SISTEM PENDUKUNG KEPUTUSAN PEMILIHAN BIDANG MINAT MAHASISWA MENGGUNAKAN ALGORITMA KNN BERBASIS WEB (Studi Kasus : Program Studi Teknik Informatika Universitas Muhammadiyah Ponorogo)

GUNAWAN, MIYA PUTRI (2024) SISTEM PENDUKUNG KEPUTUSAN PEMILIHAN BIDANG MINAT MAHASISWA MENGGUNAKAN ALGORITMA KNN BERBASIS WEB (Studi Kasus : Program Studi Teknik Informatika Universitas Muhammadiyah Ponorogo). Skripsi (S1) thesis, Universitas Muhammadiyah Ponorogo.

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Abstract

Choosing the right field of interest in higher education plays a crucial role in determining students' career paths and professional development. In the context of the Informatics Engineering Study Program, students often face difficulties in choosing between three main fields of interest: Computer Networks, Internet of Things (IoT), and Software Engineering (SEE). To overcome this problem, a computer-based Decision Support System (DSS) was developed that utilizes the K-Nearest Neighbor (KNN) algorithm. This DSS is designed to provide recommendations for the right field of interest based on student transcript data, including Cumulative Grade Point Average (GPA), high school major grades, and course grades. This study uses 80 data sets with 40 training data consisting of 15 students in the Internet of Things field of interest, 7 students in Computer Networks and 18 students in Software Engineering who take each of these fields of interest and 40 test data, namely 19 students who are recommended for the Internet of Things field of interest, 3 students who are recommended for the Computer Network field of interest, and 18 students who are recommended for the Software Engineering field of interest. This study adopts a KNN-based approach for classification and regression, processing data to produce personalized recommendations. After testing 35 students, it was found that 16 students had a match between the fields of interest recommended by the system and those selected, while the other 19 students did not match due to differences in student choices with suggestions from the supervisor during the consultation on compiling the final assignment. These results show that the K-Nearest Neighbor algorithm can provide recommendations for relevant fields of interest based on academic data. However, the supervisor's suggestions also have a major influence on students' final decisions.

Item Type: Thesis (Skripsi (S1))
Uncontrolled Keywords: Selection of Interest Fields. K-Nearest Neighbor (K-NN) Algorithm, Cumulative Grade Point Average (GPA), High School Major Grades, Course Grades
Subjects: T Technology > T Technology (General)
Divisions: Faculty of Engineering > Department of Informatic Engineering
Depositing User: ft . userft
Date Deposited: 02 Sep 2024 07:24
Last Modified: 02 Sep 2024 07:24
URI: http://eprints.umpo.ac.id/id/eprint/14783

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