scholarly journals Case Based Reasoning using K-Nearest Neighbor with Euclidean Distance for Early Diagnosis of Personality Disorder

2021 ◽  
Vol 5 (1) ◽  
pp. 23
Author(s):  
Anna Hendri Soleliza Jones ◽  
Cicin Hardiyanti
Keyword(s):  
Personality Disorder ◽  
Personality Disorders ◽  
Euclidean Distance ◽  
Nearest Neighbor ◽  
World Health ◽  
Case Based Reasoning ◽  
K Nearest Neighbor ◽  
Professional Psychologists ◽  
Case Data ◽  
Case Based

A personality disorder is a condition of a person with an extreme personality that causes the sufferer to have unhealthy and different thoughts patterns and behavior from other people. The personality disorders discussed in this study consisted of 110 diseases with 300 case data and 68 symptoms. Based on Basic Health Research (Riskesdas) 2018 data, it shows that more than 19 million people aged 15 years and over were affected by mental-emotional disorders. Data from the Statistics Indonesia in 2019 that the population of Indonesia is around 265 million people, while according to the Indonesian Clinical Psychologist Association, the number of verified professional psychologists is 1,599 clinical psychologists out of a total membership of 2,078 as of January 2019. However, this figure does not meet the standards of the World Health Organization (WHO), which is that psychologists serve 30 thousand people. This shows that Indonesia still lacks around 28,970 psychologists. The unequal distribution of professional psychologists has made psychologists need a long time to provide a diagnosis because of the number of patients being inversely proportional to the availability of psychologists in Indonesia. Moreover, there is not enough patient knowledge about the symptoms they feel. This study aims to produce a system for diagnosing personality disorders. This study is a case based reasoning to solve problems that have occurred in previous cases using K-Nearest Neighbor to classify data based on the closest distance using the calculation of the Euclidean Distance. Algorithm testing for the system used the Confusion Matrix test. Based on the results of testing data in the 60 case data using K-nearest Neighbor and the calculation of the Euclidean Distance with a score of K=3, it is known that 60 data have 100% similarity to cases with a personality disorder. Meanwhile, testing new cases with 10 case data that were not in the knowledge base was also conducted showing that 9 cases had 100% similarity to the previous case, while another case had 90% similarity to the previous case.

scholarly journals Penentuan Algoritma Similarity Yang Akurat Pada Sistem Berbasis Cased Based Reasoning Untuk Identifikasi Ikan

2020 ◽  
Vol 19 (2) ◽  
pp. 37-46
Author(s):  
Suhadi . ◽  
Prima Dina Atika ◽  
Panca Indah Lestari ◽  
Afzil Ramadian
Keyword(s):  
Open Access ◽  
Euclidean Distance ◽  
Nearest Neighbor ◽  
Case Based Reasoning ◽  
K Nearest Neighbor ◽  
Open Access Article ◽  
Case Based

Abstrak - Indonesia sebagai negara kepulauan memiliki potensi perikanan yang sangat besar dan beragam, Indonesia memiliki 17.508 pulau dengan garis pantai sepanjang 81.000 km dan 70% (5,8 juta km2) dari luas Indonesia adalah lautan, adapun keragaman sumberdaya laut untuk beragam jenis ikan merupakan ciri tersendiri untuk mengenali dan memahami suatu spesies secara detail. Identifikasi jenis ikan yang bersifat computing masih terbatas, dalam penelitian analisa yang digunakan adalah sistem Case Based Reasoning (CBR). CBR merupakan penalaran berbasis kasus yang mempunyai metode penyelesaian masalah berbasis pengetahuan untuk mempelajari dan memecahkan masalah berdasarkan pengalaman masa lalu. CBR adalah suatu model komputasi untuk meniru penalaran manusia untuk memberikan kemudahan dalam mencari kasus berdasarkan kemiripan, kemudian case based reasoner mencari kasus-kasus yang ada pada basis kasus untuk menemukan kasus yang memiliki kemiripan dengan persoalan yang sedang dihadapi (retrieve). Oleh karena itu, proses CBR sering juga disebut dengan istilah “4 Re” yaitu Retrive, Retain, Revise, Reuse. Dalam paradigma pemecahan masalah sebuah permasalahan baru diselesaikan dengan cara membandingkan dengan kasus-kasus pada dimasa lampau dan menggunakannya kembali kasus-kasus yang ada untuk menyelesaikan suatu masalah sekarang. Tujuan dari penelitian ini adalah untuk melakukan evaluasi dan komparasi Algoritma Weighted Euclidean Distance (WED), Algoritma Hamming and Levenshtein Distances (HLD), Algoritma Cosine Coefficient for Text-Based Cases (CCFTBC) dan Algoritma k-Nearest Neighbor (k-NN) untuk identifikasi jenis ikan. Hasil penelitian ini adalah untuk mencari pemilihan hasil komparasi algoritma pada sistem CBR yang cepat dan akurat. Copyright © 2019 LPPM - STMIK IKMI CirebonThis is an open access article under the CC-BY license 

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.

