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

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%

SISTEM REKOMENDASI TOPIK SKRIPSI MENGGUNAKAN METODE CASE BASED REASONING

[Id]Syarat utama mendapatkan gelar sarjana di perguruan tinggi yaitu dengan membuat suatu karya ilmiah skripsi. Skripsi bertujuan agar mahasiswa dapat menyusun serta menulis karya ilmiah sesuai dengan bidang ilmunya. Skripsi dapat dijadikan acuan atau standar untuk menilai ketercapaian pembelajaran mahasiswa selama masa perkuliahan. Mahasiswa akan mencari topik-topik skripsi yang relevan dengan kompetensi serta mata kuliah yang pernah diambil oleh mahasiswa tersebut. Mahasiswa seringkali mengalami kendala dalam menentukan topik skripsi yang akan diambil karena minimnya informasi topik-topik skripsi mahasiswa terdahulu. Oleh karena itu diperlukan suatu sistem yang mampu memberikan rekomendasi topik skripsi bagi mahasiswa.Metode Case Based Reasoning (CBR) dapat digunakan sebagai sistem rekomendasi topik skripsi bagi mahasiswa S1 Teknik Informatika Bumigora Mataram. CBR mempunyai 4 tahapan yaitu retrieval, reuse, revisi dan retain. Tahapan yang paling penting pada CBR adalah proses retrieval karena pada tahap ini dilakukan pencarian solusi untuk kasus baru dengan menghitung nilai similaritas atau nilai kedekatan antara kasus baru dengan kasus lama. Kasus lama berasal dari data-data topik skripsi mahasiswa sebelumnya. Pada penelitian ini nilai similaritas antar kasus di hitung menggunakan metode manhattan distance. Sedangkan inputan sistem menggunakan nilai mata kuliah wajib dan mata kuliah pilihan yang telah diambil oleh mahasiswa. Sistem CBR, akan menghitung nilai similaritas antara kasus baru dengan seluruh kasus lama yang tersimpan dalam basis kasus menggunakan metode manhattan distance. Kasus lama dengan nilai similaritas tertinggi digunakan sebagai solusi kasus baru. Hasil implementasi sistem menunjukkan bahwa case based reasoning mampu memberikan rekomendasi topik skripsi untuk mahasiswa. Tahap pengujian menggunakan 280 data dengan metode K-fold Cross Validation, dimana nilai K yang digunakan adalah 7, 10 dan 13. Nilai akurasi terbaik diperoleh untuk K=13 dengan nilai 94,34% disusul K=10 sebesar 93, 99% dan K= 7 sebesar 93,95%.[En]The main requirement to get a bachelor's degree in college is by making a undergraduate thesis scientific work. Undergraduate thesis aims to enable students to compile and write scientific works in accordance with their fields of science. Undergraduate thesis can be used as a reference or standard to assess the achievement of student learning during the lecture period. Students will look for thesis topics that are relevant to the competencies and courses taken by the student. Students often experience obstacles in determining thesis topics that will be taken because of the lack of information on previous student thesis topics. Therefore we need a system that is able to provide thesis topic recommendations for students.The Case Based Reasoning (CBR) method can be used as a undergraduate thesis topic recommendation system for students of S1 Informatics Engineering Bumigora Mataram. CBR has 4 stages, namely retrieval, reuse, revise and retain. The most important stage in CBR is the retrieval process because at this stage a search for a solution for a new case is done by calculating the value of similarity or the value of proximity between the new case and the old case. The old case comes from the previous student undergraduate thesis topic data. In this research the value of similarity between cases was calculated using the manhattan distance method. While the input system uses the value of compulsory courses and elective courses taken by students. CBR system, will calculate the similarity value between new cases with all old cases stored in the base case using the manhattan distance method. The old case with the highest similarity value is used as a solution to the new case. Based on the results of implementation shows that case based reasoning can be used as a recommendation system for topic and undergraduate thesis supervisor. Test phase used 280 data with K-fold Cross Validation method, where the value of K used were 7, 10 and 13. The best accuracy value obtained for K = 13 was with the value of 94,34% followed by K = 10 equal to 93, 99% and K =93,95%

Case-Based Decision Support for Disaster Management

Disasters are characterized by severe disruptions of the society’s functionality and adverse impacts on humans, the environment, and economy that cannot be coped with by society using its own resources. This work presents a decision support method that identifies appropriate measures for protecting the public in the course of a nuclear accident. The method particularly considers the issue of uncertainty in decision-making as well as the structured integration of experience and expert knowledge