Ads Block Management System Using Open Virtual Private Network on Ubuntu Operating System

Syahputra, Edy Rahman; Sembing, Boni Oktaviana; Dewi, Arie Rafika; Hasdiana, H; Maulana, Halim

doi:10.14419/ijet.v7i2.5.13951

Cited by 5 publications

(1 citation statement)

References 18 publications

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“…In the clustering technique, there are several algorithms that can be applied, one of the algorithms that is quite familiar is the K-Means algorithm. In a study conducted by Syahputra, et al (2018) which used the K-Means algorithm for clustering lecturer assessments based on the Student Satisfaction Index, from the results of the study it was concluded that it was necessary to increase the clustering accuracy of the K-Means algorithm. The K-Means algorithm has several variations of development, one of the results of this development is the K-Means++ algorithm.…”

Section: Introductionmentioning

confidence: 99%

Analysis of the Effectiveness of Online Learning Using Eda Data Science and Machine Learning

Maulana

Al-Khowarizmi

2022

SinkrOn

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In educational institutions, it is important to evaluate the performance of lecturers. One aspect that can be used as a reference in assessing the performance of lecturers is the assessment in terms of teaching (learning - teaching process). Value data in the learning evaluation process can be further processed to classify or map the results of the learning evaluation. In this study, the authors use Data Science and Exploratory Data Analysis (EDA) techniques and apply the K-Means++ clustering algorithm using the Java programming language to determine the value of the learning process. group learning evaluation data. To determine the number of clusters to be formed, the elbow method is applied. Meanwhile, to measure the quality of each cluster formed, the silhouette coefficient method is applied. The elbow graph can visualize the Sum of Square Error (SSE) value as a way to determine the best k value in the clustering process. From the tests carried out 12 times, using 6 datasets with variations in 4 areas of assessment, 10 tests resulted in a value of k = 3, 10 tests produced a value of k = 4, and 1 test resulted in a value of k = 5 as the best number of clusters. In the cluster quality measurement process, 13 tests have good clustering quality, because on average they produce clusters that have a good structure with a Global Silhouette Coefficient > 0.65.

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Section: Introductionmentioning

confidence: 99%

Analysis of the Effectiveness of Online Learning Using Eda Data Science and Machine Learning

Maulana

Al-Khowarizmi

2022

SinkrOn

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Certainty Factor for Early Detection of Children’s Respiratory Disease

Hasdiana

Syahputra

Dewi

et al. 2019

J. Phys.: Conf. Ser.

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The immune system of children are weaker than adults. They are susceptible to diseases. One of the diseases that often affects them is respiratory disease. Expert systems can be used to determine early diagnosis of it. Certainty Factor allows the user to measure the certainty’s degree of the diagnosis. It is also affecting the level of trust. It is easy for users to make decisions in determining the healing step. Symptoms of the disease are analysed and managed into the knowledge base of the expert system. The result of disease diagnosis is appeared in the form of disease data. The result data are accompanied by certainty value or percentage of confidence that obtained by certainty factor method applied.

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Analyze and Designing Low-Cost Network Monitoring System Using Icinga and Raspberry Pi

Maulana

Al-Khowarizmi

2021

IOP Conf. Ser.: Earth Environ. Sci.

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The development of information and data communication technology field in the world has now increasingly led to the development of the Internet of Things devices. The development of network technology in an organization/university has triggered the need for a system that can monitor computer networks and their devices in an organization/university environment that is quite extensive and complex. At this time, network monitoring becomes quite difficult to do if the computer network in a corporate environment has become very broad and complex. The wider the network and the complex network and multimedia topology that is built, the more difficult it is for network administrators to monitor the network. However, in its application to build a monitoring system it will require devices and hardware and computer requirements and specifications that are above average, which of course will also require substantial costs and large resources as well. In this research, a network monitoring and bandwidth management system will be built by using Raspberry-Pi and Icinga as the main modules in network management. Raspberry Pi is an alternative of computers that has many capabilities and has a high ability which is generally used as a Mini-Server at a very cheap price when compared with existing servers. And Icinga is an open source-based application platform that functions to monitor various types of variables contained in servers and infrastructure of the network such as availability and performance and provides easy access for network administrators to important data and to improve maintenance of network conditions. So, it is hoped that by applying this, a good and optimal network monitoring and bandwidth management tool can be produced at a low cost.

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Ads Block Management System Using Open Virtual Private Network on Ubuntu Operating System

Cited by 5 publications

References 18 publications

Analysis of the Effectiveness of Online Learning Using Eda Data Science and Machine Learning

Analysis of the Effectiveness of Online Learning Using Eda Data Science and Machine Learning

Certainty Factor for Early Detection of Children’s Respiratory Disease

Analyze and Designing Low-Cost Network Monitoring System Using Icinga and Raspberry Pi

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