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All Journal Jurnal Ilmiah Informatika Komputer
Tiara Ratu Alifia
Sekolah Tinggi Ilmu Komputer Cipta Karya Informatika (STIKOM CKI)
Computer Science & IT Decision Sciences, Operations Research & Management

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MODEL ALGORITMA KNN UNTUK PREDIKSI KELULUSAN MAHASISWA STIKOM CKI Tiara Ratu Alifia; Tundo Tundo; Muhammad Syazidan; Faldo Satria
Jurnal Ilmiah Informatika Komputer Vol 29, No 2 (2024)
Publisher : Universitas Gunadarma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35760/ik.2024.v29i2.11803

Abstract

This study develops a student graduation prediction model using the K-Nearest Neighbor (KNN) algorithm, considering variables such as age, Grade Point Average (GPA), number of Credits Earned (CE), participation in TOEFL tests, seminar activities, and participation in internships. Data from 80 students in the computer engineering and information systems programs at STIKOM Cipta Karya Informatika were analyzed to train and test the model. The results show that the KNN model with K=3, K=4, and K=5 produces a prediction accuracy of 66,67%. GPA and the number of credits earned significantly influence graduation, while participation in internships and TOEFL tests also contribute. Seminar certificates and age have a lower impact. These findings indicate that the KNN algorithm is effective for predicting student graduation, providing insights for educational institutions to enhance academic programs and student development.
Co-Authors Faldo Satria Muhammad Syazidan Tundo, Tundo