An Intelligent Recommendation Engine for Selecting the University for Graduate Courses in KSA: SARS Student Admission Recommender System

Khanam, Zeba; Alkhaldi, Salwa

doi:10.1007/978-3-030-33846-6_77

Cited by 10 publications

(7 citation statements)

References 12 publications

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“…According to extracted data, another significant use of the recommender system in 12 studies is to provide recommendations on specific or best-fitted colleges or universities. Most studies [29], [30], [31], [32], [33], [34], [35], [36], [37], [38] predict a set of most suitable universities or colleges for the admission of the new students. Only one study [39] determines the educational institution befitting university students.…”

Section: Results a Areas In Higher Educationmentioning

confidence: 99%

“…In [31] and [39] the authors propose a hybrid framework for university admission by integrating the back-propagation neural network algorithm and the C4.5 decision tree. Similarly, [37] focuses on a combined method of random forest and Multivariate Adaptive Regression Splines (MARS) to predict a list of the best colleges. Wakil et al [41] proposes a hybrid web recommender system by combining neural networks (NN) and decision tree (DT).…”

Section: ) Hybrid Approachesmentioning

confidence: 99%

“…However, a number of studies [37], [38], [40], [42], [44], [79] did not validate their proposed solutions.…”

Section: ) Surveymentioning

confidence: 99%

See 2 more Smart Citations

Recommender System in Academic Choices of Higher Education: A Systematic Review

Kamal,

Sarker,

Rahman

et al. 2024

IEEE Access

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Section: Results a Areas In Higher Educationmentioning

confidence: 99%

Section: ) Hybrid Approachesmentioning

confidence: 99%

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Recommender System in Academic Choices of Higher Education: A Systematic Review

Kamal,

Sarker,

Rahman

et al. 2024

IEEE Access

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“…In their work, [Khanam and Alkhaldi 2020] propose a system for recommending undergraduate courses for students who wish to participate in university selection processes. The system analyzes the profile of the students and compares it with the profile of the courses, making the process of choosing the course more straightforward.…”

Section: Related Workmentioning

confidence: 99%

Combining Clustering and Regression Models for Recommending Researchers

Lima

Oliveira

2021

Anais Do IX Symposium on Knowledge Discovery, Mining and Learning (KDMiLe 2021)

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Due to the increase in scientific production, especially in recent years, management and decision support challenge also increase significantly. The task of recommending researchers, for example, to a project is not simple. Even with the proper amount of data, ranking and recommending researchers becomes a challenging process. Despite the different methods, what can happen is that the datasets of an institution or research areas do not have a ranking value, that is, a value that can be used to assess the position of a researcher. Even having a necessary dataset, there is no ranking information for these researchers, and this process of obtaining data for training a model can be costly. We propose to use clustering techniques to support the ranking process, reducing the human effort to obtain examples for models training. Then, we used this dataset to train the regression models and Mean Squared Error (MSE) and Normalized Discounted Cumulative Gain (nDCG) to evaluate them. Tests demonstrate that our solution can support the researchers' recommendation process in an adaptive process to the needs of an organization.

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“…Terkait dengan hal ini, telah ada beberapa penelitian menggunakan berbagai macam pendekatan yang berbeda untuk memberikan solusi, semisal dengan model eksperimental (Wiswall dan Zafar, 2015) atau pemodelan yang didorong oleh data (datadriven) dalam mengidentifikasi faktor penentu pengambilan keputusan dalam pemilihan program studi. Singkat kata, pemanfaatan sains data dalam rangka proses pemilihan program studi di sisi calon mahasiswa baru atau proses PMB di sisi PT sudah mulai marak dilakukan di negara maju seperti Amerika Serikat (Picciano, 2012; Waters dan Miikkulainen, 2014; Liu dan Tan, 2020), ataupun negara lain seperti RRC (Wang dan Shi, 2016) dan Arab Saudi (Khanam dan Alkhaldi, 2019), namun masih cukup asing di Indonesia. Mengingat tiap negara memiliki ciri khas budayanya masing-masing, maka hasil dari satu negara tidak bisa otomatis diaplikasikan begitu saja ke negara lain.…”

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Model Klasifikasi Calon Mahasiswa Baru Untuk Sistem Rekomendasi Program Studi Sarjana Berbasis Machine Learning

Pratama

Aryanto

Pratama

2022

JTIIK

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Proses pemilihan program studi bagi calon mahasiswa baru, khususnya bagi mereka yang masih duduk di bangku SMA atau sederajat, merupakan salah satu momen pengambilan keputusan penting. Tak jarang pilihan yang salah berujung pada kegagalan studi atau kesulitan lain selepas menamatkan studi. Meski sudah mulai marak dilakukan di berbagai negara maju, sistem rekomendasi program studi berbasis machine learning untuk calon mahasiswa baru masih belum banyak dikembangkan di Indonesia. Penelitian ini dilakukan sebagai upaya rintisan sistem rekomendasi tersebut dengan menggunakan data pribadi dan akademik dari semua mahasiswa dan alumni program sarjana di Universitas Islam Indonesia (UII), di mana data prestasi akademik di masing-masing program studi digunakan sebagai ground truth label. Dari hasil penelitian ini, didapatkan sebuah model berbasis Random Forest (RF) dengan tingkat akurasi 86%, presisi 84%, recall 86%, dan AUC 97%. Model ini memiliki kinerja yang jauh lebih baik jika dibandingkan dengan model berbasis Multinomial Logistic Regression (MLR) maupun Support Vector Machine (SVM). Sesuai peta jalan penelitian, model yang dihasilkan dari penelitian ini akan digunakan untuk pengembangan sistem rekomendasi yang dapat membantu calon mahasiswa baru dalam memilih program studi saat proses penerimaan mahasiswa baru (PMB), khususnya di lingkungan UII. AbstractChoosing a major for the prospective undergraduate students is one of the most important moments in their life, especially for the high school graduates. Not infrequently, a wrong choice can lead to academic failure or even other difficulties after graduating from college. While a machine learning-based college major recommendation system is not strange in some developed countries, it is not the case in Indonesia. This study aims to serve as a pilot project for such a recommendation system by using personal and academic data of all students and alumni of the undergraduate programs in Universitas Islam Indonesia (UII) where academic achievement data is used as the ground truth label. Out of three models used and evaluated in this study, we found that Random Forest-based model to be the best option with an accuracy of 86%, precision on 84%, recall of 86%, and AUC of 97%. We also found that this model has a much better performance than other models with Multinomial Logistic Regression (MLR) or Support Vector Machine (SVM). The resulting model from this study will be deployed to develop a college major recommendation system that can help the prospective students choose their majors during college admission process, particularly in the context of UII as per research roadmap.

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An Intelligent Recommendation Engine for Selecting the University for Graduate Courses in KSA: SARS Student Admission Recommender System

Cited by 10 publications

References 12 publications

Recommender System in Academic Choices of Higher Education: A Systematic Review

Recommender System in Academic Choices of Higher Education: A Systematic Review

Combining Clustering and Regression Models for Recommending Researchers

Model Klasifikasi Calon Mahasiswa Baru Untuk Sistem Rekomendasi Program Studi Sarjana Berbasis Machine Learning

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