Abstract
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. 
Highlights
Selecting a major can be quite difficult for prospective college students
Survey ICNN tersebut menunjukkan bahwa pemilihan program studi masih menjadi tantangan besar yang perlu mendapatkan perhatian
Information Gain and Rough Set,” International Journal of transfer learning with an application to selection process,” in Advanced Research in Artificial Intelligence, vol 3, no. 11, Frontiers in Artificial Intelligence and Applications, 2020, vol
Summary
Melanjutkan studi pendidikan perguruan tinggi merupakan salah satu tujuan bagi siswa Sekolah Menengah Atas (SMA) dan sederajat. Temuan tersebut yang menjadi dasar pembatasan sosial berpotensi membuat calon mahasiswa dari penelitian ini untuk melakukan komparasi antara kesulitan mengakses informasi terkait program studi model klasifikasi single stage dengan model multistage. Penelitian ini sendiri rekomendasi pemilihan program studi dengan menggunakan data mahasiswa jenjang sarjana di mengimplementasikan model klasifikasi single stage. Model tersebut program studi lain memiliki atribut yang sama seperti akan mempelajari karakteristik mahasiswa berdasarkan varibel prediktor model klasifikasi semi-supervised nilai mata kuliahnya untuk kemudian learning. Penentuan Kelompok Data Latih selain Informatika, proses pengelompokkan mahasiswa Setelah melakukan pengelompokkan seluruh program akan dilakukan dengan pendekatan semi-supervised studi menggunakan model clustering maupun model learning yang dilakukan pada tahap selanjutnya. Menentukan kelompok mahasiswa mana yang akan digunakan sebagai data latih pada model klasifikasi sistem rekomendasi pemilihan program studi. Penentuan kelompok atau kelas mahasiswa tersebut dapat dilakukan dengan melihat sebaran data baik nilai mata kuliah maupun nilai mata pelajaran pada masing-masing kelas
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