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

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 (intelligent system) yaitu case based reasoning (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. AbstractOne of the reasons for the length of study time for students of the Informatics study program of UPN "Veteran" Jawa Timur 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. It 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). 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 in 141 data case. System 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.

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

IJISTECH (International Journal Of Information System & Technology) ◽

10.30645/ijistech.v5i1.111 ◽

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.

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Case Service System at the Child Welfare Institution using the Case-Based Reasoning Method

International Journal of Electrical, Energy and Power System Engineering ◽

10.31258/ijeepse.3.2.40-45 ◽

2020 ◽

Vol 3 (2) ◽

pp. 40-45

Author(s):

Priyandoko Gigih ◽

Citra Andriani ◽

Istiadi Istiadi ◽

Mohd Helmy Abd Wahab

Keyword(s):

Social Needs ◽

National Standards ◽

Case Based Reasoning ◽

Significant Similarity ◽

Standards Of Living ◽

The Social ◽

Child Problems ◽

Case Data ◽

Welfare Institution ◽

Case Based

The Child Social Welfare Institution (LKSA) is an institution tasked with providing relief services to meet the standards of living, health, education, and the social needs of both individuals and groups. An LKSA Robbani is one of the institutions that help in handling family and child cases by National Standards for Child Care. In solving child problems, the institution must open the previous case data to determine the appropriate solution, such that the service process becomes long. Therefore, an application software is needed that can help the institution to be faster. The developed system is using the Case-Based Reasoning (CBR) Method. The advantage of this method is how to adapt the solutions of the previous case, as well as in seeking similarity in each case that the most significant similarity value is considered the most similar case. The method is very suitable for building the application. The system that was built had to do a trial before being used by the user. The trial result was carried out of the system with manual calculations using the case-based reasoning method. Furthermore, from the results of the trial, it was produced by 87.5%.

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Integrating fuzzy case-based reasoning, parametric and feature-based cost estimation methods for machining process

Journal of Modelling in Management ◽

10.1108/jm2-05-2020-0123 ◽

2021 ◽

Vol ahead-of-print (ahead-of-print) ◽

Author(s):

Fentahun Moges Kasie ◽

Glen Bright

Keyword(s):

Cost Estimation ◽

Intelligent System ◽

Machining Process ◽

Estimation Methods ◽

Case Based Reasoning ◽

Cost Estimates ◽

Content Type ◽

Estimation Techniques ◽

Feature Based ◽

Case Based

Purpose This paper aims to propose an intelligent system that serves as a cost estimator when new part orders are received from customers. Design/methodology/approach The methodologies applied in this study were case-based reasoning (CBR), analytic hierarchy process, rule-based reasoning and fuzzy set theory for case retrieval. The retrieved cases were revised using parametric and feature-based cost estimation techniques. Cases were represented using an object-oriented (OO) approach to characterize them in n-dimensional Euclidean vector space. Findings The proposed cost estimator retrieves historical cases that have the most similar cost estimates to the current new orders. Further, it revises the retrieved cost estimates based on attribute differences between new and retrieved cases using parametric and feature-based cost estimation techniques. Research limitations/implications The proposed system was illustrated using a numerical example by considering different lathe machine operations in a computer-based laboratory environment; however, its applicability was not validated in industrial situations. Originality/value Different intelligent methods were proposed in the past; however, the combination of fuzzy CBR, parametric and feature-oriented methods was not addressed in product cost estimation problems.

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Case based reasoning in criminal intelligence using forensic case data

Science & Justice ◽

10.1016/s1355-0306(03)71760-2 ◽

2003 ◽

Vol 43 (3) ◽

pp. 135-143 ◽

Cited By ~ 43

Author(s):

O. Ribaux ◽

P. Margot

Keyword(s):

Case Based Reasoning ◽

Criminal Intelligence ◽

Forensic Case ◽

Case Data ◽

Case Based

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Case-Based Reasoning for Selecting Study Program in Senior High School

International Journal of Advanced Computer Science and Applications ◽

10.14569/ijacsa.2015.060418 ◽

2015 ◽

Vol 6 (4) ◽

Cited By ~ 1

Author(s):

Sri Mulyana ◽

Sri Hartati ◽

Retantyo Wardoyo ◽

Edi Winarko

Keyword(s):

