scholarly journals STRATEGI PEMASARAN PRODUK INDUSTRI KREATIF MENGGUNAKAN ALGORITMA K-MEANS CLUSTERING BERBASIS PARTICLE SWARM OPTIMIZATION

2021 ◽  
Vol 15 (2) ◽  
pp. 1-13
Author(s):  
Shanti Maulani ◽  
Oding Herdiana ◽  
Eryan Ahmad Firdaus
Keyword(s):  
Data Mining ◽  
Particle Swarm Optimization ◽  
Selection Process ◽  
Continuous Process ◽  
Particle Swarm ◽  
Support Vector ◽  
Swarm Optimization ◽  
Time Efficiency ◽  
Svm Algorithm ◽  
Hidden Data

The existence of abundant UMKM data sources can be used to dig up information. Classification is one of the techniques to explore hidden data owned by data mining. Data mining classification methods, one of which is the Support Vector Machine (SVM) algorithm. The SVM algorithm has proven better results than the KKN, Decision Tree and Linear Regression algorithms. In the classification process, the accuracy and time efficiency results obtained are very important. So optimization is needed in order to increase accuracy and time efficiency during the classification process. The optimization of the SVM algorithm was carried out using the K-Means algorithm for the clustering and continuous process on UMKM data and the feature selection process using Particle Swarm Optimization (PSO). This paper aims to optimize the accuracy of the data in the form of type of business, business and turnover. From the results of the discussion of the SVM method using K-Means and PSO, it gives an average accuracy of 55% but 0.12% lower than SVM just using PSO. Keywords: UMKM, Clustering, K-Means, SVM, PSO

scholarly journals Prediction of Successful Elearning Based on Activity Logs with Selection of Support Vector Machine based on Particle Swarm Optimization

2019 ◽  
Vol 2 (1) ◽  
Author(s):  
Elin Panca Saputra ◽  
Sukmawati Angreani Putri ◽  
Indriyanti Indriyanti
Keyword(s):  
Support Vector Machine ◽  
Particle Swarm Optimization ◽  
Particle Swarm ◽  
Attribute Selection ◽  
Support Vector ◽  
Swarm Optimization ◽  
Student Activities ◽  
Svm Algorithm ◽  
The Right ◽  
Selection Of

Prediction is a systematic estimate that identifies past and future information, we predict the success of learning with elearning based on a log of student activities. In our current study we use the Support vector machine (SVM) method which is comparable with Particle Swarm Optimization. It is known that SVM has a very good generalization that can solve a problem. however, some of the attributes in the data can reduce accuracy and add complexity to the Support Vector Machine (SVM) algorithm. It is necessary for existing tribute selection, therefore using the Particle swarm optimization (PSO) method is applied to the right attribute selection in determining the success of elearning learning based on student activity logs, because with the Swarm Optimization (PSO) method can increase accuracy in determining selection of attributes.

scholarly journals Performance Analysis of Particle Swarm Optimization for Feature Selection

2019 ◽  
Vol 4 (1) ◽  
Author(s):  
Amos O Bajeh ◽  
Bukola O Funso ◽  
Fatima E Usman-Hamza
Keyword(s):  
Data Mining ◽  
Feature Selection ◽  
Particle Swarm Optimization ◽  
Sentiment Analysis ◽  
Particle Swarm ◽  
Space Complexity ◽  
Support Vector ◽  
Marginal Effect ◽  
Features Selection ◽  
Swarm Optimization

One of the key task in data mining is the selection of relevant features from datasets with high dimensionality. This is expected to reduce the time and space complexity, and consequently improve the performance of data mining algorithms for tasks such as classification. This study presents an empirical study of the effect of particle swarm optimization as a feature selection technique on the performance of classification algorithms. Two dataset from different domains were used: SMS spam detection and sentiment analysis datasets. Particle swarm optimization is applied on the datasets for feature selection. Both the reduced and raw dataset are separately classified using C4.5 decision tree, k-nearest neighbour and support vector machine. The result of the analysis showed that the improvement of classifier performance is case-dependent; some significant improvements are noticed in the sentiment analysis datasets and not in the SMS spam dataset. Although some marginal effect are observed on performance, it implies that with particle swarm optimization features selection the space complexity is reduced while maintaining the accuracy of the classifiers. Keywords—classification, feature selection, machine learning, particle swarm optimization, text mining   

scholarly journals Forecasting Inflation Rate Using Support Vector Regression (SVR) Based Weight Attribute Particle Swarm Optimization (WAPSO)

2018 ◽  
Vol 5 (2) ◽  
pp. 118-127
Author(s):  
Erlin Mega Priliani ◽  
Anggyi Trisnawan Putra ◽  
Much Aziz Muslim
Keyword(s):  
Data Mining ◽  
Particle Swarm Optimization ◽  
Support Vector Regression ◽  
Inflation Rate ◽  
Optimization Technique ◽  
Consumer Price Index ◽  
Particle Swarm ◽  
Support Vector ◽  
Swarm Optimization ◽  
Forecasting Methods

