scholarly journals An Enhanced Hybrid Intrusion Detection Mechanism Based on Chicken Swarm Optimization and Naïve-Bayes Method

2019 ◽  
Vol 8 (3) ◽  
pp. 5906-5910
Keyword(s):  
Intrusion Detection ◽  
Naive Bayes ◽  
Naïve Bayes ◽  
Naive Bayes Classifier ◽  
Bayes Classifier ◽  
Naïve Bayes Classifier ◽  
Detection Mechanism ◽  
Swarm Optimization ◽  
Network Intrusion ◽  
Chicken Swarm Optimization

The major important factor of network intrusion detection is to avoid malicious process in network. Since, existing modules are out-dated because of improper authentication and the network may get affected because of new attacks and malwares. In this research, Hybrid module is formed by using Chicken Swarm Optimization and Naive Bayes classifier (HCSONB) for classification of intrusion data. The hybrid method is introduced to detect the features efficiently in complex dataset because strategy which is designed to be capable of detecting huge data in network. Some traditional methods results in serious limitations in case of complex datasets. The algorithms are shared their properties together to discover better optimization results and the classification precisions values. This paper examines the feature selection performance by utilizing NSLKDD-99 dataset and comparing it with the Swarm Intelligence (SI), Naïve-Bayes classifier and proposed HCSO-NB algorithms. The proposed classification process designed in NETBEANS 8.2 tool. Experiments show that proposed HCSO-NB successfully improved the accuracy

scholarly journals Analisis Sentiment pada Sosial Media Twitter Menggunakan Naїve Bayes Classifier dengan Feature Selection Particle Swarm Optimization dan Term Frequency

2017 ◽  
Vol 2 (1) ◽  
pp. 14
Author(s):  
Yono Cahyono
Keyword(s):  
Feature Selection ◽  
Particle Swarm Optimization ◽  
Naive Bayes ◽  
Particle Swarm ◽  
Naïve Bayes ◽  
Naive Bayes Classifier ◽  
Bayes Classifier ◽  
Naïve Bayes Classifier ◽  
Swarm Optimization ◽  
Term Frequency

Pengguna media sosial saat ini sangat besar; dimana setiap orang mengungkapkan pendapat; komentar; kritik dan lain-lain. Data tersebut memberikan informasi yang berharga untuk dapat membantu orang atau organisasi dalam pengambilan keputusan. Jumlah data yang sangat besar tidak mungkin bagi manusia untuk membaca dan menganalisis secara manual. Ansalisis Sentiment merupakan proses dalam menganalisis; memahami; dan mengklasifikasi pendapat; evaluasi; penilaian; sikap; dan emosi terhadap suatu entitas tertentu seperti produk; jasa; organisasi; individu; peristiwa; topik; guna mendapatkan informasi. Penelitian ini bertujuan untuk memisahkan tweets berbahasa Indonesia pada media sosial twitter kedalam kategori positif; negatif dan netral. Metode naїve bayes Classifier (NBC) dengan feature selection Particle Swarm Optimization (PSO) diterapkan pada dataset untuk mengurangi atribut yang kurang relevan pada saat proses klasifikasi. Hasil pengujian menunjukan bahwa algoritma Naïve Bayes Classifier dengan feature selection Particle Swarm Optimization (PSO) menggunakan parameter term frequency (TF) dengan akurasi 97;48%.

scholarly journals Analisis Sentimen Vaksinasi Covid-19 Pada Twitter Menggunakan Naive Bayes Classifier Dengan Feature Selection Chi-Squared Statistic dan Particle Swarm Optimization

2021 ◽  
Vol 5 (1) ◽  
pp. 49-56
Author(s):  
Ristasari Dwi Septiana ◽  
Agung Budi Susanto ◽  
Tukiyat Tukiyat
Keyword(s):  
Feature Selection ◽  
Particle Swarm Optimization ◽  
Naive Bayes ◽  
Particle Swarm ◽  
Naïve Bayes ◽  
Naive Bayes Classifier ◽  
Bayes Classifier ◽  
Naïve Bayes Classifier ◽  
Swarm Optimization ◽  
Chi Squared

