Implementasi Case-based Reasoning Untuk Sistem Tanya Jawab Penyakit Pada Anjing

This debriefing question answering system using Case Base Reasoning algorithm. Case Base Reasoning is an algorithm to solve new problems by comparing the old problems or solve new problems by providing answers from a document.In this system uses several methods to process the document as an information / knowledge that makes the system increasingly relevant in answering the question. The method used is the Modified K-Nearest Neighbour, Vector Space Model and Paragraph Based Passage. M-KNN method is used to facilitate in classifying diseases in dogs, VSM method is used to search for relevant documents that match the query. then to provide answers to relevant documents obtained by the system used method Based Paragraph Passage. This level of accuracy obtained from this exchange system using training data 228 is equal to 92% with a value of k = 3

IMPLEMENTASI CASE-BASED REASONING DAN RULE-BASED REASONING UNTUK KONSELING MAHASISWA DI STT TELKOM CASE-BASED REASONING AND RULE-BASED REASONING IMPLEMENTATION FOR STUDENT COUNSELING IN STT TELKOM

ABSTRAKSI: Mahasiswa merupakan generasi penerus yang merupakan motor penggerak pembangunan bangsa. Namun, lepas dari semua itu mahasiswa hanyalah manusia biasa yang juga diliputi masalah terutama yang dihadapi pada jenjang perkuliahan. Tidak semua mahasiswa dapat melewati jenjang ini dengan mulus, tidak sedikit pun yang harus mengalamai Dropped Out (DO) karena masalah seperti kemampuan akademik yang kurang, kurangnya dukungan dari keluarga, keadaan ekonomi yang sulit, dan lain-lain yang tidak bisa dipecahkan secara optimal. Dalam tugas akhir ini, akan dibahas salah satu cara untuk mengatasi permasalahan melalui konseling mahasiswa. Konseling mahasiswa, dilakukan dengan menggunakan sistem perangkat lunak yang secara interaktif mencoba menganalisa masalah yang sedang dihadapi oleh seorang mahasiswa, dan memberikan solusi yang tepat atas masalah tersebut. Metode yang digunakan dalam tugas akhir ini adalah case-based reasoning dan rule-based reasoning. Case-based reasoning merupakan paradigma yang menekankan penyelesaian masalah dengan berdasarkan pada kasus-kasus sebelumnya. Pada metode ini, terdapat learning yaitu proses belajar sehingga kasus baru yang ditemukan akan menjadi pengetahuan baru bagi basis pengetahuan. Sedangkan metode rule-based reasoning digunakan untuk mengidentifikasi penyebab masalah yang dialami tersebut.Kata Kunci : case-based reasoning, rule-based reasoning, mahasiswa, konseling, dan masalah.ABSTRACT: The raise of this nation is depending on the next generation, i.e. the university student. However, they are also human being that always struggle with problems especially in their college. Not all of them could pass this stage easily, or maybe ended Dropped Out (DO) because lack of academic ability, unsupported family, not able to pay the costs, and other problems that cannot be solved. Counseling is a very good alternative ways to solve out these problems. In this final project, the student could interact with the software system in such interactive way. The system will analyze the problem faced by them, and give the right solution for that problem. The used methods are case-based reasoning and rule-based reasoning. Case-based reasoning is a paradigm that focuses on solving problem based on the previous cases. In this method, learning is used as a learn process so that the solution of the new case will become new input to the knowledge base. The rule-based reasoning is used to identify the cause of the problems.Keyword: case-based reasoning, rule-based reasoning, college student, counseling, and problem

Penerapan Case Based Reasoning dan Algortima Nearest Neighbor untuk Penentuan Lokasi Waralaba

Penentuan lokasi waralaba merupakan langkah penting dalam memulai sebuah bisnis. Penentuan lokasi yang baik sudah hampir setengah jalan menuju sukses bisnis tersebut. Penentuan lokasi ini menggunakan metode dari konsultan Mandiri Bisnis Mandiri (MBC), yang terdiri dari 22 attribut sebagai penentuan prospek suatu lokasi. Prospek lokasi dibedakan menjadi tiga, yaitu sangat prospek, prospek, dan kurang prospek. Penentuan lokasi oleh MBC masih dilakukan secara manual sehingga tergantung pada konsultan serta membutuhkan waktu yang lama dan hasil kurang akurat. Untuk mengatasi masalah tersebut dilakukan dengan bantuan aplikasi komputer dengan pendekatan Case-based Reasoning(CBR) dan algoritma Nearest Neighbor. Hasil komputasi dengan implementasi CBR dan algoritma nearest neighbor menunjukkan bahwa proses penentuan lokasi menjadi lebih cepat dan akurat. Hal ini dibuktikan dengan analisis hasil penelitian yang sebelumnya rata-rata waktu yang dibutuhkan adalah 17,95 menit setiap kasus sedangkan dengan menggunakan sistem rata-rata waktu yang dibutuhkan adalah 1,15 menit dan keakuratan hasil rata-rata sebelum menggunakan sistem adalah 70% sedangkan dengan setelah menggunakan sistem meningkat tingkat keakuratannya mencapai 95%

