scholarly journals ANALISIS PEMANTAUAN LAN MENGGUNAKAN METODE QoS DAN PENGKLASIFIKASIAN STATUS JARINGAN INTERNET MENGGUNAKAN ALGORITMA NAIVE BAYES

2018 ◽  
Vol 4 (2) ◽  
Author(s):  
Sachin Sabloak ◽  
Jasuandi Wijaya ◽  
Abdul Rahman ◽  
Molavi Arman
Keyword(s):  
Quality Of Service ◽  
Naive Bayes ◽  
Naïve Bayes ◽  
The Internet ◽  
Data Set ◽  
Computer Laboratory ◽  
The Status ◽  
Bayes Algorithm ◽  
Internet Network

[Id]Pentingnya jaringan komputer pada kehidupan sekarang, perlu adanya kestabilan jaringan komputer yang digunakan. Pemantauan kualitas jaringan internet didalam sebuah jaringan LAN dilakukan network administrator untuk mendapatkan nilai dari data yang didapat, penelitian ini menerapkan algoritma Naive Bayes menggunakan dataset TIPHON dengan parameter yang terdapat dalam metode QoS yaitu delay, packetloss dan jitter untuk memonitor kualitas jaringan internet. Metode QoS akan menghasilkan nilai dari setiap parameter yang dibutuhkan untuk pemantauan jaringan, guna mendapatkan kesimpulan mengenai status jaringan internet digunakan Algoritma Naive Bayes. Metode Quality of Service (QoS) merupakan sebuah metode yang digunakan dalam mendefinisikan kemampuan suatu jaringan yang ?digunakan untuk pengukuran tentang kualitas ?jaringan. Penggunaan algoritma Naive Bayes diperlukan karena algoritma tersebut digunakan dalam pengklasifikasian yang menggunakan probabilitas dan statistik serta mampu mengambil keputusan dengan menggunakan dataset yang telah disediakan. Tujuan penelitian ini dilakukan untuk mengetahui status jaringan internet di lab komputer STMIK Global Informatika MDP serta mengetahui tingkat akurasi dari algoritma Naive Bayes untuk mengklasifikasikan status jaringan internet. Pengujian penelitian dilakukan di lab komputer STMIK Global Informatika MDP. Hasil pengujian dalam penelitian ini menunjukkan bahwa akurasi Naive Bayes yang didapatkan sebesar 87,78% dan status jaringan internet di lab komputer STMIK Global Informatika MDP masuk ke dalam kategori memuaskan dengan nilai dominan yaitu sebesar 47,78%.Kata Kunci: Naive Bayes, network administrator, Quality of Service (QoS), status jaringan internet.[En]Since computer network is very important nowadays, it needs the stability of the network used. Monitoring the quality of the internet network in LAN is conducted by an administrator to get the value of the data obtained. This research applied Naive Bayes algorithm using TIPHON data set with parameters in QoS method; delay, packetloss and jitter, to monitor the quality of the internet network. QoS method will gain value in every parameter needed for network monitoring. To get a conclusion about the status of the internet network, Naive Bayes algorithm was used. Quality of Service (QoS) method is a method used to define the ability of a network to measure its quality. Naive Bayes algorithm is needed since the algorithm is used in classifying using probability and statistic as well as making decision using dataset provided. This research is conducted to see the status of the internet network in STMIK Global Informatika MDP computer laboratory and to know the level of accuracy of Naive Bayes algorithm to classify the status of the network. The research was conducted in STMIK Global Informatika MDP computer laboratory. The result of the research showed that the accuracy of Naive Bayes was 87,78% and the status of the internet network STMIK Global Informatika MDP was in the category of satisfactory with dominant value 47,78%.

scholarly journals IMPLEMENTASI ALGORITMA NAIVE BAYES UNTUK MEMPREDIKSI FREKUENSI TUNAI PADA MESIN ATM DI MASA TRANSISI PEMBATASAN SOSIAL BERSKALA BESAR (PSBB) PANDEMI COVID-19