Tingginya penyebaran Covid-19 semakin berdampak pada bidang kesehatan, ekonomi, bahkan bidang pendidikan di Indonesia, sehingga pemerintah Indonesia melakukan tindakan vaksinasi Covid-19 guna menekan tingkat penyebaran Covid-19 di Indonesia. Namun hal tersebut dinilai kotroversial sehingga menarik perhatian masyarakat untuk memberikan opini di berbagai media seperti media sosial twitter. Sehingga membutuhkan analisa sentimen masyarakat terhadap upaya pemerintah pada tindakan vaksinasi Covid-19 untuk mencapai hasil prediksi dengan nilai akurasi paling optimal. Proses crawling secara otomatis menggunakan tools Rapidminer akan mengambil data tweets yang mengandung 5 (lima) kata kunci, yaitu “Vaksin Sinovac”, “Vaksin Astrazeneca”, “Vaksin Moderna”, “Vaksin Merah Putih”, dan “Vaksinasi Covid-19”. Dataset tweets didapatkan dari tanggal 4 Agustus 2021 sampai 12 Agustus 2021. Dataset diperoleh sejumlah 2060 tweets dan diberi label secara manual didapatkan jumlah tweet sebanyak 1193 sentimen positif, 73 negatif, dan 794 netral. Data tersebut dianalisa dengan menggunakan Metode Feature Selection Chi-Squared Statistic dan Particle Swarm Optimization (PSO) untuk mengurangi atribut yang kurang relevan pada saat proses klasifikasi dengan algoritma Naive Bayes Classifier (NBC). Hasil pengujian menunjukan bahwa Algoritma Naive Bayes Classifier (NBC) tanpa Feature Selection mendapatkan nilai akurasi 63,69%. Hasil penelitian menunjukkan bahwa Algoritma Naive Bayes Classifier (NBC) dengan Feature Selection Chi-Squared Statistic mempunyai tingkat akurasi 69,13%. Sedangkan hasil pengujian algoritma Naive Bayes Classifier (NBC) dengan Particle Swarm Optimization mempunyai tingkat akurasi 66,02%. Dengan demikian hasil seleksi fitur Chi-Squared Statistic mendapatkan nilai akurasi yang lebih baik jika dibandingkan dengan Particle Swarm Optimization untuk proses klasifikasi algoritma Naive Bayes Classifier (NBC) dengan selisih akurasi 3,11%.

scholarly journals SENTIMENT ANALYSIS OF DIGITAL WALLET SERVICE USERS USING NAÏVE BAYES CLASSIFIER AND PARTICLE SWARM OPTIMIZATION

2020 ◽  
Vol 2 (4) ◽  
pp. 241-250
Author(s):  
Alvie Delia Cahyani ◽  
Tati Mardiana
Keyword(s):  
Particle Swarm Optimization ◽  
Sentiment Analysis ◽  
Naive Bayes ◽  
Naïve Bayes ◽  
Service Users ◽  
Naive Bayes Classifier ◽  
Bayes Classifier ◽  
Naïve Bayes Classifier ◽  
Swarm Optimization ◽  
Digital Wallet

Digital wallet services provide many conveniences and benefits to its users. However, not all digital wallet service users have a positive opinion of the service. Sentiment analysis in this study aims to determine the opinions given by Dana and Isaku digital wallet service users whether they contain positive or negative opinions and apply the Naïve Bayes Classifier and Particle Swarm Optimization (PSO) method to the sentiment analysis of digital wallet service users. The Naïve Bayes Classifier method is used because it is simple, fast, high accuracy, and has good enough performance to classify data, but the Naïve Bayes Classifier has the disadvantage that each independent variable is assumed to cause a decrease in the accuracy value. Therefore, this research added an attribute weighting method, namely Particle Swarm Optimization (PSO) to increase the accuracy of the classification of the Naïve Bayes Classifier. This study uses data taken from Twitter as many as 490 tweet data. The test results using the confusion matrix and ROC curve show an increase in accuracy of the Naïve Bayes Classifier Dana digital wallet from 60.00% to 91.67% and I.Saku digital wallet from 53.23% to 85.00%. T-Test and Anova test results show that the two classification methods tested have significant (significant) differences in Accuracy values.