High School ◽

Senior High School ◽

Case Based Reasoning ◽

Study Program ◽

Case Based

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Sistem Cerdas Permainan Papan The Battle Of Honor dengan Decision Making dan Machine Learning

Jurnal Buana Informatika ◽

10.24002/jbi.v12i2.4905 ◽

2021 ◽

Vol 12 (2) ◽

pp. 136

Author(s):

Arnan Dwika Diasmara ◽

Aditya Wikan Mahastama ◽

Antonius Rachmat Chrismanto

Keyword(s):

Artificial Intelligence ◽

Machine Learning ◽

Decision Making ◽

Intelligent System ◽

Third Party ◽

Case Based Reasoning ◽

Board Game ◽

Rule Based ◽

Case Based ◽

Rule Based Systems

Abstract. Intelligent System of the Battle of Honor Board Game with Decision Making and Machine Learning. The Battle of Honor is a board game where 2 players face each other to bring down their opponent's flag. This game requires a third party to act as the referee because the players cannot see each other's pawns during the game. The solution to this is to implement Rule-Based Systems (RBS) on a system developed with Unity to support the referee's role in making decisions based on the rules of the game. Researchers also develop Artificial Intelligence (AI) as opposed to applying Case-Based reasoning (CBR). The application of CBR is supported by the nearest neighbor algorithm to find cases that have a high degree of similarity. In the basic test, the results of the CBR test were obtained with the highest formulated accuracy of the 3 examiners, namely 97.101%. In testing the AI scenario as a referee, it is analyzed through colliding pieces and gives the right decision in determining victoryKeywords: The Battle of Honor, CBR, RBS, unity, AIAbstrak. The Battle of Honor merupakan permainan papan dimana 2 pemain saling berhadapan untuk menjatuhkan bendera lawannya. Permainan ini membutuhkan pihak ketiga yang berperan sebagai wasit karena pemain yang saling berhadapan tidak dapat saling melihat bidak lawannya. Solusi dari hal tersebut yaitu mengimplementasikan Rule-Based Systems (RBS) pada sistem yang dikembangkan dengan Unity untuk mendukung peran wasit dalam memberikan keputusan berdasarkan aturan permainan. Peneliti juga mengembangkan Artificial Intelligence (AI) sebagai lawan dengan menerapkan Case-Based reasoning (CBR). Penerapan CBR didukung dengan algoritma nearest neighbour untuk mencari kasus yang memiliki tingkat kemiripan yang tinggi. Pada pengujian dasar didapatkan hasil uji CBR dengan accuracy yang dirumuskan tertinggi dari 3 penguji yaitu 97,101%. Pada pengujian skenario AI sebagai wasit dianalisis lewat bidak yang bertabrakan dan memberikan keputusan yang tepat dalam menentukan kemenangan.Kata Kunci: The Battle of Honor, CBR, RBS, unity, AI

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Designing Intelligent Tutoring Systems: A Personalization Strategy using Case-Based Reasoning and Multi-Agent Systems

ADCAIJ ADVANCES IN DISTRIBUTED COMPUTING AND ARTIFICIAL INTELLIGENCE JOURNAL ◽

10.14201/adcaij2013244154 ◽

2013 ◽

Vol 2 (1) ◽

pp. 41-54

Author(s):

Carolina González ◽

Juan Carlos Burguillo ◽

Martín Llamas ◽

Rosalía Laza

Keyword(s):

Intelligent Tutoring Systems ◽

Intelligent Agents ◽

Intelligent System ◽

Intelligent Tutoring ◽

Educational Systems ◽

Case Based Reasoning ◽

Multi Agent Systems ◽

Tutoring Systems ◽

Student Profile ◽

Case Based

Intelligent Tutoring Systems (ITSs) are educational systems that use artificial intelligence techniques for representing the knowledge. ITSs design is often criticized for being a complex and challenging process. In this article, we propose a framework for the ITSs design using Case Based Reasoning (CBR) and Multiagent systems (MAS). The major advantage of using CBR is to allow the intelligent system to propose smart and quick solutions to problems, even in complex domains, avoiding the time necessary to derive those solutions from scratch. The use of intelligent agents and MAS architectures supports the retrieval of similar students models and the adaptation of teaching strategies according to the student profile. We describe deeply how the combination of both technologies helps to simplify the design of new ITSs and personalize the e-learning process for each student

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