2021 ◽  
Vol 4 (1) ◽  
pp. 47-52
Author(s):  
Saptari Wijaya Mulia ◽  
Sujiharno Sujiharno ◽  
Arief Wibowo
Keyword(s):  
Quality Of Service ◽  
Prediction Accuracy ◽  
Large Scale ◽  
Naive Bayes ◽  
Naïve Bayes ◽  
Seasonal Factors ◽  
Bayes Algorithm ◽  
The Right

Determining the need of money for ATM is usually different, that is one of the problems in managing money allocation of ATM. Some seasonal factors such as holidays and the implementation of transition large-scale social restrictions related to the covid-19 pandemic that can affect fluctuations in cash transactions. In this paper aims to determine the frequency of cash withdrawals at ATM since the enactment of transition large-scale social restrictions in Jakarta using the naive bayes algorithm so it can be identified which ATM require more allocation money or not. Providing the right money allocation can improve the quality of service to customers and minimize unused money in ATM. Results of analysis using a Naive Bayes algorithm to predict cash withdrawals frequencies at ATM that show a prediction accuracy up to 81%

scholarly journals An Intrusion Detection Model based on Hybrid Classification algorithm

2018 ◽  
Vol 246 ◽  
pp. 03027
Author(s):  
Manfu Ma ◽  
Wei Deng ◽  
Hongtong Liu ◽  
Xinmiao Yun
Keyword(s):  
Intrusion Detection ◽  
Detection Rate ◽  
Naive Bayes ◽  
Naïve Bayes ◽  
Classification Algorithm ◽  
Data Set ◽  
Detection Model ◽  
Performance Requirements ◽  
Hybrid Classification ◽  
Bayes Algorithm

Due to using the single classification algorithm can not meet the performance requirements of intrusion detection, combined with the numerical value of KNN and the advantage of naive Bayes in the structure of data, an intrusion detection model KNN-NB based on KNN and Naive Bayes hybrid classification algorithm is proposed. The model first preprocesses the NSL-KDD intrusion detection data set. And then by exploiting the advantages of KNN algorithm in data values, the model calculates the distance between the samples according to the feature items and selects the K sample data with the smallest distance. Finally, by naive Bayes to get the final result. The experimental results on the NSL-KDD dataset show that the KNN-NB algorithm can meet the requirement of balanced performance than the traditional KNN and Naive Bayes algorithm in term of accuracy, sensitivity, false detection rate, specificity, and missed detection rate.

scholarly journals Analysis Quality of Service Wireless LAN at University PGRI Yogyakarta

2018 ◽  
Vol 6 (1) ◽  
pp. 9
Author(s):  
Nur Kukuh Wicaksono ◽  
Bambang Sugiantoro
Keyword(s):  
Quality Of Service ◽  
Packet Loss ◽  
Wireless Lan ◽  
Educational Institution ◽  
The Internet ◽  
Average Delay ◽  
Observation Methods ◽  
The University ◽  
Internet Network

PGRI University of Yogyakarta is an educational institution that uses the internet as one of the supporting facilities and infrastructures to manage and organize the data and information used by the student to find references about the lecture. PGRI University Yogyakarta has three buildings on the main campus building A building B and C buildings, where each building using wireless LAN as a means for students to use the internet network, the weakness of the wireless LAN network where poor internet network in the wireless LAN network. Thus the researchers wanted to analyze the Quality of Service wireless LAN networks in building A, building B, and C buildings, in each floor.With the existence of quality of the network at PGRI University of Yogyakarta will be done by interviews and observation methods, problems that occur in wireless LAN networks in each building have been prepared in advance, after which it will do an analysis of wireless LAN networks using quality of service parameters, namely delay, packet loss, bandwidth, throughput and factors that influence the wireless network at the University of PGRI Yogyakarta.The results of the measurement and monitoring of Quality of Service wireless LAN at PGRI University of Yogyakarta in building A, building B, C on each floor of the building can be classified in the category of poor with the average delay for each building to around 150 ms and packet loss = 28%, bandwidth = 173523 bits / s and throughput = 22%, and the factors that occurred in the signal range cannot cover every room in every building. From these results it can be concluded that the quality of the wireless LAN at the University PGRI Yogyakarta according to the TIPHON standards categorized as poor.