Data mining is the process of finding patterns or interesting information in selected data by using a particular technique or method. Utilization of data mining one of which is forecasting. Various forecasting methods have progressed along with technological developments. Support Vector Regression (SVR) is one of the forecasting methods that can be used to predict inflation. The level of accuracy of forecasting is determined by the precision of parameter selection for SVR. Determination of these parameters can be done by optimization, to obtain optimal forecasting of SVR method. The optimization technique used is Weight Attribute Particle Swarm Optimization (WAPSO). The use of WAPSO can find optimal SVR parameters, so as to improve the accuracy of forecasting. The purpose of this research is to implement SVR and SVR-WAPSO to predict the inflation rate based on Consumer Price Index (CPI) and to know the level of accuracy. The data used in this study is CPI Semarang City period January 2010-February 2018. Implementation experiments using Netbeans 8.2 gives results, SVR method has an accuracy of 94.654%. SVR-WAPSO method has an accuracy of 97.459%. Thus, the SVR-WAPSO method can increase the accuracy of 2,805% of a single SVR method for inflation rate forecasting. This research can be used as a reference for the next researcher can make improvements in determining the range of SVR parameters to get the value of each parameter more effective and efficient to get more optimal accuracy.

scholarly journals Penerapan Metode Particle Swarm Optimization Pada Optimasi Prediksi Pemasaran Langsung

2018 ◽  
Vol 5 (1) ◽  
pp. 1-11 ◽  
Author(s):  
Yuni Eka Achyani
Keyword(s):  
Data Mining ◽  
Support Vector Machine ◽  
Particle Swarm Optimization ◽  
Direct Marketing ◽  
Particle Swarm ◽  
Fair Value ◽  
Attribute Selection ◽  
Support Vector ◽  
Swarm Optimization ◽  
Auc Value

Abstrak Dalam persaingan ketat saat ini, promosi yang baik dapat memberikan kredibilitas untuk produk baru. Promosi perlu mendapat perhatian lebih dan serius, karena dalam kehidupan sehari-hari timbul produk unggulan, jika tidak mengetahuinya, kemungkinan produk yang ditawarkan kepada konsumen kurang ditanggapi oleh pasar, oleh karena itu perusahaan harus mengupayakan produknya, meyakinkan dan mempengaruhi konsumen untuk menciptakan permintaan akan produk ini. Langkah yang bisa dilakukan oleh perusahaan untuk melakukannya adalah dengan melakukan pemasaran langsung. Peningkatan akurasi prediksi pemasaran langsung dapat dilakukan dengan cara melakukan seleksi terhadap atribut, karena seleksi atribut mengurangi dimensi dari data sehingga operasi algoritma data mining dapat berjalan lebih efektif dan lebih cepat. Dalam penelitian ini akan digunakan metode support vector machine dan akan dilakukan seleksi atribut dengan menggunakan particle swarm optimization untuk prediksi pemasaran langsung. Setelah dilakukan pengujian maka hasil yang didapat adalah support vector machine menghasilkan nilai akurasi sebesar 88,71 %, nilai precision 89,47% dan nilai AUC sebesar 0,896. Kemudian dilakukan seleksi atribut dengan menggunakan particle swarm optimization dimana atribut yang semula berjumlah 16 variabel prediktor terpilih 12 atribut yang digunakan. Hasil menunjukkan nilai akurasi yang lebih tinggi yaitu sebesar 89,38%, nilai precision 89,89% dan nilai AUC sebesar 0,909 dengan nilai akurasi klasifikasi sangat baik (excellent clasiffication). Sehingga dicapai peningkatan akurasi sebesar 0,67 %, dan peningkatan AUC sebesar 0,013. Kata Kunci: Particle Swarm Optimization, Pemasaran Langsung, Seleksi Atribut Abstract In the current intense competition a good promotion can provide credibility for a new product. Promotion needs to get more attention and serious, because in everyday life arise a prime product, if not find out, the possibility of products offered to consumers less responded by the market, therefore the company should strive for its products. , convincing, and influencing consumers to create demand for these products. Steps that can be done by the company to do so is to do direct marketing. Increased accuracy of direct marketing predictions can be done by selecting attributes, because of the selection. Data mining can run more effectively and quickly. In this study the method to be used is. With particle swarm optimization for direct marketing prediction optimization. After testing, the results obtained are support vector engine yield value of 88.71%, precision value 89.47% and AUC value of 0.896. Then the attribute selection is done using particle swarm optimization where the original attribute uses 16 predictor variables selected 12 attributes used. The results showed a higher value of 89.38%, 89.89% accuracy and AUC value of 0.909 with very good fair value (excellent classification). The price increase is 0.67%, and the increase of AUC is 0,013. Keywords: Particle Swarm Optimization, Direct Marketing, Selection Attributes.

