Penerapan Algoritma FP-Growth untuk Menentukan Tingkat Kelulusan Mahasiswa (Studi Kasus Teknik Informatika Universitas Muhammadiyah Malang)
| Main Author: | Alkarana, Fachrul Indo |
|---|---|
| Format: | Thesis NonPeerReviewed Book |
| Bahasa: | eng |
| Terbitan: |
, 2020
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| Subjects: | |
| Online Access: |
http://eprints.umm.ac.id/58426/1/pendahuluan.pdf http://eprints.umm.ac.id/58426/2/BAB%20I%20DOR.pdf http://eprints.umm.ac.id/58426/3/BAB%20II%20DOR.pdf http://eprints.umm.ac.id/58426/4/BAB%20III%20DOR.pdf http://eprints.umm.ac.id/58426/5/BAB%20IV%20DOR.pdf http://eprints.umm.ac.id/58426/6/BAB%20V%20DOR.pdf http://eprints.umm.ac.id/58426/7/poster%20sidang.pdf http://eprints.umm.ac.id/58426/ |
Daftar Isi:
- University of muhammadiyah malang has an increase in number of incoming students each year, and also occurs on the scale of the major in informatic engineering. But it has several academic problems that make the number of graduations is always less than admission in each years. This affects to academic activities within the departement, the departement’s profile, and the departement’s accreditation. Making a system that can predict student graduation will help lecturers and student to improve teaching and learning performance. Determine the level of graduation students, what processed with academic data using fp-growth algorithm. Graduation prediction patterns will be processed based on the graduation period, Grade Point Average (GPA), Grade Point Semester (GPS), and Semester Credit Unit (SCU) values. Frequent Growth Pattern work by determining the frequency of each item that appears, matching the support count and the frequent list, and build up the fp-tree. The suffix value is obtained, it used to reference value for processing new data. Accuracy and black box testing are done to evaluate the system. From 500 dataset, accuracy testing shows a match level of 90.50% on 4 and 5 support count value.