The Study of Hybrid Gaussian CBR Technique and Using in Camshaft Design System

2010 ◽  
Vol 108-111 ◽  
pp. 603-607
Author(s):  
Wei Yan ◽  
Xue Qing Li ◽  
Xu Guang Tan ◽  
De Hui Tong ◽  
Qi Gao
Keyword(s):  
Expert System ◽  
Decision Model ◽  
Nearest Neighbor ◽  
Design System ◽  
Case Based Reasoning ◽  
K Nearest Neighbor ◽  
Engine Design ◽  
Cam Profile ◽  
Aided Design ◽  
Case Based

In this paper, we propose a hybrid decision model using case-based reasoning augmented the Gaussian and k nearest neighbor methods for aided design camshaft in engine. The hybrid Gaussian k-NN (HGKNN) CBR scheme is designed to compute memberships between cam profile and engine parameters, which provides a more flexible and practical mechanism for reusing the decision knowledge. These methods were implemented in the database application and expert system following the examples of Cam Profile. To get the designed case, the retrieved results were compared and analyzed by HGKNN and k-NN algorithm in the CBR database. It proves the validity of HGKNN and CBR design system is used successfully in engine design process.

scholarly journals Penyelesaian Masalah Pengelolaan Lumbung Pangan Desa Menggunakan Case-Based Reasoning dengan Algoritma K-Nearest Neighbor

2019 ◽  
Vol 11 (1) ◽  
Author(s):  
Mgs. Afriyan Firdaus ◽  
Dwi Rosa Indah ◽  
Putri Eka Sevtiyuni ◽  
Choirunnisa Qonitah
Keyword(s):  
Problem Solving ◽  
Nearest Neighbor ◽  
Technical Problem ◽  
Case Based Reasoning ◽  
Test Results ◽  
K Nearest Neighbor ◽  
Nearest Neighbor Algorithm ◽  
Existing Problems ◽  
K Nearest Neighbor Algorithm ◽  
Case Based

In this paper, we discuss the problem solving of village food barn management using Case-Based Reasoning (CBR) with the K-Nearest Neighbor algorithm. This research was carried out by adopting the stages of the CBR cycle and the nearest neighbor algorithm. The results of the study show that the application of CBR and K-nearest neighbor algorithms can support the resolution of knowledge problems in village food barn management using technical problem solving based on the symptoms and solutions to existing problems. Based on the test results, the problem-solving accuracy was 92%.Keywords - case-based reasoning, K-nearest neighbor, food barn, problem-solving

scholarly journals Multilayer Perceptron Integrated Fuzzy Nearest Neighbor to Improve the Proficiency of CBR-Retrieval

2020 ◽  
Vol 2 (2) ◽  
pp. 101-110
Author(s):  
Dr. Suma V.
Keyword(s):  
Multilayer Perceptron ◽  
Nearest Neighbor ◽  
Case Based Reasoning ◽  
K Nearest Neighbor ◽  
Medical Field ◽  
Data Set ◽  
Standard Data ◽  
Wide Range ◽  
Training Examples ◽  
Case Based

The CBR (case based reasoning) is a problem solving technique following different strategy compared to the major approaches of the artificial intelligence. It develops remedies to certain problem based on the pre-existing solutions of similar nature. So the problem using the CBR is handled by retrieving and reusing the similar previously solved problems and available solutions respectively. This makes the process functioning alike based on the human activities is instinctively attractive and more beneficial compared to the Conventional_AI as begins to reason out the possible solutions form the shallow base. The CBR due to the exceeding performance are popular among a wide range of applications such as the weather fore casting, medical and engineering diagnosis, aerospace etc. Identification or sorting out or classification take a significant role in cases that is the training examples retrieval as the perfect identification results in perfect case retrieval, this further enables the case based reasoning to arrive to at a perfect remedy for the problem. The retrieval of cases are mostly based on the similarity and utilizes the KNN (K-Nearest Neighbor). The proposed method in the paper integrates the multilayer perceptron with the fuzzy nearest neighbor (MLP-NFF) system with the help of WEKA to deliver a perfect classification to make the CBR-retrieval efficient. The evaluation of the proposed method and its comparison with the KNN is done using the standard data set obtained from the medical field.

scholarly journals Penerapan Metode Case Based Reasoning dan K-Nearest Neighbor untuk Diagnosa Penyakit dan Hama pada Tanaman Karet

