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

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 an...

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Autores principales:	Ahmad Rafie Pratama, Rio Rizki Aryanto, Lizda Iswari
Formato:	article
Lenguaje:	ID
Publicado:	Ikatan Ahli Indormatika Indonesia 2021
Materias:	comparative study recommendation system major selection machine learning classification model Systems engineering TA168 Information technology T58.5-58.64
Acceso en línea:	https://doaj.org/article/629cdece7cec4a578dc69b408583aaed
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spelling	oai:doaj.org-article:629cdece7cec4a578dc69b408583aaed2021-11-16T13:16:12ZStudi Komparasi Model Klasifikasi Berbasis Pembelajaran Mesin untuk Sistem Rekomendasi Program Studi2580-076010.29207/resti.v5i5.3392https://doaj.org/article/629cdece7cec4a578dc69b408583aaed2021-10-01T00:00:00Zhttp://jurnal.iaii.or.id/index.php/RESTI/article/view/3392https://doaj.org/toc/2580-0760Selecting 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.Ahmad Rafie PratamaRio Rizki AryantoLizda IswariIkatan Ahli Indormatika Indonesiaarticlecomparative studyrecommendation systemmajor selectionmachine learningclassification modelSystems engineeringTA168Information technologyT58.5-58.64IDJurnal RESTI (Rekayasa Sistem dan Teknologi Informasi), Vol 5, Iss 5, Pp 853-862 (2021)
institution	DOAJ
collection	DOAJ
language	ID
topic	comparative study recommendation system major selection machine learning classification model Systems engineering TA168 Information technology T58.5-58.64
spellingShingle	comparative study recommendation system major selection machine learning classification model Systems engineering TA168 Information technology T58.5-58.64 Ahmad Rafie Pratama Rio Rizki Aryanto Lizda Iswari Studi Komparasi Model Klasifikasi Berbasis Pembelajaran Mesin untuk Sistem Rekomendasi Program Studi
description	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.
format	article
author	Ahmad Rafie Pratama Rio Rizki Aryanto Lizda Iswari
author_facet	Ahmad Rafie Pratama Rio Rizki Aryanto Lizda Iswari
author_sort	Ahmad Rafie Pratama
title	Studi Komparasi Model Klasifikasi Berbasis Pembelajaran Mesin untuk Sistem Rekomendasi Program Studi
title_short	Studi Komparasi Model Klasifikasi Berbasis Pembelajaran Mesin untuk Sistem Rekomendasi Program Studi
title_full	Studi Komparasi Model Klasifikasi Berbasis Pembelajaran Mesin untuk Sistem Rekomendasi Program Studi
title_fullStr	Studi Komparasi Model Klasifikasi Berbasis Pembelajaran Mesin untuk Sistem Rekomendasi Program Studi
title_full_unstemmed	Studi Komparasi Model Klasifikasi Berbasis Pembelajaran Mesin untuk Sistem Rekomendasi Program Studi
title_sort	studi komparasi model klasifikasi berbasis pembelajaran mesin untuk sistem rekomendasi program studi
publisher	Ikatan Ahli Indormatika Indonesia
publishDate	2021
url	https://doaj.org/article/629cdece7cec4a578dc69b408583aaed
work_keys_str_mv	AT ahmadrafiepratama studikomparasimodelklasifikasiberbasispembelajaranmesinuntuksistemrekomendasiprogramstudi AT riorizkiaryanto studikomparasimodelklasifikasiberbasispembelajaranmesinuntuksistemrekomendasiprogramstudi AT lizdaiswari studikomparasimodelklasifikasiberbasispembelajaranmesinuntuksistemrekomendasiprogramstudi
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Studi Komparasi Model Klasifikasi Berbasis Pembelajaran Mesin untuk Sistem Rekomendasi Program Studi

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