Optimalisasi Metode Naive Bayes untuk Menentukan Program Studi bagi Calon Mahasiswa Baru dengan Pendekatan Unsupervised Discretization
Abstract views: 121
,
PDF downloads: 184
Abstract
The admission of prospective new students must consider various procedures to direct prospective new students in determining the study program they are interested in. This study will discuss the optimization of the Naive Bayes method to determine the study program or major for prospective new students with the Unsupervised Discritization method approach. There are several stages of research methods carried out in this study, including Data Cleaning, Data Collection, Criteria Determination, Probability Determination, and Data Testing. This research has been carried out using the same method, namely the Naïve Bayes method which is used to classify the interests of prospective new students in determining the study program with an accuracy value of 96.68%. Ongoing research uses the same method, namely Naive Bayes, then optimization is carried out with the Unsupervised Discretization method approach. For data testing, there are 1671 student data records. After testing with the same method and optimizing it, the accuracy value from 96.68% became 97.66% with the classification results showing the DIII Pharmacy study program. The purpose of this research is to produce a classification in determining the study program or major for prospective new students using the Naïve Bayes method by the optimization of the Unsupervised Discretization method. From the results of testing the data, the Naïve Bayes method after optimization with the Unsupervised Discretization method is very good compared to the application before optimization.
References
M.S.Sungkar,“ANALISISMINATMAHASISWAMEMASUKIPROGRAMSTUDITEKNIKDENGANMENGGUNAKANMETODEK-MEANSCLUSTERINGDIPOLITEKNIKHARAPANBERSAMA,”J.Chem.Inf.Model.,vol.21,no.1,pp.1–9,2020,[Online].Available:https://doi.org/10.1016/j.tmaid.2020.101607%0Ahttps://doi.org/10.1016/j.ijsu.2020.02.034%0Ahttps://onlinelibrary.wiley.com/doi/abs/10.1111/cjag.12228%0Ahttps://doi.org/10.1016/j.ssci.2020.104773%0Ahttps://doi.org/10.1016/j.jinf.2020.04.011%0Ahttps://doi.o.[2]C.Gonzalez-Arias,C.C.Viafara,J.J.Coronado,andF.Martinez,“Automaticclassificationofsevereandmildwearinwornsurfaceimagesusinghistogramsoforientedgradientsasdescriptor,”Wear,vol.426–427,no.November2018,pp.1702–1711,2019,doi:10.1016/j.wear.2018.11.028.[3]M.AndrejiovaandA.Grincova,“Classificationofimpactdamageonarubber-textileconveyorbeltusingNaïve-Bayesmethodology,”Wear,vol.414–415,pp.59–67,2018,doi:10.1016/j.wear.2018.08.001.[4]A.Saleh,F.Nasari,U.P.Utama,andK.J.Siswa,“PENGGUNAANTEKNIKUNSUPERVISEDDISCRETIZATIONPADAMETODENAIVEBAYESDALAMMENENTUKANJURUSANSISWAAPPLIEDOFUNSUPERVISEDDISCRETIZATIONTECHNIQUEINNAIVEBAYESMETHODINDETERMININGMADRASAHALIYAHSTUDENTS’MAJOR,”vol.5,no.3,pp.353–360,2018,doi:10.25126/jtiik.201853705.[5]M.A.AlandM.Lirboyo,“Programstudisisteminformasifakultasteknikuniversitasnusantarapgrikediri2017,”2017.[6]T.Wong,“¨veBayesianclassifiersAhybriddiscretizationmethodfornaı,”PatternRecognit.,vol.45,no.6,pp.2321–2325,2012,doi:10.1016/j.patcog.2011.12.014.[7]K.A.K.Niazi,W.Akhtar,H.A.Khan,Y.Yang,andS.Athar,“HotspotdiagnosisforsolarphotovoltaicmodulesusingaNaiveBayesclassifier,”Sol.Energy,vol.190,no.July,pp.34–43,2019,doi:10.1016/j.solener.2019.07.063.[8]J.Sainsetal.,“KlasifikasiBeritaIndonesiaMenggunakanMetodeNaiveBayesianClassificationdanSupportVectorMachinedenganConfixStrippingStemmer,”vol.4,no.2,2015.[9]S.U.Khan,M.Niazi,andR.Ahmad,“Factorsinfluencingclientsintheselectionofoffshoresoftwareoutsourcingvendors:Anexploratorystudyusingasystematicliteraturereview,”J.Syst.Softw.,vol.84,no.4,pp.686–699,Apr.2011,doi:10.1016/j.jss.2010.12.010.[10]T.PrawiraandD.Kusuma,“SistemPendukungKeputusanBerbasisWebuntukMenentukanPenjurusan(IPA/IPS/Bahasa)padaSMAIslamBumiayu,”Juita,vol.I,pp.177–189,2011.[11]W.E.Nugroho,A.Sofyan,andO.Somantri,“MetodeNaiveBayesDalamMenentukanProgramStudiBagiCalonMahasiswaBaru,”vol.12,no.01,pp.59–64,2021,doi:10.35970/infotekmesin.v12i1.491.[12]A.Saleh,“KlasifikasiMetodeNaiveBayesDalamDataMiningUntukMenentukanKonsentrasiSiswa,”KeTIK,pp.200–208,2015.[13]J.Wu,S.Pan,Z.Cai,X.Zhu,andC.Zhang,“DualinstanceandattributeweightingforNaiveBayesclassification,”Proc.Int.Jt.Conf.NeuralNetworks,no.1994,pp.1675–1679,2014,doi:10.1109/IJCNN.2014.6889572.[14]D.Mondal,D.K.Kole,andK.Roy,“GradationofyellowmosaicvirusdiseaseofokraandbittergourdbasedonentropybasedbinningandNaiveBayesclassifierafteridentificationofleaves,”Comput.Electron.Agric.,vol.142,no.October,pp.485–493,2017,doi:10.1016/j.compag.2017.11.024.[15]A.H.Mirza,“ApplicationofNaiveBayesClassifierAlgorithminDeterminingNewStudentAdmissionPromotionStrategies,”J.Inf.Syst.Informatics,vol.1,no.1,pp.14–28,2019,doi:10.33557/journalisi.v1i1.2.[16]J.J.Christopher,“UnsupervisedDiscretization:AnAnalysisofClassificationApproachesforClinicalDatasetsUnsupervisedDiscretization:AnAnalysisofClassificationApproachesforClinicalDatasets,”no.February,2017,doi:10.19026/rjaset.14.3991.
Copyright (c) 2022 Infotekmesin
This work is licensed under a Creative Commons Attribution 4.0 International License.
Authors who publish with this journal agree to the following terms:
- Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under a Creative Commons Attribution License that allows others to share the work with an acknowledgement of the work's authorship and initial publication in this journal.
- Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgement of its initial publication in this journal.
- Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work (See The Effect of Open Access).
