scholarly journals Particle Swarm Optimization pada Analisa Review Software Antivirus Menggunakan Metode K-Nearest Neighbors

2020 ◽  
Vol 4 (2) ◽  
pp. 123
Author(s):  
Sucitra Sahara ◽  
Rizqi Agung Permana ◽  
Hariyanto Hariyanto
Keyword(s):  
Particle Swarm Optimization ◽  
Text Mining ◽  
Nearest Neighbor ◽  
Particle Swarm ◽  
Optimization Method ◽  
Data Sets ◽  
K Nearest Neighbor ◽  
Process Data ◽  
Data Set ◽  
Swarm Optimization

Abstrak: Virus pada komputer menjadi hal yang membahayakan bagi para pengguna komputer perorangan maupun perusahaan yang telah menerapkan sistem terkomputerisasi. Virus program yang didesain untuk tujuan jahat dapat merusak bagian tertentu dari komputer, bahkan yang paling merugikan adalah dapat merusak data penting pada perusahaan. Dalam hal ini maka diciptakanlah sebuah software anti virus, perkembangan anti virus selalu lebih lambat dari virus itu sendiri, sehingga peneliti akan mengadakan penyeleksian software anti virus pada suatu opini atau berdasarkan komentar masyarakat yang telah menggunakan software anti virus produk tertentu dan dituangkan ke media online seperti komentar pada suatu situs penjualan produk tersebut. Berdasarkan ribuan komentar akan diolah dan dikelompokkan pada jenis kata teks positif dan teks negatif, dan peneliti membuat klasifikasi data dengan menggunakan metode algoritma k-Nearest Neighbor (k-NN), algoritma k-NN adalah salah satu algoritma yang sesuai dalam penelitian kali ini. Peneliti menemukan bahwa algoritma k-NN mampu mengolah data set yang sudah dikelompokan pada teks positif dan negatif khususnya dalam pemilihan teks, dan penerapan metode optimasi Particle Swarm Optimization (PSO) yang dikombinasikan dengan k-NN diharapkan mampu meningkatkan nilai akurasi sehingga datanya lebih kuat dan valid. Sebelum data set diolah menggukanan optimasi PSO hanya menggunakan metode k-NN akurasi data yang diperoleh 70,50% sedangkan hasil akurasi setelah penggunaan metode k-nn dan optimasi PSO didapatkan nilai akurasi sebesar 83,50%. Dapat disimpulkan bahwa penggunaan optimasi PSO dan metode k-NN sangat sesuai pada konsep text mining dan  penyeksian pada data set berupa text. Kata kunci: Analisis Review, Optimasi Particle Swarm Optimization, Metode k-Nearest Neighbor.   Abstract: Viruses on computers become dangerous for individual computer users and companies that have implemented computerized systems. Virus programs that are designed for malicious purposes can damage certain parts of the computer, even the most detrimental is that it can damage important data on the company. In this case an anti-virus software is created, the development of anti-virus is always slower than the virus itself, so researchers will conduct an anti-virus software selection on an opinion or based on public comments that have used a particular product's anti-virus software and poured it into online media such as comment on a product sales site. Of the thousands of comments will be processed and grouped on the type of positive and negative text words, and researchers make data classification using the k-Nearest Neighbor (k-NN) algorithm method, the k-NN algorithm is one of the appropriate algorithms in this study. The researcher found that the k-NN algorithm is able to process data sets that have been grouped in positive and negative texts, especially in text selection, and the application of the Particle Swarm Optimization (PSO) optimization method combined with k-NN is expected to be able to increase the accuracy value so that the data is stronger and valid. Before the data set is processed using PSO optimization only using the k-NN method the accuracy of the data obtained is 70.50% while the accuracy results after the use of the k-nn method and PSO optimization obtained an accuracy value of 83.50%. It can be concluded that the use of PSO optimization and the k-NN method are very compatible with the concept of text mining and correction of text data sets. Keywords: Analysis Review, k-Nearest Neighbor Method, Particle Swarm Optimization optimization

scholarly journals A correlation-based binary particle swarm optimization method for feature selection in human activity recognition

