Studi Komparasi Model Klasifikasi Berbasis Pembelajaran Mesin untuk
Sistem Rekomendasi Program Studi

Studi Komparasi Model Klasifikasi Berbasis Pembelajaran Mesin untuk
Sistem Rekomendasi Program Studi

: comparative study, recommendation system, major selection, machine learning classification models, single stage
model, multistage model

Selecting a major can be quite difficult for prospective college students. The choice may have an effect not only on their
academic life, but also on their career path. Due to some restrictions as the impact of the COVID-19 pandemic, universities
must find novel ways to reach prospective students and assist them in choosing their majors, one of which is a college major
recommendation system. This system can assist prospective students in determining the most appropriate majors for them
based on data from the current students. Unlike other existing systems that employ either a rule-based or fuzzy model, this
study employs a machine learning approach using data from undergraduate students at Universitas Islam Indonesia. This
paper aims to compare several clustering models (i.e., K-means, Agglomerative, Birch, and DBSCAN) for the purpose of
categorizing current students, to which the results will be used for classification purposes using various approaches (i.e., single
stage vs. multistage), algorithms (i.e., multinomial logistic regression, random forest, and support vector machine), and
scenarios (i.e., with or without GPA-based label). Our findings indicate that the K-means model outperformed all other
clustering models and that the single stage with random forest classification model performed the best across all scenarios.

Rio Rizki Aryanto1
, Ahmad R Pratama2*
, Lizda Iswari3

Universitas Islam Indonesia

25-10-2021

Fajar bagus W

PDF

Indonesia

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