scholarly journals Optimization the Naive Bayes Classifier Method to diagnose diabetes Mellitus

2019 ◽  
Vol 1 (1) ◽  
pp. 78-86
Author(s):  
Desi Susilawati Susilawati ◽  
Dwiza Riana
Keyword(s):  
Diabetes Mellitus ◽  
Particle Swarm Optimization ◽  
Naive Bayes ◽  
Particle Swarm ◽  
Naïve Bayes ◽  
Diabetes Mellitus Type ◽  
Naive Bayes Classifier ◽  
Bayes Classifier ◽  
Naïve Bayes Classifier ◽  
Swarm Optimization

World Health Organization (WHO) states that Diabetes Mellitus is the world's top deadly  disease. several studies in the health sector including diabetes mellitus have been carried out to detect diseases early. In this study  optimization of naive bayes classifier using particle swarm optimization was applied to the data of patients with 2 classes namely positive diabetes mellitus and negative diabetes  mellitus and data on patients with 3 classes, those who tested positive for diabetes mellitus type 1, diabetes mellitus type 2 and negative diabetes mellitus. After testing, the algorithm of Naive Bayes Classifier and Naive Bayes Classifier based on Particle Swarm Optimization, the results obtained are the Naive Bayes Classifier method for 2 classes and 3 classes each producing an accuracy value of 78.88% and 68.50%. but after adding Particle Swarm Optimization the value of accuracy increased respectively to 82.58% and 71, 29%. The classification results for 2 classes have an accuracy value higher than 3 classes with a difference of 11.29%

scholarly journals PREDIKSI PENYAKIT GINJAL KRONIS MENGGUNAKAN ALGORITMA NAIVE BAYES CLASSIFIER BERBASIS PARTICLE SWARM OPTIMIZATION

2019 ◽  
Vol 13 (1) ◽  
pp. 26-30
Author(s):  
Toni Arifin ◽  
Daniel Ariesta
Keyword(s):  
Particle Swarm Optimization ◽  
Naive Bayes ◽  
Confusion Matrix ◽  
Particle Swarm ◽  
Naïve Bayes ◽  
Naive Bayes Classifier ◽  
Bayes Classifier ◽  
Naïve Bayes Classifier ◽  
Swarm Optimization ◽  
Missing Value

Penyakit ginjal kronis (PGK) merupakan masalah kesehatan masyarakat global dengan prevalens dan insidens gagal ginjal yang meningkat, prognosis yang buruk dan biaya yang tinggi. nilai prevalensi di seluruh Indonesia untuk penyakit gagal ginjal memiliki nilai rata - rata berkisar kurang lebih 0.2 persen. Langkah pertama dalam pengelolaan penyakit ginjal adalah penetapan diagnosis yang tepat. Maka dibutuhkan sebuah metode untuk memprediksi penyakit ginjalkronis. Naïve Bayes memiliki beberapa kelebihan, yaitu cepat dalam perhitungan, algoritma yang sederhana dan berakurasi tinggi. Naïve Bayes Classifier lebih tepat diterapkan pada data yang besar dan dapat menangani data yang tidak lengkap (missing value) serta kuat terhadap atribut yang tidak relevan dan noise pada data. Untuk meningkatkan akurasi maka digunakan Particle Swarm Optimization untuk pembobotan atribut. Dari hasil penelitian Naive Bayes Classification berbasis Particle Swarm Optimization memiliki akurasi confusion matrix sebesar 98,75% dan AUC sebesar 99%. sedangkan Naive Bayes memiliki akurasi confusion matrix  97.00% dan AUC sebesar 99.8%.

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