SPARRING: Sistem Rekomendasi Peneliti Terintegrasi Google Scholar via SerpAPI dan Latent Dirichlet Allocation pada Konteks Perguruan Tinggi

Mochamad Nizar Palefi Ma'ady; Denny Daffa Rizaldy; Rahul Fahmi Satria; Purnama Anaking

doi:10.26905/jtmi.v9i2.11111

SPARRING: Sistem Rekomendasi Peneliti Terintegrasi Google Scholar via SerpAPI dan Latent Dirichlet Allocation pada Konteks Perguruan Tinggi

Mochamad Nizar Palefi Ma'ady, Denny Daffa Rizaldy, Rahul Fahmi Satria, Purnama Anaking

Abstract

The researcher partner recommendation system plays a crucial role in fostering academic collaboration in universities, where a challenge for new users is finding suitable research partners. In addressing the limitations of Naïve Bayes classifiers, this article introduces an innovative approach in the form of a non-linear sigmoid activation function. We highlight the urgency of this solution, detail its implementation steps, and describe its substantial contribution to research partner recommendations. This article not only identifies existing obstacles but also proposes revolutionary solutions to enhance the effectiveness of consultation systems in academic environments. A gap in this research is the manual input method for data retrieval, creating weaknesses, susceptibility to human errors, and reduced efficiency in collecting journal data. We propose SPARRING, a researcher recommendation system connected to Google Scholar, in the context of higher education. This approach uses a dataset of faculty members from the Faculty of Information Technology and Business at a private university in Indonesia. The results from Google Scholar extraction, with topic keywords determined by Latent Dirichlet Allocation, are then classified using the Naïve Bayes algorithm. Additionally, we integrate web scraping tools, particularly SerpAPI, to access data from Google Scholar. Through the integration of SerpAPI, the proposed web-based system is capable of providing more accurate recommendations, especially for new users with limited collaboration experience. By incorporating SerpAPI, the proposed web-based system can offer more accurate recommendations, particularly for new users without extensive collaboration experience.

Keywords

Google Scholar; Naive Bayes; Latent Dirichlet Allocation; Recommendation System; Higher Education;

Full Text:

PDF

References

H. Samin and T. Azim, “Knowledge Based Recommender System for Academia Using Machine Learning: A Case Study on Higher Education Landscape of Pakistan,”

V. Amarante, M. Bucheli, and R. Vivas, “Documentos de Trabajo Research networks and publications in Econ

J. F. Volkwein and K. Parmley, “Comparing administrative satisfaction in public and private universities,” Res. High. Educ., vol. 41, no. 1, pp. 95–116, 2000.

L. Aldieri, G. Guida, M. Kotsemir, and C. P. Vinci, An investigation of impact of research collaboration on academic performance in Italy, vol. 53, no. 4. Springer Netherlands, 2019.

A. Abbas, A. Arrona-Palacios, H. Haruna, and D. Alvarez-Sosa, “Elements of students’ expectation towards teacher-student research collaboration in higher education,” Proc. - Front. Educ. Conf. FIE, vol. 2020-Octob, 2020.

G. Abramo, C. A. D’Angelo, and M. Solazzi, “Assessing public private research collaboration: Is it possible to compare university performance?,” Scientometrics, vol. 84, no. 1, pp. 173–197, 2010.

Kemenristekdikti, “Pangkalan Data Pendidikan Tinggi [Higher Education Database],” 2013. [Online]. Available: https://forlap.kemdikbud.go.id/. [Accessed: 19-Apr-2022].

S. Khalid, M. Zohaib Irshad, and B. Mahmood, “Job Satisfaction among Academic Staff: A Comparative Analysis between Public and Private Sector Universities of Punjab, Pakistan,” Int. J. Bus. Manag., vol. 7, no. 1, 2011.

D. B. Guruge, R. Kadel, and S. J. Halder, “The state of the art in methodologies of course recommender systems—a review of recent research,” Data, vol. 6, no. 2, pp. 1–30, 2021.