2018 ◽  
Vol 14 (4) ◽  
pp. 155014771877278 ◽  
Author(s):  
Huaijun Wang ◽  
Ruomeng Ke ◽  
Junhuai Li ◽  
Yang An ◽  
Kan Wang ◽  
...  
Keyword(s):  
Feature Selection ◽  
Particle Swarm Optimization ◽  
Activity Recognition ◽  
Human Activity ◽  
Nearest Neighbor ◽  
Particle Swarm ◽  
Optimization Method ◽  
Binary Particle Swarm Optimization ◽  
K Nearest Neighbor ◽  
Swarm Optimization

Effective feature selection determines the efficiency and accuracy of a learning process, which is essential in human activity recognition. In existing works, for simplification purposes, feature selection algorithms are mostly based on the assumption of feature independence. However, in some scenarios, the optimization method based on this independence hypothesis results in poor recognition performance. This article proposes a correlation-based binary particle swarm optimization method for feature selection in human activity recognition. In the proposed algorithm, the particle swarm optimization algorithm is no longer used as a black box. Meanwhile, correlation coefficients among the features are added to binary particle swarm optimization as a feature correlation factor to determine the position of particles, so that the feature with more information is more likely to be selected. The k-nearest neighbor classifier is then used as the fitness function in the particle swarm optimization to evaluate the performance of the feature subset, that is, feature combination with the highest k-nearest neighbor classifier recognition rate would be picked as the eigenvector. Experimental results show that the proposed method can work well with six classifiers, namely, J48, random forest, k-nearest neighbor, multilayer perceptron, naïve Bayesian, and support vector machine, and the new algorithm can improve the classification accuracy in the OPPORTUNITY Activity Recognition dataset.

scholarly journals Determination of Reactivity Ratios from Binary Copolymerization Using the k-Nearest Neighbor Non-Parametric Regression

Polymers ◽  
2021 ◽  
Vol 13 (21) ◽  
pp. 3811
Author(s):  
Iosif Sorin Fazakas-Anca ◽  
Arina Modrea ◽  
Sorin Vlase
Keyword(s):  
Experimental Data ◽  
Nearest Neighbor ◽  
Optimization Method ◽  
Reactivity Ratios ◽  
Data Sets ◽  
K Nearest Neighbor ◽  
Integration Algorithm ◽  
Data Set ◽  
Parametric Regression ◽  
Non Parametric

This paper proposes a new method for calculating the monomer reactivity ratios for binary copolymerization based on the terminal model. The original optimization method involves a numerical integration algorithm and an optimization algorithm based on k-nearest neighbour non-parametric regression. The calculation method has been tested on simulated and experimental data sets, at low (<10%), medium (10–35%) and high conversions (>40%), yielding reactivity ratios in a good agreement with the usual methods such as intersection, Fineman–Ross, reverse Fineman–Ross, Kelen–Tüdös, extended Kelen–Tüdös and the error in variable method. The experimental data sets used in this comparative analysis are copolymerization of 2-(N-phthalimido) ethyl acrylate with 1-vinyl-2-pyrolidone for low conversion, copolymerization of isoprene with glycidyl methacrylate for medium conversion and copolymerization of N-isopropylacrylamide with N,N-dimethylacrylamide for high conversion. Also, the possibility to estimate experimental errors from a single experimental data set formed by n experimental data is shown.

Data-driven method based on particle swarm optimization and k-nearest neighbor regression for estimating capacity of lithium-ion battery

2014 ◽  
Vol 129 ◽  
pp. 49-55 ◽  
Author(s):  
Chao Hu ◽  
Gaurav Jain ◽  
Puqiang Zhang ◽  
Craig Schmidt ◽  
Parthasarathy Gomadam ◽  
...  
Keyword(s):  
Particle Swarm Optimization ◽  
Lithium Ion Battery ◽  
Nearest Neighbor ◽  
Particle Swarm ◽  
Lithium Ion ◽  
Data Driven ◽  
K Nearest Neighbor ◽  
Swarm Optimization

scholarly journals K-Nearest Neighbor Berbasis Particle Swarm Optimization untuk Analisis Sentimen Terhadap Tokopedia