scholarly journals Implemented PSO-NBC and PSO-SVM to Help Determine Status of Volcanoes

2019 ◽  
Vol 9 (2) ◽  
pp. 97
Author(s):  
Firman Tempola
Keyword(s):  
Particle Swarm Optimization ◽  
Naive Bayes ◽  
Particle Swarm ◽  
Naïve Bayes ◽  
Bayes Classifier ◽  
Naïve Bayes Classifier ◽  
Swarm Optimization ◽  
Attribute Weights ◽  
The Status ◽  
Bayes Algorithm

<p class="JGI-AbstractIsi">This research is a continuation of previous research that applied the Naive Bayes classifier algorithm to predict the status of volcanoes in Indonesia based on seismic factors. There are five attributes used in predicting the status of volcanoes, namely the status of the normal, standby and alerts. The results Showed the accuracy of the resulted prediction was only 79.31%, or fell into fair classification. To overcome these weaknesses and in order to increase accuracy, optimization is done by giving criteria or attribute weights using particle swarm optimization. This research compared the optimization of Naive Bayes algorithm to vector machine support using particle swarm optimization. The research found improvement on system after application of PSO-NBC to that of 91.3 % and 92.86% after applying PSO-SVM.</p>

scholarly journals The Comparison of Data Mining Methods Using C4.5 Algorithm and Naive Bayes in Predicting Heart Disease

Tech-E ◽  
2021 ◽  
Vol 4 (2) ◽  
pp. 44
Author(s):  
Rino Rino
Keyword(s):  
Data Mining ◽  
Heart Disease ◽  
Naive Bayes ◽  
Naïve Bayes ◽  
Data Set ◽  
A Value ◽  
C4.5 Algorithm ◽  
Calculation Results ◽  
Mining Methods ◽  
Bayes Algorithm

Heart disease is a condition of the presence of fatty deposits in the coronary arteries in the heart which changes the role and shape of the arteries so that blood flow to the heart is obstructed. Data mining methods can predict this disease, some of the methods are C4.5 Algorithm and Naive Bayes which are often used in research.The data set in this research was obtained from the uci machine learning repository site, where the dataset has 3546 records and 13 attributes.The accuracy value of the Naïve Bayes algorithm has a high value of 81.40% compared to the C4.5 algorithm which only has an accuracy value of 79.07%. Based on the calculation results, it can be concluded that the Naïve Bayes Algorithm is a very good clarification because it has a value between 0.709 - 1.00.From conclusion above, the Naïve Bayes algorithm has a higher accuracy value than the C4.5 algorithm so the researchers decided to use the Naïve Bayes algorithm in predicting heart disease.

scholarly journals The Use of Naive Bayes for Broiler Digestive Tract Disease Detection

2019 ◽  
Vol 3 (01) ◽  
pp. 1-7
Author(s):  
Hindriyanto Dwi Purnomo
Keyword(s):  
Evaluation Method ◽  
Naive Bayes ◽  
Confusion Matrix ◽  
Gastrointestinal Diseases ◽  
Naïve Bayes ◽  
Common Disease ◽  
High Productivity ◽  
Bayes Algorithm ◽  
Tract Disease

Broiler chicken is a species of chicken that have high productivity. In order to get a good quality of chicken, good treatments of the breeding factors is needed, so the chicken will not easily infected by diseases. Gastrointestinal diseases are common disease that infects chickens. The mortality level caused by gastrointestinal diseases is considered high. This study is designed to address the problem by developing a system using the Naive Bayes algorithm. 60 chicken data samples were used, and the result shows that Naive Bayes might be used to detect gastrointestinal diseases among chickens with accuracy level of 93.3%. The number was confirmed by using confusion matrix evaluation method, and gave same level of accuracy compared to the expert judgments. 

