Arie Rachmad Syulistyo

Work place: Departemen of Information Technologi, State Polytechnic of Malang , Malang, Indonesia



Research Interests: Computer systems and computational processes, Enterprise Information System, Multimedia Information System, Data Structures and Algorithms, Programming Language Theory


Arie Rachmad Syulistyo was born on August 24, 1987 He received the S.T. and M.Kom. degrees from Brawijaya University and Indonesia University, in 2011 and 2015, respectively. His research interests include mobile programming, object based programming, enterprise application, and applied multimedia.

Author Articles
Research Supervisor Recommendation System Based on Topic Conformity

By Ridwan Rismanto Arie Rachmad Syulistyo Bebby Pramudya Citra Agusta

DOI:, Pub. Date: 8 Feb. 2020

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.

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