Hayong, Emanuel Amstrong (2024) IMPLEMENTASI ALGORITME K-MEANS CLUSTERING DAN K-NEAREST NEIGHBOR (KNN) DALAM PEMILIHAN PROGRAM STUDI MAHASISWA BARU. Undergraduate thesis, Universitas Katolik Darma Cendika Fakultas Teknik.
![[img]](http://repositori.ukdc.ac.id/style/images/fileicons/text.png) |
Text (ABSTRAK)
Abstrak.pdf Download (4MB) |
|
Text (BAB 1)
BAB I.pdf Download (2MB) |
|
Text (BAB 2)
BAB II.pdf Restricted to Registered users only Download (5MB) | Request a copy |
|
Text (BAB 3)
BAB III.pdf Restricted to Registered users only Download (4MB) | Request a copy |
|
Text (BAB 4)
BAB IV.pdf Restricted to Repository staff only Download (6MB) | Request a copy |
|
Text (BAB 5 dan DAFTAR PUSTAKA)
BAB V.pdf Download (1MB) |
|
Text (LAMPIRAN)
Lampiran.pdf Restricted to Repository staff only Download (903kB) | Request a copy |
Abstract
Specialization aims to provide students with the opportunity to thoroughly study and understand a specific field in depth and focus. Additionally, during academic studies, specialization also aims to prepare students to enter the workforce and pursue a career in a field that aligns with their interests and talents. If students make a wrong choice in selecting their major, interest in learning may wane, leading to a lack of motivation to attend classes, making all assignments feel burdensome. To address this challenge, the author develops a Clustering and Classification-based system by implementing the K-Means and K-NN algorithms in selecting the study programs for new students. The evaluation of test results indicates an accuracy rate of 91.11%, demonstrating high consistency in grouping and classifying students. These classification results are then used as a guide for new students to determine a study program that aligns with their interests and talents. With a focus on fields of study such as "Acupuncture," "Architecture," "Management," and others, this research contributes to an understanding of the importance of proper specialization in achieving academic success and career development for students.
| Item Type: | Thesis (Undergraduate) | ||||||||
|---|---|---|---|---|---|---|---|---|---|
| Contributors: |
|
||||||||
| Uncontrolled Keywords: | New Students, K-Means, K-Nearest Neighbors | ||||||||
| Subjects: | T Technology > T Technology (General) | ||||||||
| Divisions: | Fakultas Teknik > Prodi Ilmu Informatika | ||||||||
| Depositing User: | EMANUEL AMSTRONG HAYONG | ||||||||
| Date Deposited: | 08 Mar 2024 09:04 | ||||||||
| Last Modified: | 08 Mar 2024 09:04 | ||||||||
| URI: | http://repositori.ukdc.ac.id/id/eprint/1665 |
Actions (login required)
 |
View Item |