Case Based Reasoning Untuk Mendiagnosa Penyakit Kehamilan Menggunakan Cosine Similarity

The application of case-based reasoning in diagnosing pregnancy deseases is motivated by the lack of number of obstetricians. The use of CBR aims to solve new problems by adapting the solutions contained in the previous case, by calculating the level of similarity. Calculation of similarity value using cosine similarity method, with threshold equal to 100%. This system can diagnose 6 diseases, 28 existing symptoms. System outbreaks of illness experienced by patients based on symptoms induced by non-specialist medical personnel, as well as handling solutions accompanied by a presentation of similarities with previous cases to indicate the degree of truth of possible diagnosis. Based on the results of case testing, the results obtained: the system can retrieve the exact old case and have used the cosine similarity methodology correctly, shown with 100% accuracy results, and using 104 cases is optimal enough to diagnose 6 illnesses shown with average results Similarity to 20 cases is 90%

Implementation Case Based Reasoning in Determining the Rational Prescription of TB Drugs

Case Based Reasoning has been widely applied in many artificial intelligence, expert systems and well shaped decision support systems that help decision-makers to take the right decisions. The use of CBR to diagnose the disease have also been made by several previous researchers. Yet another problem arises, namely the use of drugs that are not rational

Case Based Reasoning Dan Similarity Untuk Memprediksi Kondisi Keuangan Perusahaan

Financial report is on of the tools to help managing the companies. Financial report can give clear picture about how good executives manage the company. Financial ratio is the measurement to know how the company daily operation and help to analyze situation within the organizations.Case based reasoning is one of problem solving methods which use past data to help solve current or future problems. Nearest Neighbor is one of the method in Case based reasoning to test similarity within past data and tested data. In this research, Case based reasoning is tested and implemented to predict future financial reports. This research use sample data from public company which is listed in Indonesia Stock Exchange. Some financial ratios are used to help future company financial condition.This study found that Case based reasoning is one of the method to predict company financial ratio. With Case based reasoning, it gives a clear description to find appropriate and suitable case as a reference. Keywords— Case based reasoning, Financial Forecasting, Financial Time Series, Bursa Efek Indonesi

Case based reasoning as a model for cognitive artificial intelligence.

Cognitive Systems understand the world through learning and experience. Case Based Reasoning (CBR) systems naturally capture knowledge as experiences in memory and they are able to learn new experiences to retain in their memory. CBR's retrieve and reuse reasoning is also knowledge-rich because of its nearest neighbour retrieval and analogy-based adaptation of retrieved solutions. CBR is particularly suited to domains where there is no well-defined theory, because they have a memory of experiences of what happened, rather than why/how it happened. CBR's assumption that 'similar problems have similar solutions' enables it to understand the contexts for its experiences and the 'bigger picture' from clusters of cases, but also where its similarity assumption is challenged. Here we explore cognition and meta-cognition for CBR through self-refl ection and introspection of both memory and retrieve and reuse reasoning. Our idea is to embed and exploit cognitive functionality such as insight, intuition and curiosity within CBR to drive robust, and even explainable, intelligence that will achieve problemsolving in challenging, complex, dynamic domains

Case Based Reasoning untuk Diagnosis Penyakit Jantung Menggunakan Metode Minkowski Distance

Case Based Reasoning is a computer system that used for reasoning old knowledge to solve new problems. It works by looking at the closest old case to the new case. This research attempts to establish a system of CBR  for diagnosing heart disease. The diagnosis process  is done by inserting new cases containing symptoms into the system, then  the similarity value calculation between cases  uses the minkowski distance similarity. Case taken is the case with the highest similarity value. If a case does not succeed in the diagnosis or threshold less than 0.80, the case will be revised by experts. Revised successful cases are stored to add the system knowledge. Method with the best diagnostic result accuracy will be used in building the CBR system for heart disease diagnosis. The test results using medical records data validated by expert indicate that the system is able to recognize diseases heart using minskowski distance similarity correctly of 100 percent. Using minkowski get accuracy of 100 percent.  Keywords : Case Based Reasoning, Minkowski Distance Similarity

Dynamics of Inductive Inference in a Unified Framework

We present a model of inductive inference that includes, as special cases, Bayesian reasoning, case-based reasoning, and rule-based reasoning. This unified framework allows us to examine, positively or normatively, how the various modes of inductive inference can be combined and how their relative weights change endogenously. We establish conditions under which an agent who does not know the structure of the data generating process will decrease, over the course of her reasoning, the weight of credence put on Bayesian vs. non-Bayesian reasoning. We show that even random data can make certain theories seem plausible and hence increase the weight of rule-based vs. case-based reasoning, leading the agent in some cases to cycle between being rule-based and case-based. We identify conditions under which minmax regret criteria will not be effective.Induction, Bayesian updating, Case-Based Reasoning, Inference