IJCCS (Indonesian Journal of Computing and Cybernetics Systems) Vol.15, No.1, January 2021, pp. 1~10 ISSN (print): 1978-1520, ISSN (online): 2460-7258 DOI: https://doi.org/10.22146/ijccs.58919  1 Received August 20 th ,2020; Revised November 5 th , 2020; Accepted January 28 th , 2021 Reccomendations on Selecting The Topic of Student Thesis Concentration using Case Based Reasoning Annisaa Utami* 1 , Yohanes Suyanto 2 , Agus Sihabuddin 3 1 Master Program in Computer Science, FMIPA UGM, Yogyakarta, Indonesia 2,3 Department of Computer Science and Electronics, FMIPA UGM, Yogyakarta, Indonesia e-mail: * 1 annisaa.utami@mail.ugm.ac.id, 2 yanto@ugm.ac.id , 3 a_sihabudin@ugm.ac.id Abstrak Case Based Reasoning (CBR) merupakan metode yang bertujuan untuk menyelesaikan suatu kasus baru dengan cara mengadaptasi solusi-solusi yang terdapat pada kasus-kasus sebelumnya yang mirip dengan kasus baru tersebut. Sistem yang dibangun dalam penelitian ini adalah sistem CBR untuk melakukan rekomendasi topik konsentrasi skripsi mahasiswa. Penelitian ini menggunakan data mahasiswa S1 Teknik Informatika IST AKPRIND Yogyakarta dengan jumlah data sebanyak 115 data yang terdiri 80 data latih dan 35 data uji. Penelitian ini bertujuan merancang dan membangun sistem Case Based Reasoning menggunakan Metode Similaritas Nearest Neighbor dan Manhattan Distance, serta membandingkan hasil nilai akurasi menggunakan Metode Nearest Neighbor Similarity dan Manhattan Distance Similarity. Proses rekomendasi dilakukan dengan menghitung nilai kedekatan atau similaritas antara kasus baru dengan kasus lama yang tersimpan dalam basis kasus menggunakan Metode Nearest Neighbor dan Manhattan Distance. Fitur yang digunakan dalam penelitian ini terdiri dari IPK dan nilai mata kuliah. Kasus yang diambil adalah kasus dengan nilai similaritas tertinggi. Jika suatu kasus tidak mendapatkan rekomendasi topik atau kurang dari nilai trashold 0,8, maka akan dilakukan revisi kasus oleh pakar. Kasus yang berhasil direvisi disimpan kedalam sistem untuk dijadikan pengetahuan baru.. Hasil pengujian menggunakan Metode Nearest Neighbor mendapat nilai akurasi 97.14% dan Metode Manhattan Distance 94.29%. Kata kunci— Case Based Reasoning, Nearest Neighbor, Manhattan Distance, Skripsi Abstract 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%. Keywords— Case Based Reasoning, Nearest Neighbor, Manhattan Distance, Thesis