2020 ◽  
Vol 6 (2) ◽  
Author(s):  
Dicki Pajri ◽  
Yuyun Umaidah ◽  
Tesa Nur Padilah
Keyword(s):  
Particle Swarm Optimization ◽  
Nearest Neighbor ◽  
Confusion Matrix ◽  
Particle Swarm ◽  
K Nearest Neighbor ◽  
Swarm Optimization ◽  
Evaluation Result ◽  
Source Of Information ◽  
F Measure ◽  
Evaluation Accuracy

Tokopedia is a popular marketplace used by e-commerce in Indonesia. Customers’ perception of Twitter towards Tokopedia can be used as an important source of information and can be processed into useful insights. Sentiment analysis is a solution that can be used to process the customers’ perception using K-Nearest Neighbor based on Particle Swarm Optimization. The purpose of this study is to classify customers’ perception based on positive, neutral, and negative classes. The test is carried out with four different scenarios and k values which are evaluated using a confusion matrix. Evaluation results showed the distribution of the dataset is 90:10 and the value of k = 1 is the best evaluation result, which is 88.11%. The feature selection was used for results by using Particle Swarm Optimization. The Particle Swarm Optimization used 20 iterations and 10 particles. It produced 97.9% the best evaluation accuracy, 96.17% precision, 96.62% recall, and 96.39% f-measure.

scholarly journals Optimasi K-Nearest Neighbor Dengan Particle Swarm Optimization Untuk Memprediksi Harga Komoditi Karet

2018 ◽  
Vol 7-2 ◽  
pp. 172-182
Keyword(s):  
Data Mining ◽  
Particle Swarm Optimization ◽  
Nearest Neighbor ◽  
Particle Swarm ◽  
K Nearest Neighbor ◽  
Swarm Optimization

Komoditi dan harganya karet mengalami perubahan yang fluktuatif dan menunjukkan pola yang tidak stasioner, di sisi lain pengambilan keputusan bisnis memerlukan data yang akurat dan terukur. Algoritma k-NN merupakan algoritma yang merupakan algoritma unsupervised, dan terbukti baik pada data mining. Sedangkan Particle Swarm Optimization (PSO) menunjukkan performa optimasi yang lebih baik dibandingkan dengan metode yang lain. Penelitian ini bertujuan untuk merancang metode prakiraan yang dapat memperkirakan tingkat harga dan volume permintaan untuk TSR 20. Prakiraan dilakukan menggunakan Jaringan Syaraf Tiruan dengan algoritma propagasi balik, dimana data yang digunakan adalah data perkembangan pasar TSR pada bursa berjangka SICOM.. Berdasarkan tiga indikator pelatihan yang dijadikan acuan dalam pemilihan arsitektur terbaik, spesifikasi ke 15 tidak perlu melakukan pelatihan sampai epoch maksimum. Dalam teknik PSO terdapat beberapa cara untuk melakukan pengoptimasian diantaranya meningkatkan bobot atribut terhadap semua atribut atau variable yang dipakai, menseleksi atribut dan fitur seleksi. Hasil penelitian dari prediksi harga komoditi karet dengan menggunakan model k-NN mendapatkan nilai RMSE sebesar 0,087 sedangkan bila menggunakan k-NN yang dioptimasi dengan menggunakan Particle Swarm Optimization didapatkan nilai RMSE sebesar 0,082 lebih baik dibandingkan dengan hanya menggunakan k-NN saja.

scholarly journals Optimasi Algoritma SVM Dan k-NN Berbasis Particle Swarm Optimization Pada Analisis Sentimen Fenomena Tagar #2019GantiPresiden

2020 ◽  
Vol 6 (1) ◽  
pp. 95-102
Author(s):  
Atang Saepudin ◽  
Riska Aryanti ◽  
Eka Fitriani ◽  
Dahlia Dahlia
Keyword(s):  
Support Vector Machine ◽  
Particle Swarm Optimization ◽  
Nearest Neighbor ◽  
Particle Swarm ◽  
Support Vector ◽  
K Nearest Neighbor ◽  
Swarm Optimization

