Skripsi

PENERAPAN TEKNIK DATA MINING ALGORITMA C4.5 UNTUK PREDIKSI KESESUAIAN TINGKAT PENDIDIKAN DENGAN BIDANG PEKERJAAN PADA ALUMNI UNIVERSITAS SRIWIJAYA

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Penilaian

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Universitas Sriwijaya has a talent and career development center (CDC) unit for alumni. The CDC website provided a questionnaire form for alumni to fill out. The features on the questionnaire had relevance regarding the level of education of alumni with their job such as lectures, research projects, internships, practicum, field work, discussions, time to get a job, fields of science, outside the field of science, English, internet, computers, and the relationship between study program and work. Based on data on the CDC from 2013 to 2015, there were 1143 alumni of Sriwijaya University who filled out the questionnaire form completely, but only 1019 alumni who had jobs. The selection of attributes was done by calculating using the C4.5 algorithm . This study used the k-fold cross-validation confusion matrix method to see the success rate of the C4.5 algorithm. The result of accuracy in the testing process using confusion matrix was 92% for the decision tree method using the C4.5 algorithm. The best features selected were 9 features, namely the level of learning in the discussion, the level of competence outside the field of science, the level of learning in field work, the level of competency in English skills, the level of learning in internships, the level of learning in practicum, the level of competence in the field of science, the level of learning in research projects, as well as the time to get a job. Based on the accuracy obtained, it can be concluded that the C4.5 algorithm worked very well in the feature selection process and improved accuracy.

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Availability

Inventory Code	Barcode	Call Number	Location	Status
2007001812	T43374	T433742020	Central Library (REFERENCES)	Available but not for loan - Not for Loan

Detail Information

Series Title

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Call Number

T433742020

Publisher

Inderalaya : Jurusan Matematika, Fakultas Matematika Dan Ilmu Pengetahuan Alam Universitas Sriwijaya., 2020

Collation

xii, 85 hlm. : ilus. ; 28 cm

Language

Indonesia

ISBN/ISSN

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Classification

518.107

Content Type

Text

Media Type

unmediated

Carrier Type

-

Edition

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Subject(s)

Jurusan Matematika
Teknik Data Mining Algoritma

Specific Detail Info

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Statement of Responsibility

DS

Other version/related

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