{"id":"https://openalex.org/W2594335349","doi":"https://doi.org/10.1109/iwbis.2016.7872890","title":"Predicting the status of water pumps using data mining approach","display_name":"Predicting the status of water pumps using data mining approach","publication_year":2016,"publication_date":"2016-10-01","ids":{"openalex":"https://openalex.org/W2594335349","doi":"https://doi.org/10.1109/iwbis.2016.7872890","mag":"2594335349"},"language":"en","primary_location":{"id":"doi:10.1109/iwbis.2016.7872890","is_oa":false,"landing_page_url":"https://doi.org/10.1109/iwbis.2016.7872890","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2016 International Workshop on Big Data and Information Security (IWBIS)","raw_type":"proceedings-article"},"type":"conference-paper","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/A5017849246","display_name":"Darmatasia","orcid":null},"institutions":[{"id":"https://openalex.org/I29617571","display_name":"University of Indonesia","ror":"https://ror.org/0116zj450","country_code":"ID","type":"education","lineage":["https://openalex.org/I29617571"]}],"countries":["ID"],"is_corresponding":false,"raw_author_name":"Darmatasia","raw_affiliation_strings":["Faculty of Computer Science, University of Indonesia, Depok, West Java, Indonesia"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Faculty of Computer Science, University of Indonesia, Depok, West Java, Indonesia","institution_ids":["https://openalex.org/I29617571"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5039645915","display_name":"Aniati Murni Arymurthy","orcid":"https://orcid.org/0000-0002-2762-7001"},"institutions":[{"id":"https://openalex.org/I29617571","display_name":"University of Indonesia","ror":"https://ror.org/0116zj450","country_code":"ID","type":"education","lineage":["https://openalex.org/I29617571"]}],"countries":["ID"],"is_corresponding":false,"raw_author_name":"Aniati Murni Arymurthy","raw_affiliation_strings":["Faculty of Computer Science, University of Indonesia, Depok, West Java, Indonesia"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Faculty of Computer Science, University of Indonesia, Depok, West Java, Indonesia","institution_ids":["https://openalex.org/I29617571"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I29617571"],"apc_list":null,"apc_paid":null,"fwci":0.3883,"has_fulltext":false,"cited_by_count":7,"citation_normalized_percentile":{"value":0.64632155,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":96},"biblio":{"volume":null,"issue":null,"first_page":"57","last_page":"64"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11652","display_name":"Imbalanced Data Classification Techniques","score":0.9904000163078308,"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":0.9904000163078308,"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":0.9902999997138977,"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/T11220","display_name":"Water Systems and Optimization","score":0.9891999959945679,"subfield":{"id":"https://openalex.org/subfields/2205","display_name":"Civil and Structural Engineering"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.7130204439163208},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.705950140953064},{"id":"https://openalex.org/keywords/decision-tree","display_name":"Decision tree","score":0.5931831002235413},{"id":"https://openalex.org/keywords/boosting","display_name":"Boosting (machine learning)","score":0.5930321216583252},{"id":"https://openalex.org/keywords/gradient-boosting","display_name":"Gradient boosting","score":0.5820637345314026},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.3687945604324341},{"id":"https://openalex.org/keywords/random-forest","display_name":"Random forest","score":0.13885152339935303}],"concepts":[{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.7130204439163208},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.705950140953064},{"id":"https://openalex.org/C84525736","wikidata":"https://www.wikidata.org/wiki/Q831366","display_name":"Decision