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Intan Yuniar Purbasari
Informatics, Department of Computer Science, UPN “Veteran” Jawa Timur
Computer Science & IT Economics, Econometrics & Finance

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Undergraduate Thesis Supervisor Recommendation Based On Text Similarity Fetty Tri Anggraeny; Intan Yuniar Purbasari; Eka Fitria Wulandari
Prosiding International conference on Information Technology and Business (ICITB) 2019: INTERNATIONAL CONFERENCE ON INFORMATION TECHNOLOGY AND BUSINESS (ICITB) 5
Publisher : Proceeding International Conference on Information Technology and Business

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Abstract

The measurement of the similarity of texts is very extensive research. The most common implementation is finding documents that match the search keywords. And the current implementation is to measure the plagiarism of scientific documents. In the academic field, the topic of undergraduate students needs to be done an examination of similarity, in addition to knowing the title of the previous thesis that is similar, also to provide recommendations for suitable supervisors. In this study, we propose a system that can help undergraduate students determine supervisors based on the undergraduate thesis title to be proposed. The experimental results show that the proposed method is quite good as a recommendation system for undergraduate thesis supervisors with an accuracy of 80% in the field of research and 87.5% in the field of lecturer research.Keywords: Text similarity, Dice Coefficient, Supervisor Recommendation.
Relief Feature Selection and Bayesian Network Model for Hepatitis Diagnosis Fetty Tri Anggraeny; Intan Yuniar Purbasari; Evi Suryaningsih
Prosiding International conference on Information Technology and Business (ICITB) 2017: INTERNATIONAL CONFERENCE ON INFORMATION TECHNOLOGY AND BUSINESS (ICITB) 3
Publisher : Proceeding International Conference on Information Technology and Business

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Abstract

A doctor diagnose a disease by evaluating patient condition or by comparing with another patient that have similar conditions or symptoms. In computer science, this task can be done by a computer program that included intelligent algorithm in it. Some disease have similar symptoms, such as typhoid fever, hepatitis, and dengue fever. Based on UCI database there are 17 symptoms of Hepatitis that may be similar with other disease, so it needs a method to find the major symptoms. In this research, we proposed hepatitis diagnose using statistic Bayesian network and find major symptoms using ReliefF algorithm. ReliefF algorithm resulting 4 majority symptoms and used to constructing Bayesian Network. ReliefF and Bayesian Network have 76,8% accuracy, 76,5% precision, and 100% recall for 69 test data. Keywords: Hepatitis, ReliefF, Bayesian Network, Probabilistic.
Co-Authors Eka Fitria Wulandari Evi Suryaningsih Fetty Tri Anggraeny