C. Di Sipio, R. Rubei, D. Di Ruscio, and P. T. Nguyen, “A Multinomial Naïve Bayesian (MNB) Network to Automatically Recommend Topics for GitHub Repositories,” ACM Int. Conf. Proceeding Ser., pp. 71–80, 2020.

P. P. Wadekar, Y. P. Pillai, M. U. Roy, and P. N. Phadnis, “Placement Predictor and Course Recommender System,” Academia.Edu, pp. 3960–3965, 2018.

T. Cardona, E. A. Cudney, R. Hoerl, and J. Snyder, “Data Mining and Machine Learning Retention Models in Higher Education,” J. Coll. Student Retent. Res. Theory Pract., 2020.

A. I. Saleh, A. I. El Desouky, and S. H. Ali, “Promoting the performance of vertical recommendation systems by applying new classification techniques,” Knowledge-Based Syst., 2015.

R. S. Gaikwad, S. S. Udmale, and V. K. Sambhe, “E-commerce Recommendation System Using Improved Probabilistic Model,” Lect. Notes Networks Syst., vol. 10, pp. 277–284, 2018.

M. J. Pazzani, J. Muramatsu, D. S. Billsus, and Webert, “Identifying interesting web sites,” Aaai, pp. 54–59, 1996.

P. Cotter and B. Smyth, "PTV: Intelligent Personalized TV Guides," Intell. Appl. Artif. Intell., 2000.

L. Shah, H. Gaudani, and P. Balani, “Survey on Recommendation System,” Int. J. Comput. Appl., vol. 137, no. 7, pp. 43–49, 2016.

D. Pavlov and D. Pennock, “A Maximum Entropy Approach to

Collaborative Filtering in Dynamic, Sparse, High-Dimensional Domains,” in Advances in Neural Information Processing Systems, 2002, vol. 15.

A. A. Neamah and A. S. El-Ameer, “Design and Evaluation of a Course Recommender System Using Content-Based Approach,” in 2018 International Conference on Advanced Science and Engineering (ICOASE), 2018, pp. 1–6.

R. Ghani and A. Fano, “Building Recommender Systems using a Knowledge Base of Product Semantics,” Recomm.. eCommerce.

K. Miyahara and M. J. Pazzani, “Collaborative filtering with the simple bayesian classifier,” in Pacific Rim International conference on artificial intelligence, 2000, pp. 679–689.

X. Yang, Y. Guo, and Y. Liu, “Bayesian-inference-based recommendation in online social networks,” IEEE Trans. Parallel Distrib. Syst., vol. 24, no. 4, pp. 642–651, 2013.

Y. Zhang and J. Koren, “SIGIR2007_Hierarchical_Bayesian_User_Modeling_in_RS.pdf,” pp. 47–54, 2007.

S. Eger, P. Youssef, and I. Gurevych, “Is it time to swish? Comparing Deep Learning Activation Functions across NLP tasks,” Proc. Conf. Empir. Methods Nat. Lang. Process. 2018.

P. Ramachandran, B. Zoph, and Q. V Le, “Searching for activation functions. CoRR abs/1710.05941,” arXiv Prepr. arXiv1710.05941, 2017.

L. M. De Campos, “A scoring function for learning Bayesian networks based on mutual information and conditional independence tests,” J. Mach. Learn. Res., pp. 2149–2187, 2006.

B. Zadrozny and C. Elkan, “I ° :,” no. x, pp. 694–699.

D. Tripathi, D. R. Edla, V. Kuppili, and A. Bablani, “Evolutionary Extreme Learning Machine with novel activation function for credit scoring,” Eng. Appl. Artif. Intell., vol. 96, p. 103980, 2020.

C. Nwankpa, W. Ijomah, A. Gachagan, and S. Marshall, “Activation Functions: Comparison of trends in Practice and Research for Deep Learning,” pp. 1–20, 2018.

A. Ivaschenko and M. Milutkin, “HR decision-making support based on natural language processing,” in Conference on Creativity in Intelligent Technologies and Data Science, 2019.

DOI: https://doi.org/10.26905/jtmi.v9i2.11111