tree","level":2,"score":0.5931831002235413},{"id":"https://openalex.org/C46686674","wikidata":"https://www.wikidata.org/wiki/Q466303","display_name":"Boosting (machine learning)","level":2,"score":0.5930321216583252},{"id":"https://openalex.org/C70153297","wikidata":"https://www.wikidata.org/wiki/Q5591907","display_name":"Gradient boosting","level":3,"score":0.5820637345314026},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3687945604324341},{"id":"https://openalex.org/C169258074","wikidata":"https://www.wikidata.org/wiki/Q245748","display_name":"Random forest","level":2,"score":0.13885152339935303}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/iwbis.2016.7872890","is_oa":false,"landing_page_url":"https://doi.org/10.1109/iwbis.2016.7872890","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2016 International Workshop on Big Data and Information Security (IWBIS)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"display_name":"Clean water and sanitation","id":"https://metadata.un.org/sdg/6","score":0.6800000071525574}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":10,"referenced_works":["https://openalex.org/W1975107556","https://openalex.org/W1977130378","https://openalex.org/W1995017913","https://openalex.org/W2037536778","https://openalex.org/W2071917018","https://openalex.org/W2247602019","https://openalex.org/W2295598076","https://openalex.org/W3102476541","https://openalex.org/W6643913003","https://openalex.org/W7053126446"],"related_works":["https://openalex.org/W2967733078","https://openalex.org/W3204430031","https://openalex.org/W3137904399","https://openalex.org/W4310492845","https://openalex.org/W2885778889","https://openalex.org/W2766514146","https://openalex.org/W2885516856","https://openalex.org/W4289703016","https://openalex.org/W4310224730","https://openalex.org/W1985505753"],"abstract_inverted_index":{"Data":[0,21],"mining":[1,22,54,137,174],"approach":[2,23,138,175],"can":[3,139,156],"be":[4,73,140],"used":[5,25,40,141],"to":[6,26,41,72,90,98,145,159,163],"discover":[7],"knowledge":[8,131],"by":[9,113,142],"analyzing":[10],"the":[11,28,31,64,92,96,143,147,160,165],"patterns":[12,29],"or":[13,130],"correlations":[14],"among":[15],"of":[16,30,36,47,66,95,167],"fields":[17],"in":[18,50,126,170],"large":[19],"databases.":[20],"was":[24],"find":[27],"data":[32,53,97,136,173],"from":[33,135],"Tanzania":[34],"Ministry":[35],"Water.":[37],"It":[38],"is":[39,57,87,133,176],"predict":[42],"current":[43],"and":[44,79,115,151,181],"future":[45],"status":[46],"water":[48,161,169],"pumps":[49,162],"Tanzania.":[51,171],"The":[52,103,121,128],"method":[55],"proposed":[56,89],"XGBoost":[58,62,116],"(eXtreme":[59],"Gradient":[60,67],"Boosting).":[61],"implement":[63],"concept":[65],"Tree":[68],"Boosting":[69],"which":[70,132,153],"designed":[71],"highly":[74],"fast,":[75],"accurate,":[76],"efficient,":[77],"flexible,":[78],"portable.":[80],"In":[81],"addition,":[82],"Recursive":[83],"Feature":[84],"Elimination":[85],"(RFE)":[86],"also":[88],"select":[91],"important":[93],"features":[94],"obtain":[99],"an":[100],"accurate":[101],"model.":[102,120],"best":[104],"accuracy":[105],"achieved":[106,122],"with":[107],"using":[108],"27":[109],"input":[110],"factors":[111],"selected":[112],"RFE":[114],"as":[117],"a":[118],"learning":[119],"result":[123],"show":[124],"80.38%":[125],"accuracy.":[127],"information":[129],"discovered":[134],"government":[144],"improve":[146],"inspection":[148],"planning,":[149],"maintenance,":[150],"identify":[152],"factor":[154],"that":[155],"cause":[157],"damage":[158],"ensure":[164],"availability":[166],"potable":[168],"Using":[172],"cost-effective,":[177],"less":[178],"time":[179],"consuming":[180],"faster":[182],"than":[183],"manual":[184],"inspection.":[185]},"counts_by_year":[{"year":2025,"cited_by_count":1},{"year":2024,"cited_by_count":1},{"year":2022,"cited_by_count":1},{"year":2020,"cited_by_count":2},{"year":2019,"cited_by_count":1},{"year":2017,"cited_by_count":1}],"updated_date":"2026-07-29T14:22:42.915294","created_date":"2025-10-10T00:00:00"}