Analisis sentimen adalah proses untuk menentukan konten dataset berbasis teks yang positif atau negatif. Saat ini, opini publik menjadi sumber penting dalam keputusan seseorang dalam menemukan solusi. Algoritma klasifikasi seperti Support Vector Machine (SVM) dan K-Nearest Neighbor (k-NN) diusulkan oleh banyak peneliti untuk digunakan dalam analisis sentimen untuk pendapat ulasan. Namun, klasifikasi sentimen teks memiliki masalah pada banyak atribut yang digunakan dalam dataset. Fitur pemilihan dapat digunakan sebagai proses optimasi untuk mengurangi set fitur asli ke subset yang relatif kecil dari fitur yang secara signifikan meningkatkan akurasi klasifikasi untuk cepat dan efektif. Masalah dalam penelitian ini adalah pemilihan pemilihan fitur untuk meningkatkan nilai akurasi Support Vector Machine (SVM) dan K-Nearest Neighbor (k-NN) dan membandingkan akurasi tertinggi untuk analisis sentimen tweet / komentar yang menggunakan tagar # 2019GantiPresiden. Algoritma perbandingan, SVM menghasilkan akurasi 88,00% dan AUC 0,964, kemudian dibandingkan dengan SVM berdasarkan PSO dengan akurasi 92,75% dan AUC 0,973. Data hasil pengujian untuk akurasi algoritma k-NN adalah 88,50% dan AUC 0,948, kemudian dibandingkan untuk akurasi dengan PSO berbasis k-NN sebesar 75,25% dan AUC 0,768. Hasil pengujian algoritma PSO dapat meningkatkan akurasi SVM, tetapi tidak mampu meningkatkan akurasi algoritma k-NN. Algoritma SVM berbasis PSO terbukti memberikan solusi untuk masalah klasifikasi tweets/ komentar yang menggunakan tagar # 2019GantiPresiden di Twitter agar lebih akurat dan optimal.

scholarly journals Implementasi Particle Swarm Optimization (PSO) pada Analysis Sentiment Review Aplikasi Trafi menggunakan Algoritma Naive Bayes (NB)

2021 ◽  
Vol 7 (1) ◽  
pp. 25-29
Author(s):  
Eka Rini Yulia ◽  
Kusmayanti - Solecha
Keyword(s):  
Particle Swarm Optimization ◽  
Text Mining ◽  
Naive Bayes ◽  
Particle Swarm ◽  
Naïve Bayes ◽  
Data Set ◽  
Swarm Optimization ◽  
Transportation Mode ◽  
The Many ◽  
Transportation Applications

Abstract - The development of transportation applications is now getting bigger so that many vendors compete for business in creating transportation mode applications, starting from the quality and quantity so that it is often questioned. With this, the researcher held a transportation application called Trafi to get opinions or comments on applications from people who had used the application and poured it into online media. Of the many comments reviewed to obtain a set of positive and negative forms of data from the text that the researcher will process. For classification data using Naïve Bayes (NB), NB is one of the most popular algorithms for pattern recognition. Apart from simplicity, the Naive Bayes classifier is a popular machine learning technique for text classification, Particle Swarm Optimization (PSO) which combines with the Naive Bayes classification to improve performance. Before use, optimization with PSO in the data set accuracy obtained was 69.50% and after the combination of Naive Bayes and PSO accuracy was 72.34%. Use PSO and Naïve Bayes according to the concept of text mining which aims to find patterns that exist in text, the activity carried out by text mining here is text classification.Abstract - The development of transportation applications is now getting bigger so that many vendors compete for business in creating transportation mode applications, starting from the quality and quantity so that it is often questioned. With this, the researcher held a transportation application called Trafi to get opinions or comments on applications from people who had used the application and poured it into online media. Of the many comments reviewed to obtain a set of positive and negative forms of data from the text that the researcher will process. For classification data using Naïve Bayes (NB), NB is one of the most popular algorithms for pattern recognition. Apart from simplicity, the Naive Bayes classifier is a popular machine learning technique for text classification, Particle Swarm Optimization (PSO) which combines with the Naive Bayes classification to improve performance. Before use, optimization with PSO in the data set accuracy obtained was 69.50% and after the combination of Naive Bayes and PSO accuracy was 72.34%. Use PSO and Naïve Bayes according to the concept of text mining which aims to find patterns that exist in text, the activity carried out by text mining here is text classification.Keywords: Sentiment Analysis, Android Appstore Product Review, Naive Bayes Algorithm

Sign in / Sign up

Export Citation Format

Share Document