Prediksi Pengaruh Kegiatan MBKM terhadap Mahasiswa menggunakan Metode K-Nearest Neighbor

The Merdeka Belajar Kampus Merdeka (MBKM) program provides students the opportunity to study for one semester outside of their major, aiming to develop the soft and hard skills required in the workforce. One key component of this program is internships or practical work, which gives students hands-on experience in the professional world and the chance to build professional networks. This research uses the K-Nearest Neighbor (K-NN) method to predict the impact of MBKM activities on undergraduate students at STMIK Kaputama. Using the RapidMiner application, student data was tested to obtain the accuracy of predicting students' engagement in the MBKM program in the future. The test results show that the K-NN model has an accuracy of 75.34%, indicating that the model is fairly good at predicting the impact of the MBKM program on students.

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Publication Details

Journal
Bridge
Published
2024-09-18
DOI
https://doi.org/10.62951/bridge.v2i4.249
Primary Topic
Data Mining and Machine Learning Applications
Type
article
Field-Weighted Citation Impact
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article

Prediksi Pengaruh Kegiatan MBKM terhadap Mahasiswa menggunakan Metode K-Nearest Neighbor

I Gusti Prahmana, Yani Maulita, Farida Hanum
Bridge
Data Mining and Machine Learning Applications
article

Prediksi Pengaruh Kegiatan MBKM terhadap Mahasiswa menggunakan Metode K-Nearest Neighbor

I Gusti Prahmana, Yani Maulita, Farida Hanum
article en

Abstract

The Merdeka Belajar Kampus Merdeka (MBKM) program provides students the opportunity to study for one semester outside of their major, aiming to develop the soft and hard skills required in the workforce. One key component of this program is internships or practical work, which gives students hands-on experience in the professional world and the chance to build professional networks. This research uses the K-Nearest Neighbor (K-NN) method to predict the impact of MBKM activities on undergraduate students at STMIK Kaputama. Using the RapidMiner application, student data was tested to obtain the accuracy of predicting students' engagement in the MBKM program in the future. The test results show that the K-NN model has an accuracy of 75.34%, indicating that the model is fairly good at predicting the impact of the MBKM program on students.

BridgeVol. 2(4)
Kootenay Association for Science & Technology (CA)
Quality Education
Openalex Percentile: Top 68%
Data Mining and Machine Learning Applications
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