Reccomendations on Selecting The Topic of Student Thesis Concentration using Case Based Reasoning

• Published in: IJCCS (Indonesian Journal of Computing and Cybernetics Systems) Vol. 15; no. 1; pp. 1 - 10
• Main Authors: Utami, Annisaa, Suyanto, Yohanes, Sihabuddin, Agus
• Format: Journal Article
• Language: English
• Published: Universitas Gadjah Mada 31.01.2021
• Subjects: case based reasoning; manhattan distance; nearest neighbor; thesis
• ISSN: 1978-1520; 2460-7258
• DOI: 10.22146/ijccs.58919

Case Based Reasoning (CBR) is a method that aims to resolve a new case by adapting the solutions contained in previous cases that are similar to the new case. The system built in this study is the CBR system to make recommendations on the topic of student thesis concentration.               This study used data from undergraduate students of Informatics Engineering IST AKPRIND Yogyakarta with a total of 115 data consisting of 80 training data and 35 test data. This study aims to design and build a Case Based Reasoning system using the Nearest Neighbor and Manhattan Distance Similarity Methods, and to compare the results of the accuracy value using the Nearest Neighbor Similarity and Manhattan Distance Similarity methods.               The recommendation process is carried out by calculating the value of closeness or similarity between new cases and old cases stored on a case basis using the Nearest Neighbor Method and Manhattan Distance.  The features used in this study consisted of GPA and course grades. The case taken is the case with the highest similarity value. If a case doesnt get a topic recommendation or is less than the trashold value of 0.8, a case revision will be carried out by an expert. Successfully revised cases are stored in the system to be made new knowledge. The test results using the Nearest Neighbor Method get an accuracy value of 97.14% and Manhattan Distance Method 94.29%.