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

2021 ◽  
Vol 15 (1) ◽  
pp. 1
Author(s):  
Annisaa Utami ◽  
Yohanes Suyanto ◽  
Agus Sihabuddin
Keyword(s):  
Undergraduate Students ◽  
Nearest Neighbor ◽  
Training Data ◽  
Manhattan Distance ◽  
Case Based Reasoning ◽  
Distance Method ◽  
Reasoning System ◽  
Course Grades ◽  
Case Based ◽  
Student Thesis

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%.

A Case Based Reasoning System For Recommendation Of Restaurant In Jimbaran Using K-Nearest Neighbor

2020 ◽  
Vol 9 (2) ◽  
pp. 267
Author(s):  
I Gede Teguh Mahardika ◽  
I Wayan Supriana
Keyword(s):  
Recommendation System ◽  
Nearest Neighbor ◽  
Case Based Reasoning ◽  
Retrieval Process ◽  
K Nearest Neighbor ◽  
Reasoning System ◽  
Human Ability ◽  
And Function ◽  
Case Based ◽  
Selection Of

Culinary is one of the favorite businesses today. The number of considerations to choose a restaurant or place to visit becomes one of the factors that is difficult to determine the restaurant or place to eat. To get the desired place to eat advice, one needs a recommendation system. Decisions made by the recommendation system can be used as a reference to determine the choice of restaurants. One method that can be used to build a recommendation system is Case Based Reasoning. The Case Based Reasoning (CBR) method mimics human ability to solve a problem or cases. The retrieval process is the most important stage, because at this stage the search for a solution for a new case is carried out. The study used the K-Nearest Neighbor method to find closeness between new cases and case bases. With the selection of features used as domains in the system, the results of recommendations presented can be more suggestive and accurate. The system successfully provides complex recommendations based on the type and type of food entered by the user. Based on blackbox testing, the system has features that can be used and function properly according to the purpose of creating the system.

scholarly journals DETERMINING LECTURAL EVALUATION IN FACULTY OF ENGINEERING MALIKUSSALEH UNIVERSITY USING K-NN

2019 ◽  
Vol 11 (2) ◽  
pp. 307
Author(s):  
Asrianda Asrianda ◽  
Risawandi Risawandi ◽  
Gunarwan Gunarwan
Keyword(s):  
Nearest Neighbor ◽  
Predictor Variable ◽  
Training Data ◽  
Manhattan Distance ◽  
Similar Data ◽  
K Nearest Neighbor ◽  
Classification Result ◽  
Distance Method ◽  
Testing Data ◽  
Supervised Learning Algorithms

K-Nearest Neighbor is a method that can classify data based on the closest distance. In addition, K-NN is one of the supervised learning algorithms with learning processes based on the value of the target variable associated with the value of the predictor variable. In the K-NN algorithm, all data must have a label, so that when a new data is given, the data will be compared with the existing data, then the most similar data is taken by looking at the label of that data. Filling and processing many questionnaires to determining the results of lectural evaluation from the performance of lecturers certainly requires a lot of time and process. Therefore, it is necessary to apply the K-NN Manhattan Distance method. In this study, the testing data is taken from one of the training data and has a classification result that is "Very Good". After going through the K-NN Manhattan Distance method with k being the closest / smallest neighbor, then the following results are obtained: Distance 5.4, the classification result is "Very Good" and 74.03% of similarity value. Based on the results obtained, the result of the classification from K-NN Manhattan Distance method show similarities with the results of the pre-existing classification.

scholarly journals SISTEM REKOMENDASI TOPIK SKRIPSI MENGGUNAKAN METODE CASE BASED REASONING

2018 ◽  
Vol 4 (3) ◽  
Author(s):  
Ni Luh Putu Merawati ◽  
Sri Hartati
Keyword(s):  
Recommendation System ◽  
Cross Validation ◽  
Manhattan Distance ◽  
Base Case ◽  
Case Based Reasoning ◽  
Distance Method ◽  
K 13 ◽  
Fold Cross Validation ◽  
Undergraduate Thesis ◽  
Case Based

[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%.

Research of Case-Based Reasoning System on the Automotive Body Panel Process

2012 ◽  
Vol 155-156 ◽  
pp. 62-65
Author(s):  
Zhen Wang
Keyword(s):  
Process Design ◽  
Evaluation Method ◽  
Nearest Neighbor ◽  
Comprehensive Evaluation ◽  
Practical Experience ◽  
Case Based Reasoning ◽  
Automotive Body ◽  
Reasoning System ◽  
Case Based ◽  
Body Panel

Case-based reasoning technology has got better application in some fields. This article applies case-based reasoning technology to the automotive body panel process design on the basis of the author's practical experience. It establishes cases of the automobile body panels process design by the object-oriented representation. It designs the automotive body panels case library in the form of relational database.It establishes the way of case retrieval with non-quantitative and comprehensive evaluation method and nearest-neighbor retrieval strategy.

scholarly journals Using extended siamese networks to provide decision support in aquaculture operations

2021 ◽  
Author(s):  
Bjørn Magnus Mathisen ◽  
Kerstin Bach ◽  
Agnar Aamodt
Keyword(s):  
Neural Networks ◽  
Decision Support ◽  
Similar Case ◽  
Decision Makers ◽  
Similarity Function ◽  
Case Based Reasoning ◽  
Similarity Learning ◽  
Reasoning System ◽  
Siamese Networks ◽  
Case Based

AbstractAquaculture as an industry is quickly expanding. As a result, new aquaculture sites are being established at more exposed locations previously deemed unfit because they are more difficult and resource demanding to safely operate than are traditional sites. To help the industry deal with these challenges, we have developed a decision support system to support decision makers in establishing better plans and make decisions that facilitate operating these sites in an optimal manner. We propose a case-based reasoning system called aquaculture case-based reasoning (AQCBR), which is able to predict the success of an aquaculture operation at a specific site, based on previously applied and recorded cases. In particular, AQCBR is trained to learn a similarity function between recorded operational situations/cases and use the most similar case to provide explanation-by-example information for its predictions. The novelty of AQCBR is that it uses extended Siamese neural networks to learn the similarity between cases. Our extensive experimental evaluation shows that extended Siamese neural networks outperform state-of-the-art methods for similarity learning in this task, demonstrating the effectiveness and the feasibility of our approach.

A self-adaptive case-based reasoning system for dose planning in prostate cancer radiotherapy

2011 ◽  
Vol 38 (12) ◽  
pp. 6528-6538 ◽  
Author(s):  
Nishikant Mishra ◽  
Sanja Petrovic ◽  
Santhanam Sundar
Keyword(s):  
Prostate Cancer ◽  
Case Based Reasoning ◽  
Cancer Radiotherapy ◽  
Reasoning System ◽  
Dose Planning ◽  
Prostate Cancer Radiotherapy ◽  
Case Based ◽  
Self Adaptive
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