PENERAPAN TEKNIK DATA MINING DALAM PREDIKSI TINGKAT INDEKS PRESTASI KUMULATIF MAHASISWA MENGGUNAKAN METODE KLASIFIKASI (STUDI KASUS : UNIVERSITAS SRIWIJAYA)

HANDAYANI, ERIKA and Tania, Ken Ditha (2021) PENERAPAN TEKNIK DATA MINING DALAM PREDIKSI TINGKAT INDEKS PRESTASI KUMULATIF MAHASISWA MENGGUNAKAN METODE KLASIFIKASI (STUDI KASUS : UNIVERSITAS SRIWIJAYA). Undergraduate thesis, Sriwijaya University.

	Text RAMA_57201_09031181722007.pdf - Accepted Version Restricted to Repository staff only Available under License Creative Commons Public Domain Dedication. Download (7MB) | Request a copy
	Text RAMA_57201_09031181722007_TURNITIN.pdf - Accepted Version Restricted to Repository staff only Available under License Creative Commons Public Domain Dedication. Download (6MB) | Request a copy
Preview	Text RAMA_57201_09031181722007_0018078502_01_front_ref.pdf - Accepted Version Available under License Creative Commons Public Domain Dedication. Download (6MB) | Preview
	Text RAMA_57201_09031181722007_0018078502_02.pdf - Accepted Version Restricted to Repository staff only Available under License Creative Commons Public Domain Dedication. Download (388kB) | Request a copy
	Text RAMA_57201_09031181722007_0018078502_03.pdf - Accepted Version Restricted to Repository staff only Available under License Creative Commons Public Domain Dedication. Download (1MB) | Request a copy
	Text RAMA_57201_09031181722007_0018078502_04.pdf - Accepted Version Restricted to Repository staff only Available under License Creative Commons Public Domain Dedication. Download (807kB) | Request a copy
	Text RAMA_57201_09031181722007_0018078502_05.pdf - Accepted Version Restricted to Repository staff only Available under License Creative Commons Public Domain Dedication. Download (86kB) | Request a copy
	Text RAMA_57201_09031181722007_0018078502_06_ref.pdf - Bibliography Restricted to Repository staff only Available under License Creative Commons Public Domain Dedication. Download (112kB) | Request a copy
	Text RAMA_57201_09031181722007_0018078502_07_lamp.pdf - Accepted Version Restricted to Repository staff only Available under License Creative Commons Public Domain Dedication. Download (3MB) | Request a copy

Abstract

The grade point average or, abbreviated as GPA is the average value of student learning outcomes during the lecture period. GPA is used as an indicator of student success and is used as one of the requirements proposed by a company when recruiting workers. This study aims to predict the level of student GPA based on the competencies mastered by alumni when they graduate which has a relationship with the GPA level and to design a web-based system that can predict student’s GPA levels using the classification method. The method used is CRISP-DM. The data used is tracer study 2019 as many as 3,906 records. With a significant level of 1% (0.01) it was found that the GPA level had a positive correlation with the variables of the study program, gender, knowledge in the field or discipline, knowledge outside the field or discipline, general knowledge, internet skills, critical thinking, learning skills, communication skills, working under pressure, time management, team work. In this study using 10-fold cross validation with accuracy results in the decision tree algorithm of 68.78%, the K-NN algorithm of 69.30%, the Naive Bayes Classifier algorithm of 71.17% and the Random Forest algorithm of 68.75%. . After that, a T-Test was carried out so that the Naive Bayes Classifer algorithm was obtained as the most dominant algorithm among the other three algorithms so that it could classify and predict the GPA level well.

Item Type:	Thesis (Undergraduate)
Uncontrolled Keywords:	Data mining, Classification Method, CRISP-DM, Decision Tree, K-NN, Naive Bayes Classifier, Random Forest, Prediction of GPA Level
Subjects:	Q Science > Q Science (General) > Q334-342 Computer science. Artificial intelligence. Algorithms. Robotics. Automation.
Divisions:	09-Faculty of Computer Science > 57201-Information Systems (S1)
Depositing User:	Erika Handayani
Date Deposited:	22 Sep 2021 04:14
Last Modified:	22 Sep 2021 04:14
URI:	http://repository.unsri.ac.id/id/eprint/54436

Actions (login required)

View Item