scholarly journals Analisa Perbandingan Quality Of Service Antara Protokol PPTP dan L2TP Pada Virtual Private Network Berbasis Router Mikrotik

2019 ◽  
Vol 10 (2) ◽  
Author(s):  
Dahnial Dahnial
Keyword(s):  
Quality Of Service ◽  
Packet Loss ◽  
Virtual Private Network ◽  
The Internet ◽  
Public Network ◽  
Layer 2 ◽  
Point To Point ◽  
Private Network ◽  
Internet Network

<p align="center"><strong>ABSTRACT</strong></p><p><em>The internet as a data transmission backbone has security threats in sending data. To overcome the security problem of every data communication that is done through a public network (public network), then a connection is needed that requires a connection between workstations running privately, so that only workstations that have access can connect, by using a virtual private network or VPN. The advantage of a VPN is that data sent over an encrypted VPN is quite safe and the secret is maintained even through the internet network because the data sent will go through the tunnel. Tunneling itself is a method for transferring data from one network to another by using a veiled internet network. Two protocols can be chosen in a VPN, namely Point to Point Tunneling Protocol (PPTP) and Layer 2 Tunneling Protocol (L2TP). However, the performance of each of these protocols is unknown yet. To find out the performance of the two protocols we need a test with a simulation method. Using a Mikrotik router and Wireshark application with Quality of Service (QoS) parameters consisting of Packet Loss, Delay, and Throughput on 2 clients connected to the mikrotik router and each client uses a different protocol. All clients will stream videos simultaneously to get a data packet capture. The test results will be grouped into four categories, namely bad, moderate, good and very good. It is expected that data will be able to show the quality of service of both protocols. so that it can be used as a reference in the selection of VPN protocol to be used.</em></p><p><strong><em>Keywords: </em></strong><em>Quality of Service, PPTP, L2TP</em></p><p align="center"><strong>ABSTRAK</strong></p><p><em>Internet sebagai backbone pengiriman data memiliki ancaman keamanan dalam pengiriman data. Untuk mengatasi masalah keamanan setiap komunikasi data yang dilakukan melalui jaringan publik (public network) maka diperlukan suatu mekanisme yang memungkinkan koneksi antar workstation berjalan secara private, sehingga hanya workstation yang memiliki akses yang dapat saling terhubung, dengan cara memanfaatkan virtual private network atau VPN.</em> <em>Keuntungan VPN adalah data yang dikirimkan melalui VPN terenkripsi sehingga cukup aman dan rahasianya tetap terjaga meskipun melalui jaringan internet, karena data yang dikirim akan melalui tunnel.</em> <em>Tunneling sendiri merupakan metode untuk transfer data dari suatu jaringan ke jaringan lain dengan memanfaatkan jaringan internet secara terselubung. Terdapat dua protokol yang dapat dipilih dalam VPN yaitu Point to Point Tunneling Protocol (PPTP) dan Layer 2 Tunneling Protocol (L2TP). Akan tetapi belum diketahui performa dari masing – masing protokol tersebut. Untuk mengetahui kinerja dari kedua protokol tersebut diperlukan sebuah pengujian dengan metode simulasi. Menggunakan router mikrotik dan aplikasi Wireshark dengan parameter Quality of Service (QoS) yang terdiri dari Packet Loss, Delay, dan Throughtput pada 2 client yang terhubung ke router mikrotik dan setiap client akan menggunakan protokol yang berbeda. Semua client akan melakukan video streaming secara bersamaan untuk mendapatkan capture paket data. Hasil pengujian akan dikelompokkan menjadi empat kategori, yaitu kategori buruk, sedang, bagus dan sangat bagus. Diharapkan akan dihasilkan sebuah data yang dapat menunjukkan kualitas dari layanan kedua protokol tersebut. sehingga dapat dijadikan acuan dalam pemilihan protokol vpn yang akan digunakan.</em></p><strong><em>Kata kunci:</em></strong><em> Quality of Service, PPTP, L2TP</em>

