scholarly journals PEMBOBOTAN MENGGUNAKAN PAIRWISE COMPARISON PADA CASE BASED REASONING REKOMENDASI HOTEL

2021 ◽  
Vol 4 (1) ◽  
pp. 9
Author(s):  
Kukuh Tri Nur Iman ◽  
Setyawan Wibisono
Keyword(s):  
Pairwise Comparison ◽  
Nearest Neighbors ◽  
Case Based Reasoning ◽  
K Nearest Neighbors ◽  
Case Based

Meningkatnya pertumbuhan hotel di kota Semarang, maka akan mengakibatkan peningkatan terhadap pilihan hotel di kota Semarang. Setiap hotel memiliki penawaran layanan yang berbeda-beda seperti kelas hotel dan fasilitas yang terdapat di hotel tersebut. Untuk memudahkan dalam pemilihan hotel dibutuhkan sistem rekomendasi yang bisa digunakan dalam memilih hotel di kota Semarang. Dalam mengembangkan penelitian ini, digunakan model penalaran dengan metode Case Based Reasoning (CBR). Metode CBR berguna untuk membuat pilihan rekomendasi hotel terbaik dengan cara membandingkan antara fasilitas-fasilitas yang dikehendaki dengan fasilitas-fasilitas yang dimiliki oleh setiap hotel. Fasilitas hotel dikategorikan dalam tiga kelompok, yaitu: fasilitas utama, fasilitas umum dan fasilitas tambahan. Setiap fasilitas diberikan bobot secara subjektif dengan menentukan kelebihpentingan antara satu fasilitas dibandingkan dengan fasilitas yang lain. Dalam penentuan kelebihpentingan setiap fasilitas tetap mempertimbangkan penilaian umum dalam bidang perhotelan Nilai-nilai subjektif tersebut diuji validitasnya menggunakan metode pairwise comparison. Pada penelitian ini hasil perhitungan pairwise comparison didapatkan bahwa bobot untuk fasilitas utama sebesar 0,63, bobot untuk fasilitas umum sebesar 0,24 serta bobot untuk fasilitas tambahan sebesar 0,13.Hasil perbandingan pada CBR akan dihitung nilai kedekatannya menggunakan  algoritma similaritas K–Nearest Neighbors (KNN) sehingga akan memberikan nilai kemiripan antara parameter dan hasil rekomendasi pemilihan hotel. Hasil nilai akhir similaritas berada dalam rentang antara 0 sampai dengan  1. Sistem ini akan merekomendasikan beberapa hotel dengan similaritas lebih dari 0,4 sedangkan similaritas kurang dari  0,4 akan akan ditambahkan ke dalam tabel revise supaya bisa dicarikan solusi.

scholarly journals CASE BASED REASONING MENGGUNAKAN ALGORITMA K-NEAREST NEIGHBORS UNTUK PENANGANAN PENYAKIT IKAN CUPANG HIAS

2020 ◽  
Vol 15 (2) ◽  
Author(s):  
Ariyono Setiawan ◽  
Barep J. A. I. Nahusuly ◽  
Fitri Aulia Yuliandi Putri ◽  
Askara Raditya ◽  
I Gede Susrama Mas Diyasa
Keyword(s):  
Nearest Neighbors ◽  
Case Based Reasoning ◽  
K Nearest Neighbors ◽  
Case Based

scholarly journals Hybrid approach of the fuzzy C-Means and the K-Nearest neighbors methods during the retrieve phase of dynamic case based reasoning for personalized Follow-up of learners in real time

2019 ◽  
Vol 9 (6) ◽  
pp. 4939
Author(s):  
EL Ghouch Nihad ◽  
En-Naimi El Mokhtar ◽  
Zouhair Abdelhamid ◽  
Al Achhab Mohammed
Keyword(s):  
Machine Learning ◽  
Adaptive Learning ◽  
Nearest Neighbors ◽  
Case Based Reasoning ◽  
Machine Learning Method ◽  
Learning Method ◽  
K Nearest Neighbors ◽  
Initial Learning ◽  
Dynamic Case ◽  
Case Based

<span>The goal of adaptive learning systems is to help the learner achieve their goals and guide their learning. These systems make it possible to adapt the presentation of learning resources according to learners' needs, characteristics and learning styles, by offering them personalized courses. We propose an approach to an adaptive learning system that takes into account the initial learning profile based on Felder Silverman's learning style model in order to propose an initial learning path and the dynamic change of his behavior during the learning process using the Incremental Dynamic Case Based Reasoning approach to monitor and control its behavior in real time, based on the successful experiences of other learners, to personalize the learning. These learner experiences are grouped into homogeneous classes at the behavioral level, using the Fuzzy C-Means unsupervised machine learning method to facilitate the search for learners with similar behaviors using the supervised machine learning method K- Nearest Neighbors.</span>