scholarly journals Segmentation and abnormality detection of cervical cancer cells using fast elm with particle swarm optimization

Genetika ◽  
2015 ◽  
Vol 47 (3) ◽  
pp. 863-876 ◽  
Author(s):  
P. Sukumar ◽  
R.K. Gnanamurthy
Keyword(s):  
Cervical Cancer ◽  
Particle Swarm Optimization ◽  
Cancer Cells ◽  
Selection Process ◽  
Particle Swarm ◽  
Experimental Result ◽  
Support Vector ◽  
Swarm Optimization ◽  
Cervical Cancer Cells ◽  
Cervical Cells

Cervical cancer arises when the anomalous cells on the cervix mature unmanageable obviously in the renovation sector. The most probably used methods to detect abnormal cervical cells are the routine and there is no difference between the abnormal and normal nuclei. So that the abnormal nuclei found are brown in color while normal nuclei are blue in color. The spread or cells are examined and the image denoising is performed based on the Iterative Decision Based Algorithm. Image Segmentation is the method of paneling a digital image into compound sections. The major utilize of segmentation is to abridge or modify the demonstration of an image. The images are segmented by applying anisotropic diffusion on the Denoised image. Image can be enhanced using dark stretching to increase the quality of the image. It separates the cells into all nuclei region and abnormal nuclei region. The abnormal nuclei regions are further classified into touching and non-touching regions and touching regions undergoes feature selection process. The existing Support Vector Machines (SVM) is classified few nuclei regions but the time to taken for execution is high. The abnormality detected from the image is calculated as 45% from the total abnormal nuclei. Thus the proposed method of Fast Particle Swarm Optimization with Extreme Learning Machines (Fast PSO-ELM) to classify all nuclei regions further into touching region and separated region. The iterative method for to training the ELM and make it more efficient than the SVM method. In experimental result, the proposed method of Fast PSO-ELM may shows the accuracy as above 90% and execution time is calculated based on the abnormality (ratio of abnormal nuclei regions to all nuclei regions) image. Therefore, Fast PSO-ELM helps to detect the cervical cancer cells with maximum accuracy.

scholarly journals A Novel Method for Matching Reservoir Parameters Based on Particle Swarm Optimization and Support Vector Machine

2020 ◽  
Vol 2020 ◽  
pp. 1-10
Author(s):  
Rongwang Yin ◽  
Qingyu Li ◽  
Peichao Li ◽  
Detang Lu
Keyword(s):  
Support Vector Machine ◽  
Particle Swarm Optimization ◽  
Physical Properties ◽  
Particle Swarm ◽  
Oil Reservoirs ◽  
Support Vector ◽  
Swarm Optimization ◽  
Svm Algorithm ◽  
Potential Applications ◽  
Novel Method

When the reservoir physical properties are distributed very dispersedly, the matching precision of these reservoir parameters is not good. We propose a novel method for matching the reservoir physical properties based on particle swarm optimization (PSO) and support vector machine (SVM) algorithm. First, the data structure characteristics of the reservoir physical properties are analyzed. Then, the particle swarm differential perturbation evolution algorithm is used to cluster and characterize the reservoir physical properties. Finally, by using the SVM algorithm for feature reorganization and the least squares matching of the extracted reservoir physical properties, the feature quantity of the reservoir physical properties can be accurately mined and the pressure matching precision is improved. The experimental results show that employing the proposed method to analyze and sample the data characteristics of the physical properties of the reservoir is better. The extracted parameters can effectively reflect the physical characteristics of oil reservoirs. The proposed method has potential applications in guiding the exploration and development of oil reservoirs.

Analysis of Feature Reduction Techniques for Online News Popularity Prediction

2018 ◽  
Vol 4 (10) ◽  
pp. 6
Author(s):  
Shivangi Bhargava ◽  
Dr. Shivnath Ghosh
Keyword(s):  
Neural Network ◽  
Support Vector Machine ◽  
Particle Swarm Optimization ◽  
Naive Bayes ◽  
Particle Swarm ◽  
Naïve Bayes ◽  
Online News ◽  
Feature Reduction ◽  
Support Vector ◽  
Swarm Optimization

News popularity is the maximum growth of attention given for particular news article. The popularity of online news depends on various factors such as the number of social media, the number of visitor comments, the number of Likes, etc. It is therefore necessary to build an automatic decision support system to predict the popularity of the news as it will help in business intelligence too. The work presented in this study aims to find the best model to predict the popularity of online news using machine learning methods. In this work, the result analysis is performed by applying Co-relation algorithm, particle swarm optimization and principal component analysis. For performance evaluation support vector machine, naïve bayes, k-nearest neighbor and neural network classifiers are used to classify the popular and unpopular data. From the experimental results, it is observed that support vector machine and naïve bayes outperforms better with co-relation algorithm as well as k-NN and neural network outperforms better with particle swarm optimization.

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