154 research outputs found
DISKRITISASI EQUAL-WIDTH INTERVAL PADA NAÏVE BAYES (STUDI KASUS: KLASIFIKASI PASIEN TBC)
Klasifikasi Naive Bayes merupakan teknik untuk memprediksi probabilitas keanggotaan suatu kelas dengan menerapkan teorema Bayes. Klasifikasi Naive Bayes akan lebih baik jika menggunakan data yang berbentuk kategorik, sehingga dalam penelitian ini digunakan diskritisasi equal-width interval pada Naive Bayes untuk mengubah data yang berbentuk numerik menjadi kategorik. Tujuan dari penelitian ini adalah untuk menerapkan metode Naive Bayes dengan diskritisasi equal-width interval dalam mengklasifikasi pasien TBC di Puskesmas Sewon 1. Hasil penelitian ini menunjukkan akurasi sebesar 100% dengan perbandingan data training dan data testing sebesar 80%:20% dan 90%:10%, sehingga klasifikasi Naive Bayes dapat dikategorikan baik dalam mengklasifikasi pasien TBC
Distance Functions Study in Fuzzy C-Means Core and Reduct Clustering
Fuzzy C-Means is a distance-based clustering process which applied by fuzzy logic concept. Clustering process worked in linear to the iteration process to minimizing the objective function. The objective function is an addition of the multiplication between the coordinates distance towards their closest cluster centroid and their membership degree. The more the iteration process, the objective function should get lower and lower. The objective of this research is to observe whether the distances which usually applied are able to fulfill the aforementioned hypothesis for determining the most suitable distance for Fuzzy C-Means clustering application. Few distance function was applied in the same dataset. 5 standard datasets and 2 random datasets were used to test the fuzzy c-means clustering performance with the 7 different distance function. Accuracy, purity, and Rand Index also applied to measure the quality of the resulted cluster. The observation result depicted that the distance function which resulted in the best quality of clusters are Euclidean, Average, Manhattan, Minkowski, Minkowski-Chebisev, and Canberra distance. These 6 distances were able to fulfill the basic hypothesis of the objective function behavior on Fuzzy C-Means Clustering method. The only distance who were not able to fulfill the basic hypothesis is Chebisev distance
Comparasional of the M, MM and S Estimatory in Rabust Regression Analysis on Indonesian Literacy Index Data 2018
Regression analysis is a method used to determine the relationship between one dependent variable and one or more independent variables. However, the existence of outliers in the 2018 Community Literacy Development Index data led to the application of statistical methods not sensitive to pencils for analysis. This was the reason for adopting robust regression methods, including the M, S, and MM estimations. Therefore, this research aims to compare these three estimates and select the one with the best estimate based on the parameter estimation model associated with the RSE and R2 values. Descriptive and inferential analysis with robust regression was used due to several outlier data and to provide good regression model results with unbiased values. It was discovered that the S-estimator and MM-estimator are the best methods because they have the most minor Residual Standard Error (RSE) of 1.856 and R2 of 0.9778
Implementation of Takagi Sugeno Kang Fuzzy with Rough Set Theory And Mini-Batch Gradient Descent Uniform Regularization
The Takagi Sugeno Kang (TSK) fuzzy approach is popular since its output is either a constant or a
function. Parameter identification and structure identification are the two key requirements for
building the TSK fuzzy system. The input utilized in fuzzy TSK can have an impact on the number
of rules produced in such a way that employing more data dimensions typically results in more rules,
which causes rule complexity. This issue can be solved by employing a dimension reduction
technique that reduces the number of dimensions in the data. After that, the resulting rules are
improved with MBGD (Mini-Batch Gradient Descent), which is then altered with uniform
regularization (UR). UR can enhance the classifier's fuzzy TSK generalization performance. This
study looks at how the rough sets method can be used to reduce data dimensions and use Mini Batch
Gradient Descent Uniform Regularization (MBGD-UR) to optimize the rules that come from TSK.