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.

scholarly journals Using Item Response Theory for Explainable Machine Learning in Predicting Mortality in the Intensive Care Unit: Case-Based Approach

10.2196/20268 ◽  
2020 ◽  
Vol 22 (9) ◽  
pp. e20268
Author(s):  
Adrienne Kline ◽  
Theresa Kline ◽  
Zahra Shakeri Hossein Abad ◽  
Joon Lee
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Intensive Care Unit ◽  
Logistic Regression ◽  
Intensive Care ◽  
Item Response ◽  
Nearest Neighbors ◽  
K Nearest Neighbors ◽  
Linear Discriminant ◽  
Case Based

Background Supervised machine learning (ML) is being featured in the health care literature with study results frequently reported using metrics such as accuracy, sensitivity, specificity, recall, or F1 score. Although each metric provides a different perspective on the performance, they remain to be overall measures for the whole sample, discounting the uniqueness of each case or patient. Intuitively, we know that all cases are not equal, but the present evaluative approaches do not take case difficulty into account. Objective A more case-based, comprehensive approach is warranted to assess supervised ML outcomes and forms the rationale for this study. This study aims to demonstrate how the item response theory (IRT) can be used to stratify the data based on how difficult each case is to classify, independent of the outcome measure of interest (eg, accuracy). This stratification allows the evaluation of ML classifiers to take the form of a distribution rather than a single scalar value. Methods Two large, public intensive care unit data sets, Medical Information Mart for Intensive Care III and electronic intensive care unit, were used to showcase this method in predicting mortality. For each data set, a balanced sample (n=8078 and n=21,940, respectively) and an imbalanced sample (n=12,117 and n=32,910, respectively) were drawn. A 2-parameter logistic model was used to provide scores for each case. Several ML algorithms were used in the demonstration to classify cases based on their health-related features: logistic regression, linear discriminant analysis, K-nearest neighbors, decision tree, naive Bayes, and a neural network. Generalized linear mixed model analyses were used to assess the effects of case difficulty strata, ML algorithm, and the interaction between them in predicting accuracy. Results The results showed significant effects (P<.001) for case difficulty strata, ML algorithm, and their interaction in predicting accuracy and illustrated that all classifiers performed better with easier-to-classify cases and that overall the neural network performed best. Significant interactions suggest that cases that fall in the most arduous strata should be handled by logistic regression, linear discriminant analysis, decision tree, or neural network but not by naive Bayes or K-nearest neighbors. Conventional metrics for ML classification have been reported for methodological comparison. Conclusions This demonstration shows that using the IRT is a viable method for understanding the data that are provided to ML algorithms, independent of outcome measures, and highlights how well classifiers differentiate cases of varying difficulty. This method explains which features are indicative of healthy states and why. It enables end users to tailor the classifier that is appropriate to the difficulty level of the patient for personalized medicine.

scholarly journals DETEKSI DINI PENYAKIT BALITA MENGGUNAKAN ALGORITMA SORENSEN BERBOBOT

2021 ◽  
Vol 9 (02) ◽  
pp. 60-67
Author(s):  
Nur Kharisa Umami ◽  
Setyawan Wibisono
Keyword(s):  
Expert System ◽  
Pairwise Comparison ◽  
Comparison Method ◽  
Case Based Reasoning ◽  
Analytic Hierarchy ◽  
Comparison Results ◽  
Similarity Algorithm ◽  
Analytic Hierarchy Process Method ◽  
Hierarchy Process ◽  
Case Based

There are still many parents who do not have sufficient understanding in terms of toddler disease. One way to provide education is the availability of a system that can be used for consultation based on the symptoms of illness experienced by toddlers and the actions needed to overcome them. The system that will be built is an expert system that can relatively provide suggestions for solutions to children's health problems using the Case Based Reasoning (CBR) method. namely an expert system that uses case-based reasoning methods, namely looking for similarities of a disease compared to a disease that has existed before. In this study, the CBR method was combined with a weighting process using the pairwise comparison method which was within the scope of the AHP (Analytic Hierarchy Process) method. In comparing consultations with old diseases that already exist in the system, and looking for similarities from the comparison results, the Sorensen similarity algorithm is used. This study resulted in weights with 3 symptom categories, namely mild symptoms with a weight of 0.09, moderate symptoms with a weight of 0.24 and severe symptoms with a weight of 0.67 and will recommend several diseases with a similarity above 0.5 and diseases with a similarity below 0.5 will be entered into the revise table to find a solution.

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