2020 ◽  
Vol 6 (1) ◽  
pp. 23
Author(s):  
Heni Sulistiani ◽  
Imam Darwanto ◽  
Imam Ahmad
Keyword(s):  
Nearest Neighbor ◽  
Case Based Reasoning ◽  
K Nearest Neighbor ◽  
Case Base ◽  
Case Base Reasoning ◽  
Case Based

Petani karet di wilayah Kabupaten Tulang Bawang sering menemukan masalah seperti penyakit dan hama tanaman karet yang dapat mengakibatkan kematian pada tanaman karet, antara lain penyakit pada bidang sadap, dan hama penggangu seperti rayap dan kutu tanaman. Penyakit tersebut dapat dideteksi melalui gejala-gejala yang ditimbulkan. Akan tetapi untuk mengetahui jenis penyakit yang menyerang tanaman karet diperlukan seorang pakar pertanian dan perkebunan. Namun, saat ini petani di Tulang Bawang masih memliki kekurangan dalam hal pengetahuan untuk pencegehan dan penanganan penyakit tanaman karet. Untuk itu, diperlukan suatu sistem yang berisikan pengetahuan tertentu dalam hal kepakaran melalui pendekatan kemampuan manusia di salah  satu  bidang. Salah satunya adalah sistem pakar. Berbagai metode telah diterapkan untuk membangun sistem pakar, diantaranya adalah Metode Case Base Reasoning dan K-Nearest Neighbor. Metode ini digunakan untuk mencari solusi dari permasalahan berdasarkan pengalaman kasus masa lalu dan pendekatan untuk mencari kasus dengan menghitung kedekatan antara kasus baru dengan kasus lama. Hasil pengujian keakuratan kesesuaian antara data testing yang diperoleh dari pakar dengan hasil pengolahan sistem adalah sebesar 80%.

A Recommendation System for Hotel in Bali Using Case Based Reasoning Method

2021 ◽  
Vol 10 (1) ◽  
pp. 11
Author(s):  
Ni Putu Vidya Vira Prashanti ◽  
I Gede Santi Astawa
Keyword(s):  
Recommendation System ◽  
Nearest Neighbor ◽  
Case Based Reasoning ◽  
Air Conditioner ◽  
K Nearest Neighbor ◽  
Tourist Destinations ◽  
Black Box Testing ◽  
Class Room ◽  
The Right ◽  
Case Based

When on vacation, one of the essential amenities that is needed is the availability of accommodation such as hotel. One of the areas that become a holiday destination is the island of Bali. Being one of the tourist destinations of course many lodgings available in Bali with various facilities offered. The problem facing tourists is when choosing the right lodging that suits your wishes or needs, so in this study will be built a hotel recommendation system in Bali. The purpose of this research is to assist tourists in choosing the right hotel. The study used 78 hotel data sourced from the agoda.com. The methods used in this study are Case Based Reasoning and K-Nearest Neighbor.  The result of this study is that the hotel recommendation system in Bali has managed to provide hotel selection recommendations based on 14 features namely district, hotel class, room type, and availability of facilities such as breakfast, swimming pool, television, gym, air conditioner, scenery, and nightly stay price. Based on black box testing, it is obtained the result that the hotel recommendation system in Bali can function properly.   

scholarly journals Case based Reasoning Prediksi Waktu Studi Mahasiswa Menggunakan Metode Euclidean Distance dan Normalisasi Min-Max

2020 ◽  
Vol 7 (6) ◽  
pp. 1279
Author(s):  
Henni Endah Wahanani ◽  
Made Hanindia Prami Swari ◽  
Fawwaz Ali Akbar
Keyword(s):  
Euclidean Distance ◽  
Intelligent System ◽  
Accurate Method ◽  
Case Based Reasoning ◽  
Study Program ◽  
A Value ◽  
Student Graduation ◽  
Case Data ◽  
Precision Testing ◽  
Case Based