scholarly journals ALGORITMA KLASIFIKASI NAIVE BAYES DAN SUPPORT VECTOR MACHINE DALAM LAYANAN KOMPLAIN MAHASISWA

2020 ◽  
Vol 5 (2) ◽  
pp. 211-220 ◽  
Author(s):  
Hermanto Hermanto ◽  
Ali Mustopa ◽  
Antonius Yadi Kuntoro
Keyword(s):  
Support Vector Machine ◽  
Text Mining ◽  
Naive Bayes ◽  
Naïve Bayes ◽  
Support Vector ◽  
Machine Method ◽  
The Status ◽  
Auc Value ◽  
Bayes Algorithm

Service in the world of education is an important element for the creation of an academic atmosphere that is conducive to the implementation of a successful teaching and learning process. The process of service to students there is a tendency to be implemented not following the minimum service standards that must be provided to students so that students tend to complain about the services provided. Submission of criticism, complaints, input, or suggestions for dissatisfaction and problems that exist in the university environment is still very limited. Complaints can be constructive if submitted to the right place and party. In this research the data processing of email complaints from students conducted at the academic student body (students.bsi.ac.id). Student complaint data that will be processed is data in the form of * .xls complaint file. Before text data is analyzed using text mining methods, the pre-processing text needs to be done including tokenizing, case folding, stopwords, and stemming. After pre-processing, the classification method is then performed in classifying each complaint category and dividing the status into two parts, namely complaint and not complaint so that the status becomes a normal condition in text mining research. The purpose of this study is to obtain the most accurate algorithm in the classification of student complaints and can find out the results of the classification of the Naïve Bayes algorithm method and Support vector Machine used and compared. In this study, the results of testing by measuring the performance of these two algorithms using Cross-Validation, Confusion Matrix, and ROC Curves. The obtained Support vector Machine algorithm has the highest accuracy value compared to Naïve Bayes. AUC value = 0.922. for the Support vector machine method using the student academic data collection dataset (students.bsi.ac.id) has 84.45%, from the Naïve Bayes algorithm has an accuracy rate of about 69.75% and AUC value = 0.679.

scholarly journals PENERAPAN DATA MINING TERHADAP DATA COVID-19 MENGGUNAKAN ALGORITMA KLASIFIKASI

2021 ◽  
Vol 21 (1) ◽  
pp. 44-52
Author(s):  
Rizka Dahlia ◽  
Nanik Wuryani ◽  
Sri Hadianti ◽  
Windu Gata ◽  
Arina Selawati
Keyword(s):  
Data Mining ◽  
South Korea ◽  
Respiratory System ◽  
Nearest Neighbor ◽  
Naive Bayes ◽  
Naïve Bayes ◽  
K Nearest Neighbor ◽  
The World ◽  
Bayes Algorithm

Coronavirus 2019 or more commonly referred to as COVID-19 is a type of virus that attacks the respiratory system. Until now the number of spread and the number of deaths caused by this virus continues to increase. As of April 21, 2020, based on data from the WHO, the total number of cases infected with this virus reached 2,397,217 with 162 deaths from all over the world. For South Korea itself, as of March 21, 2020, the total number of infected cases was 10,683 with a total of 237 deaths. In this study, researchers conducted data processing on the spread of COVID-19 in South Korea with Rapidminer using a classification algorithm, namely Naïve Bayes, C4.5, and K-Nearest Neighbor by performing the stages of selection, preprocessing, transfotmating, data mining and interpretation or evaluating the quality of the best accuracy of 80.79% with AUC of 0.881 achieved by the Naïve Bayes algorithm. The distribution of the data found that the influential attribute of the isolated class factor from the patient contained in the sex attribute where more women experienced isolation. Keywords— COVID-19, data mining, classification, C4.5, Naïve Bayes, K-NN

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