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%.

scholarly journals DIAGNOSIS PENYAKIT HIPERTENSI MENGGUNAKAN METODE CASE-BASED REASONING

2020 ◽  
Vol 5 (1) ◽  
Author(s):  
Amalia Beladinna Arifa ◽  
Gita Fadila Fitriana
Keyword(s):  
Cross Validation ◽  
Case Based Reasoning ◽  
Minkowski Distance ◽  
Fold Cross Validation ◽  
Case Based

Hipertensi adalah kondisi ketika tekanan darah pada pembuluh darah bersih meningkat secara kronik. Jika tidak segera ditangani dapat menyebabkan peningkatan resiko kejadian penyakit lainnya, misalnya kardiovaskuler, serebrovaskuler dan renovaskuler. Diagnosis penyakit hipertensi perlu ditegakkan sedini mungkin guna menurunkan peningkatan resiko kejadian penyakit lainnya. Penelitian ini bertujuan menghasilkan sistem yang mengimplementasikan metode Case-Based Reasoning yang dapat membantu paramedis untuk mendiagnosis penyakit hipertensi. Implementasi sistem dirancang menggunakan bahasa pemrograman PHP serta penyimpanan data kasus menggunakan MySQL. Kasus-kasus penyakit hipertensi yang sudah berhasil ditangani oleh dokter dijadikan sebagai data acuan untuk mendiagnosis kasus hipertensi yang baru. Kasus baru yang berisi faktor resiko, gejala dan riwayat penyakit selanjutnya dicari kemiripannya dengan kasus-kasus lama dengan cara menghitung nilai similaritas menggunakan Minkowski Distance. Pengujian dilakukan pada 172 data menggunakan 10-fold Cross-Validation. Hasil perhitungan dengan menetapkan threshold sebesar 0,90 didapatkan tingkat akurasi sebesar 94,71%. Hasil penelitian menunjukkan implementasi Case-Based Reasoning dapat digunakan untuk melakukan diagnosis penyakit hipertensi

scholarly journals Diagnosis Tahapan Pengguna Narkoba Menggunakan Metode K-Nearest Neighbor

2020 ◽  
Vol 6 (1) ◽  
pp. 101
Author(s):  
Tursina Tursina ◽  
Hafiz Muhardi ◽  
Dian Aulia Sari
Keyword(s):  
Cross Validation ◽  
Nearest Neighbor ◽  
Confusion Matrix ◽  
Case Based Reasoning ◽  
K Nearest Neighbor ◽  
Fold Cross Validation ◽  
Case Based

Narkoba merupakan bahan yang sangat bermanfaat untuk pengobatan, namun jika disalahgunakan akan memberikan dampak buruk yang luar biasa seperti gangguan kesehatan, gangguan kejiwaan hingga kematian. Seorang pengguna narkoba cenderung tertutup dan tidak ingin berkonsultasi langsung ke dokter maupun rehabilitasi dikarenakan pengguna malu dengan kondisinya, biaya yang relatif mahal, jarak dan waktu yang ditempuh, takut dilaporkan dan tanggapan negatif dari masyarakat. Tujuan dilakukannya penelitian ini adalah untuk membantu seorang pengguna narkoba ataupun bagi seseorang yang dicurigai sebagai pengguna narkoba dalam mendiagnosis tahapan pengguna narkoba dan memberikan solusi serta saran terhadap pengguna narkoba tersebut. Case based reasoning merupakan penalaran yang digunakan untuk menyelesaikan kasus baru dengan cara mengadaptasi solusi yang terdapat pada kasus-kasus sebelumnya, yang mempunyai permasalahan yang mirip dengan kasus baru. Pada tahapan retrieve, terjadi proses menghitung similaritas antara kasus baru dan kasus lama. Perhitungan similaritas kasus pada penelitian ini menggunakan metode k-nearest neighbor. Pengujian hasil akhir sistem menggunakan pengujian tahapan CBR dan pengujian kinerja metode k-nearest neighbor. Hasil pengujian mengukur kinerja dari metode k-nearest neighbor dengan nilai k=7, tingkat akurasi untuk 10-fold cross validation sebesar 98,333%, confusion matrix sebesar 100% dan termasuk excellent classification karena memiliki nilai AUC 1,000.

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%.

scholarly journals Case-Based Reasoning for Stroke Disease Diagnosis

2018 ◽  
Vol 12 (1) ◽  
pp. 33 ◽  
Author(s):  
Nelson Rumui ◽  
Agus Harjoko ◽  
Aina Musdholifah
Keyword(s):  
Blood Flow ◽  
Cerebrovascular Disease ◽  
Cross Validation ◽  
High Sensitivity ◽  
Disease Diagnosis ◽  
Case Based Reasoning ◽  
Similarity Calculation ◽  
The Brain ◽  
Fold Cross Validation ◽  
Case Based

