":[126],"achieved":[127],"highest":[128],"accuracy,":[129,143],"followed":[130],"by":[131],"forest.":[133],"As":[134],"regression,":[137],"does":[139],"not":[140,150],"achieve":[141],"good":[142],"thus":[144],"considered":[147],"as":[148],"a":[149],"suitable":[151],"method":[152],"dataset.":[161]},"counts_by_year":[{"year":2025,"cited_by_count":2},{"year":2024,"cited_by_count":1},{"year":2023,"cited_by_count":2}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}