<p>Salah satu penyebab dari lamanya waktu tempuh mahsiswa di Jurusan Informatika UPN “Veteran” Jawa Timur adalah sullitnya memantau kemajuan studi mahasiswa secara seksama, mengingat jumlah mahasiswa yang cukup banyak serta pihak akademik belum memiliki metode yang akurat untuk memetakan mahasiswa yang diprediksi akan mengalami keterlambatan dalam penyelesaian studinya. Melalui perkembangan teknologi informasi yang berkembang pesat saat ini, maka sangat dimungkinkan untuk membuat sebuah sistem yang mampu memprediksi kemungkinan keterlambatan kelulusan mahasiswa melalui penggunaan berbagai metode komputasi yang ada. Salah satu pendekatan yang dapat digunakan untuk membuat sebuah sistem prediksi kelulusan adalah menggunakan pendekatan populer yang digunakan dalam pembuatan sistem cerdas <em>(intelligent system) </em>yaitu <em>case based reasoning </em>(CBR). Langkah-langkah yang dilakukan pada penelitian ini adalah melakukan pengumpulan dan memasukkan data kasus pada basis kasus, melakukan praprosesing yakni normalisasi atribut yang akan digunakan dalam perhitungan similartitas antar kasus menggunakan normalisasi min-max, implementasi CBR menggunakan metode Euclidean Distance, serta melakukan pengujian pada 141 data kasus. Dari sisi perhitungan akurasi, sistem mampu memberikan nilai akurasi paling tinggi sebesar 100% pada pada pengujian berdasarkan predikat kelulusan, sedangkan berdasarkan ketepatan waktu, sistem mampu memberikan akurasi tertinggi dengan nilai 85,71%, dan sistem mampu memberikan nilai akurasi tertinggi sebesar 71,43% pada pengujian berdasarkan massa studi. Untuk pengujian presisi, sistem mampu mengasilkan nilai terbesar berturut-turut sebesar 90,90%, 43,33%, dan 100%. Sedangkan pada pengujian sensitivitas, sistem berturut-turut mampu menghasilan nilai sebesar 90,90%, 40,48%, dan 100%. Hasil pengujian ini tentunya sangat bergantung dari basis kasus yang dimiliki, oleh sebab itu perbaikan dan peningkatan jumlah kasus yang dimiliki diharapkan mampu meningkatkan performa sistem rekomendasi.</p><p> </p><p><strong><em>Abstract</em></strong></p><p class="Judul2"><em>One of the reasons for the length of study time for students of the Informatics study program of UPN "Veteran" </em><em>Jawa Timur</em><em> is the difficulty of monitoring the progressy, given the large number of students and academics do not have an accurate method to map students who are predicted to experience delays. </em><em>I</em><em>t is possible to create a system that is able to predict the possibility of student graduation delay through the use of various existing computational methods. One approach that can be used to create a graduation prediction system is to use the popular approach namely case based reasoning (CB).</em><em> </em><em>The steps taken in this study are collecting and entering case data, normalizing the attributes using min-max normalization, implementing CBR using the Euclidean Distance, and system testing</em><em> in 141 data case</em><em>.</em><em> </em><em>Sy</em><em>stem is able to provide the highest accuracy value of 100% in testing based on the predicate of graduation, while based on timeliness, the system is able to provide the highest accuracy value with a value of 85.71%, and the system is able to provide the highest accuracy value of 71.43%. on testing based on the study period. For precision testing, the system was able to produce the largest values of 90.90%, 43.33% and 100%, respectively. Whereas in the sensitivity test, the system was able to produce values of 90.90%, 40.48%, and 100% respectively. The results of this test are of course very dependent on the basis of cases that are owned, therefore improvements and an increase in the number of cases owned are expected to be able to improve the performance.</em></p><p><strong><em><br /></em></strong></p>

scholarly journals Aircraft Troubleshooting Optimization Using Case-based Reasoning and Decision Analysis

2020 ◽  
Vol 12 (1) ◽  
pp. 8
Author(s):  
Gustavo Borba Evangelista ◽  
Guilherme Conceição Rocha ◽  
Wlamir Olivares Loesch Vianna
Keyword(s):  
Decision Analysis ◽  
Field Experience ◽  
Nearest Neighbor ◽  
Fault Isolation ◽  
Machine Learning Algorithms ◽  
Data Driven ◽  
Support Vector ◽  
Case Based Reasoning ◽  
K Nearest Neighbor ◽  
Case Based

The Fault Isolation Manual (FIM) can be seen as a specialist system that carries the expectations and expertise of engineers and technical team concerning the aircraft components and systems operation. It is basically a manual that supports the maintainers regarding the actions to perform in determined situations to properly isolate a fault. Although the FIM is the most common tool that assists maintainer on the troubleshooting process today, it does not adequately consider field experience and it does not explore situations where the maintenance operator has limited resources, such as a lack of tools and equipment. These drawbacks are essentially caused by the lack of flexibility or adaptability of this method since it is a static manual. There are several dynamic methods studied in the field of system troubleshooting and aircraft maintenance such as Artificial Neural Networks, Support Vector Machine, K Nearest Neighbor and many other machine learning algorithms. These techniques are considered very powerful and useful; however, the training process of the data-driven strategies requires a large amount of data to provide a reliable result. In this context, the present work proposes a combination of data-driven with legacy knowledge-based approaches. The following techniques are employed to integrate the concepts mentioned: decision trees that explore the legacy knowledge with its topology based on the FIM, truth tables and decision analysis that explores Bayes’ rule to assist the decision- making process and case-based reasoning, technique that enables the learning from the field experience.

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