Stroke is a type of cerebrovascular disease that occurs because blood flow to the brain is disrupted. Examination of stroke accurately using CT scan, but the tool is not always available, so it can be done by the Siriraj Score. Each type of stroke has similar symptoms so doctors should re-examine similar cases prior to diagnosis. The hypothesis of the Case-based reasoning (CBR) method is a similar problems having similar solution.This research implements CBR concept using Siriraj score, dense index and Jaccard Coeficient method to perform similarity calculation between cases.The test is using k-fold cross validation with 4 fold and set values of threshold (0.65), (0.7), (0.75), (0.8), (0.85), (0.9), and (0.95). Using 45 cases of data test  and 135 cases of case base. The test showed that threshold of 0.7 is suitable to be applied in sensitivity (89.88%) and accuracy (84.44% for CBR using indexing and 87.78% for CBR without indexing). Threshold of 0.65 resulted high sensitivity  and accuracy but showed many cases of irrelevant retrieval results. Threshold (0.75), (0.8), (0.85), (0.9) and (0.95) resulted in sensitivity (65.48%, 59.52%, 5.95%, 3,57% and 0%) and accuracy of CBR using indexing (61.67%, 55.56%, 5.56%, 3.33%, and 0%) and accuracy of CBR without indexing (62.78% 56.67%, 55.56%, 5.56%, 3.33%, and 0%).

scholarly journals Development of Ground Special Vehicle PHM with Case-Based Reason Model

2021 ◽  
Vol 11 (10) ◽  
pp. 4494
Author(s):  
Qicai Wu ◽  
Haiwen Yuan ◽  
Haibin Yuan
Keyword(s):  
Cross Validation ◽  
Optimization Methods ◽  
Engineering Practice ◽  
Case Based Reasoning ◽  
Case Retrieval ◽  
Data Set ◽  
Attribute Weights ◽  
Special Vehicle ◽  
Weight Allocation ◽  
Case Based

The case-based reasoning (CBR) method can effectively predict the future health condition of the system based on past and present operating data records, so it can be applied to the prognostic and health management (PHM) framework, which is a type of data-driven problem-solving. The establishment of a CBR model for practical application of the Ground Special Vehicle (GSV) PHM framework is in great demand. Since many CBR algorithms are too complicated in weight optimization methods, and are difficult to establish effective knowledge and reasoning models for engineering practice, an application development using a CBR model that includes case representation, case retrieval, case reuse, and simulated annealing algorithm is introduced in this paper. The purpose is to solve the problem of normal/abnormal determination and the degree of health performance prediction. Based on the proposed CBR model, optimization methods for attribute weights are described. State classification accuracy rate and root mean square error are adopted to setup objective functions. According to the reasoning steps, attribute weights are trained and put into case retrieval; after that, different rules of case reuse are established for these two kinds of problems. To validate the model performance of the application, a cross-validation test is carried on a historical data set. Comparative analysis of even weight allocation CBR (EW-CBR) method, correlation coefficient weight allocation CBR (CW-CBR) method, and SA weight allocation CBR (SA-CBR) method is carried out. Cross-validation results show that the proposed method can reach better results compared with the EW-CBR model and CW-CBR model. The developed PHM framework is applied to practical usage for over three years, and the proposed CBR model is an effective approach toward the best PHM framework solutions in practical applications.

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 Geometric Case Based Reasoning for Stock Market Prediction

2020 ◽  
Vol 12 (17) ◽  
pp. 7124
Author(s):  
Se-Hak Chun ◽  
Young-Woong Ko
Keyword(s):  
Stock Market ◽  
Nearest Neighbors ◽  
Percentage Error ◽  
Case Based Reasoning ◽  
Stock Market Index ◽  
Distance Method ◽  
Market Index ◽  
Shape Distance ◽  
Case Based ◽  
Better Than

Case based reasoning is a knowledge discovery technique that uses similar past problems to solve current new problems. It has been applied to many tasks, including the prediction of temporal variables as well as learning techniques such as neural networks, genetic algorithms, decision trees, etc. This paper presents a geometric criterion for selecting similar cases that serve as an exemplar for the target. The proposed technique, called geometric Case Based Reasoning, uses a shape distance method that uses the number of sign changes of features for the target case, especially when extracting nearest neighbors. Thus, this method overcomes the limitation of conventional case-based reasoning in that it uses Euclidean distance and does not consider how nearest neighbors are similar to the target case in terms of changes between previous and current features in a time series. These concepts are investigated against the backdrop of a practical application involving the prediction of a stock market index. The results show that the proposed technique is significantly better than the random walk model at p < 0.01. However, it was not significantly better than the conventional CBR model in the hit rate measure and did not surpass the conventional CBR in the mean absolute percentage error.

Using a Recommendation System to Support Problem Solving and Case-Based Reasoning Retrieval

2017 ◽  
Vol 23 (1) ◽  
pp. 177-187
Author(s):  
Andrew A. Tawfik ◽  
Hamed Alhoori ◽  
Charles Wayne Keene ◽  
Christian Bailey ◽  
Maureen Hogan
Keyword(s):  
Problem Solving ◽  
Recommendation System ◽  
Case Based Reasoning ◽  
Case Based
Sign in / Sign up

Export Citation Format

Share Document