Research Supervisor Recommendation System Based on Topic Conformity

Abstract

In a higher education such as universities, final project are under supervision of one or more supervisors with a similar research interest or topic. The determination of the final project supervisor is an important factor in the work of the student's final project. However, the lack of information about the supervisor can hamper students in making the determination of the supervisor. Thus, a system is needed that can facilitate students in determining the final project or thesis advisors in accordance with the research topic. This problem is the basis of this research. The study is conducted by developing a web-based system and applying the TF-IDF word weighting and cosine similarity method. TF-IDF method is a way to give the weight of the relationship of a word to the document. The cosine similarity is a method for calculating the similarity between two objects expressed in two vectors by using keywords from a document as a measure. The results of the advisor recommendation system can provide recommendations to students regarding the final assignment advisor who has conducted research in accordance with the topic of the student's final assignment written in Indonesian. In 20 testing, the accuracy of the comparison of the results of the system recommendations with the actual data obtained an average of 75% by comparing system recommendation with actual assigned supervisor.

Text Classification based on Discriminative Semantic Features and Variance of Fuzzy Similarity

“…It is shown (Aggarwal and Zhaei, 2012) that pre-processing improves the classification performance in most evaluations. Before the dataset is entered into the proposed model, pre-processing data is first performed, this process is needed to prepare the data to be able to further processed by the algorithm and to increase accuracy by minimizing bias and noise caused by non-basic words, unimportant terms (Rismanto et al, 2020). This phase includes text normalization, tokenization, and stop words removal.…”

Recommendations of Thesis Supervisor using the Cosine Similarity Method

“…Penelitian sebelumnya tentang data mining ataupun text mining bidang pendidikan sudah dilakukan oleh beberapa peneliti seperti metode C4.5 digunakan untuk penentuan beasiswa dengan akurasi sebesar 92% [1] dan akurasi 81% [2], penelitian [3] menggunakan metode C4.5 untuk prediksi kelulusan dengan akurasi 93%, dan penelitian [4] menggunakan metode naïve bayes untuk klasifikasi topik skripsi berdasarkan abstrak dengan akurasi 88%. Adapun penelitian yang berfokus pada permasalahan rekomendasi dosen pembimbing sudah dilakukan oleh peneliti menggunakan berbagai metode, diantaranya adalah metode Cosine Similiarity oleh [5], [6] dengan masing-masing akurasi sebesar 75 % dan 98% , metode Naïve Bayes [7] mendapatkan presisi dan recall masing-masing sebesar 74% dan 100%, metode Vector Space Model (VSM) [8] dengan akurasi 93%, SVM dan Weighted Product [9] dengan presisi 93%, AHP [10], dan Dice Coefficient Similarity [11] dengan akurasi sebesar 88%.…”

Recommendation Systems for Education: Systematic Review

“…In terms of the type of element recommended, it was noted that 37 works stand out in which RSs offer learning resources, and these recommendations are mostly given in accordance with user preferences; 133 RSs suggest courses; 5 recommend a sequence of courses/syllabuses; 5 recommend elective degrees courses; and the remainder focus on recommending papers, postgraduate courses, academic advice, and professions, among others. The articles [85,106,108] that give academic advice stand out. Likewise, articles [31,33,112] that recommend study sequence or syllabuses.…”