252 respondents' body fat data were utilized as the input, and the mean absolute percentage error
(MAPE) was used to analyze the results. Jupyter Notebook software and the Python programming
language are used for data processing. The analysis revealed that the MAPE value was 37%, falling
into the moderate are
An epidemic model with viral mutations and vaccine interventions
Prediction is a means of forecasting a future value by using and analyzing historical or current data. A popular neural network
architecture used as a prediction model is the Recurrent Neural Network (RNN) because of its wide application and very high
generalization performance. This study aims to improve the RNN prediction model’s accuracy using k-means grouping and
PCA dimension reduction methods by comparing the five distance functions. Data were processed using Python software
and the results obtained from the PCA calculation yielded three new variables or principal components out of the five
examined. This study used an optimized RNN prediction model with k-means clustering by comparing the Euclidean,
Manhattan, Canberra, Average, and Chebyshev distance functions as a measure of data grouping similarity to avoid being
trapped in the local optimal solution. In addition, PCA dimension reduction was also used in facilitating multivariate data
analysis. The k-means grouping showed that the most optimal distance is the average function producing a DBI value of
0.60855 and converging at the 9th iteration. The RNN prediction model results evaluated based on the number of RMSE
errors which was 0.83, while that of MAPE was 8.62%. Therefore, it was concluded that the K-means and PCA methods
generated a more optimal prediction model for the RNN metho
Optimization of markov weighted fuzzy time series forecasting using genetic algorithm (GA) AND particle Swarm Optimization (PSO)
The Markov Weighted Fuzzy Time Series (MWFTS) is a method for making predictions based on
developing a fuzzy time series (FTS) algorithm. The MWTS has overcome certain limitations of
FTS, such as repetition of fuzzy logic relationships and weight considerations of fuzzy logic
relationships. The main challenge of the MWFTS method is the absence of standardized rules for
determining partition intervals. This study compares the MWFTS model to the partition methods
Genetic Algorithm-Fuzzy K-Medoids clustering (GA-FKM) and Fuzzy K-Medoids clusteringParticle Swarm Optimization (FKM-PSO) to solve the problem of determining the partition interval
and develop an algorithm. Optimal partition optimization. The GA optimization algorithm’s
performance on GA-FKM depends on optimizing the clustering of FKM to obtain the most
significant partition interval. Implementing the PSO optimization algorithm on FKM-PSO involves
maximizing the interval length following the FKM procedure. The proposed method was applied to
Anand Vihar, India’s air quality data. The MWFTS method combined with the GA-FKM
partitioning method reduced the mean absolute square error (MAPE) from 17.440 to 16.85%. While
the results of forecasting using the MWFTS method in conjunction with the FKM-PSO partition
method were able to reduce the MAPE percentage from 9.78% to 7.58%, the MAPE percentage was
still 9.78%. Initially, the root mean square error (RMSE) score for the GA-FKM partitioning
technique was 48,179 to 47,01. After applying the FKM-PSO method, the initial RMSE score of
30,638 was reduced to 24,863
Fuzzy Support Vector Machine Using Function Linear Membership and Exponential with Mahanalobis Distance
Support vector machine (SVM) is one of effective biner classification technic with structural risk minimization (SRM) principle. SVM method is known as one of successful method in classification technic. But the real-life data problem lies in the occurrence of noise and outlier. Noise will create confusion for the SVM when the data is being processed. On this research, SVM is being developed by adding its fuzzy membership function to lessen the noise and outlier effect in data when trying to figure out the hyperplane solution. Distance calculation is also being considered while determining fuzzy value because it is a basic thing in determining the proximity between data elements, which in general is built depending on the distance between the point into the real class mass center. Fuzzy support vector machine (FSVM) uses Mahalanobis distances with the goal of finding the best hyperplane by separating data between defined classes. The data used will be going over trial for several dividing partition percentage transforming into training set and testing set. Although theoretically FSVM is able to overcome noise and outliers, the results show that the accuracy of FSVM, namely 0.017170689 and 0.018668421, is lower than the accuracy of the classical SVM method, which is 0.018838348. The existence of fuzzy membership function is extremely influential in deciding the best hyperplane. Based on that, determining the correct fuzzy membership is critical in FSVM problem
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