PENERAPAN ALGORITMA MACHINE LEARNING UNTUK PENJURUSAN SISWA BARU SEKOLAH MENENGAH KEJURUSAN BERDASARKAN NILAI RAPORT DAN PSIKOTEST (Studi Kasus : SMK Telkom Jakarta)

MAULANA, GIAN (2023) PENERAPAN ALGORITMA MACHINE LEARNING UNTUK PENJURUSAN SISWA BARU SEKOLAH MENENGAH KEJURUSAN BERDASARKAN NILAI RAPORT DAN PSIKOTEST (Studi Kasus : SMK Telkom Jakarta). S1 thesis, Universitas Mercu Buana Jakarta.

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Abstract

Telkom Jakarta Vocational High School is one of the schools under the auspices of the Telkom Sandhykara Putra Foundation. It was founded in 1992 as Telkom Sandhy Putra Jakarta Technical Middle School (STM). The school implements a major system manually through the calculation of report cards, independent tests, and psychological tests, which can take a long time to determine the majors of students who have been officially admitted. This study uses four algorithms to determine student majors: K-Nearest Neighbor, Naive Bayes, Support Vector Machine, and Random Forest. Research shows that these four algorithms have a low level of accuracy, with an average below 40%. However, when comparing the accuracy of the algorithms, the Random Forest algorithm has greater accuracy than the others. Keywords: Major, Algorithm, Accuracy, Comparing Sekolah Menengah Kejuruan Telkom Jakarta adalah salah satu sekolah yang berada dalam naungan Yayasan Sandhykara Putra Telkom. Sekolah ini didirikan pada tahun 1992 dengan nama Sekolah Teknik Menengah (STM) Telkom Sandhy Putra Jakarta. SMK Telkom Jakarta menerapkan sistem penjurusan secara manual melalui perhitungan nilai raport, test mandiri, dan psikotes, sehingga dapat memakan waktu yang cukup lama untuk menentukan penjurusan siswa yang telah resmi diterima masuk untuk menjadi siswa SMK Telkom Jakarta. Penelitian ini menggunakan empat algoritma untuk menentukan penjurusan siswa, yakni K-Nearest Neighbor, Naive Bayes, Support Vector Machine, dan Random Forest. Berdasarkan penelitian, keempat algoritma tersebut memiliki tingkat akurasi yang rendah dengan akurasi rata-rata dibawah 40%. Namun jika dilihat perbandingan akurasi dari keempat algoritma tersebut, algoritma Random Forest memiliki akurasi yang lebih besar dibandingkan algoritma yang lainnya. Kata Kunci : Penjurusan, , Algoritma, Akurasi, Perbandingan

Item Type: Thesis (S1)
Call Number CD: FIK/INFO. 23 021
Call Number: SIK/15/23/031
NIM/NIDN Creators: 41519010028
Uncontrolled Keywords: Penjurusan, , Algoritma, Akurasi, Perbandingan
Subjects: 000 Computer Science, Information and General Works/Ilmu Komputer, Informasi, dan Karya Umum > 000. Computer Science, Information and General Works/Ilmu Komputer, Informasi, dan Karya Umum
000 Computer Science, Information and General Works/Ilmu Komputer, Informasi, dan Karya Umum > 000. Computer Science, Information and General Works/Ilmu Komputer, Informasi, dan Karya Umum > 004 Data Processing, Computer Science/Pemrosesan Data, Ilmu Komputer, Teknik Informatika
000 Computer Science, Information and General Works/Ilmu Komputer, Informasi, dan Karya Umum > 000. Computer Science, Information and General Works/Ilmu Komputer, Informasi, dan Karya Umum > 006 Special Computer Methods/Metode Komputer Tertentu > 006.3 Artificial Intelligence/Kecerdasan Buatan > 006.31 Machine Learning/Pembelajaran Mesin
500 Natural Science and Mathematics/Ilmu-ilmu Alam dan Matematika > 510 Mathematics/Matematika > 518 Numerical Analysis/Analisis Numerik, Analisa Numerik > 518.1 Algorithms/Algoritma
Divisions: Fakultas Ilmu Komputer > Informatika
Depositing User: ADELINA HASNA SETIAWATI
Date Deposited: 01 Apr 2023 06:00
Last Modified: 01 Apr 2023 06:00
URI: http://repository.mercubuana.ac.id/id/eprint/75835

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