Analisa Performa RStudio Server Berbasis Cloud Menggunakan Elastic Stack sebagai Sistem Manajemen Metrik

Reliabilitas atau keandalan merupakan salah satu sifat penting pada sebuah server dalam melayani pengguna. Salah satu cara mengukurnya ialah dengan melakukan uji perfoma. Penelitian ini bertujuan untuk mengetahui kemampuan RStudio Server pada infrastruktur cloud saat digunakan oleh multiuser dengan Elastic Stack sebagai sistem yang menangani pengumpulan, penyimpanan dan visualisasi data metriknya. Tahapan dimulai dengan mengumpulkan data berupa metrik sistem oleh Metricbeat, lalu diproses Logstash dan disimpan menjadi index dalam Elasticsearch, visualisasi data ditampilkan oleh Kibana. Pengujian kinerja server dilakukan dengan menjalankan script R berdurasi 2 menit dan 7 menit secara simultan. Hasil pengujian berupa catatan CPU Usage, Memory Usage dan durasi penyelesaian script selanjutnya di plotting pada R. Hasil analisa dari plotting data menunjukkan jumlah user yang dapat menggunakan Rstudio Server dengan spesifikasi 2 CPU dan RAM 4GB secara optimal ialah maksimal 2 user pada script dengan run time 2 menit dan 7 menit, lebih dari jumlah user itu akan mempengaruhi waktu proses penyelesaian script menjadi tingkat performa sedang hingga berat.

An Enhanced Block Pre-processing of PRESENT Algorithm for Fingerprint Template Encryption in the Internet of Things Environment

Many previous studies had proven that The PRESENT algorithm is ultra-lightweight encryption. Therefore, it is suitable for use in an IoT environment. However, the main problem with block encryption algorithms like PRESENT is that it causes attackers to break the encryption key. In the context of a fingerprint template, it contains a header and many zero blocks that lead to a pattern and make it easier for attackers to obtain an encryption key. Thus, this research proposed header and zero blocks bypass method during the block pre-processing to overcome this problem. First, the original PRESENT algorithm was enhanced by incorporating the block pre-processing phase. Then, the algorithm’s performance was tested using three measures: time, memory usage, and CPU usage for encrypting and decrypting fingerprint templates. This study demonstrated that the proposed method encrypted and decrypted the fingerprint templates faster with the same CPU usage of the original algorithm but consumed higher memory. Thus, it has the potential to be used in IoT environments for security.

A Secure Environment Using a New Lightweight AES Encryption Algorithm for E-Commerce Websites

Providing security for transmitted data through the e-commerce environment requires using a fast and high secure encryption algorithm. Balancing between the speed and the security degree is a problem that many of the encryption algorithms suffer from. Increasing the security degree requires increasing the level of complexity which results in increasing encryption time. On the other hand, increasing the algorithm speed may reduce the complexity degree which affects the security level. This paper aims to design an encryption algorithm that balances time and complexity (speed and security). This is done by suggesting a security environment that depends on creating and providing an agent software to be settled into each customer device that manages the purchase and security process without customer interference. The suggested encryption algorithm is applied within this environment. Several modifications are performed on the AES encryption algorithm. The AES was chosen due to its performance (security and speed), which makes it suitable for encrypting transmitted data over the Internet. These modifications involve adding preprocessing steps (padding and zigzag), eliminating Sub Byte step, and reducing the number of rounds. The experimental results showed that the suggested algorithm provides more security and speed in the encryption and decryption process. The randomness degree has increased by 29.5%. The efficiency is increased because the encryption and decryption times are reduced, as is the CPU usage. The throughput for the suggested algorithm is increased by 10% for the encryption process and is increased by 9.3% for the decryption process.

Dynamic detection of mobile malware using smartphone data and machine learning

Mobile malware are malicious programs that target mobile devices. They are an increasing problem, as seen in the rise of detected mobile malware samples per year. The number of active smartphone users is expected to grow, stressing the importance of research on the detection of mobile malware. Detection methods for mobile malware exist but are still limited. In this paper, we propose dynamic malware-detection methods that use device information such as the CPU usage, battery usage, and memory usage for the detection of 10 subtypes of Mobile Trojans on the Android Operating System (OS). We use a real-life sensor dataset containing device and malware data from 47 users for a year (2016) to create multiple mobile malware detection methods. We examine which features, i.e. aspects, of a device, are most important to monitor to detect (subtypes of) Mobile Trojans. The focus of this paper is on dynamic hardware features. Using these dynamic features we apply the following machine learning classifiers: Random Forest, K-Nearest Neighbour, and AdaBoost.

Designing High Availability Cluster Using Server Virtualization on Cloud Storage Services

Cloud Storage Service at PT. Harrisma Globaltechnologies Jakarta has not been applied based on virtualization. The problem with cloud storage services in general is that the server is not able to serve a high number of requests as a result the cloud storage server fails. One of these problems is caused by inadequate server resources and less than optimal storage server management. These problems can be solved effectively and efficiently with server virtualization. Where server virtualization is managed by Proxmox. Proxmox is an open-source virtualization platform supported by KVM and OpenVZ hypervisors and can be managed with web-based graphics. Server virtualization collaboration can save server resources by duplicating server services virtually. The research methodology used is clustering by applying the concept of high-availability where there is a secondary server as a backup-server for the primary-server. The results of this study include three parameters, namely Throughput, Response Time, and CPU Usage. The results show that cloud storage servers that implement virtualization than those that do not apply with a difference in the value of the Throughput parameter of 1896.6 Kbps, the difference in the value of the Response Time parameter of 10.6 requests/second, and the difference in the value of the CPU Usage criteria of 11.91%

Penerapan Algoritma A-Star Dalam Mencari Jalur Tercepat dan Pergerakan NonPlayer Character Pada Game Petualangan Labirin Tech-Edu

Games are a means of entertainment that are in electronic media such as smartphones that are made as attractive as possible so that players get inner satisfaction. The development of the game industry is growing quite fast from mobile to desktop. There are many categories of games such as action, strategy, sports, shooter, adventure and simulation. Adventure games are one category of games where players are trained to think in order to complete the game. There are many things that need to be considered, such as visuals on maps or interactions with NPCs (NonPlayer Character), by utilizing technology now learning can be delivered through a game. The characteristics of entertaining games and interesting visual activities will make learning more attractive. Making games will of course use algorithms as features or systems in game making. The a-star algorithm is one that is widely used in making maze games. The a-star algorithm can be applied to the help of the fastest path or movement of the NPC. The purpose of the research is to make a maze educational game using the a-star algorithm as the fastest path search and NPC movement. The test results on games on seven devices got the results that they were successfully run, in CPU usage on mobile devices a minimum of 7% and a maximum of 57%. CPU usage on desktop devices is a minimum of 2% and a maximum of 33%.

Comparative analysis of frameworks used in automated testing on example of TestNG and WebdriverIO

The article presents a comparative analysis of frameworks supporting the development of automated tests for defined test scenarios. The comparative study concerned the TestNG and WebDriverIO frameworks. The overview of the tool has been analyzed both in terms of the test development process as well as the speed and efficiency of their execution. The website github.com was used for the purposes of the work. This application was used to run test scripts written in both frameworks. The results were compared by five defined criteria: the time of running the test files in different browser operation modes, the average value of memory and CPU usage during the test execution. The summary includes the evaluation of the compared frameworks.

A multi-criteria comparison of mobile applications built with the use of Android and Flutter Software Development Kits

This publication presents a multi-criteria comparison of two mobile applications built with the use of Android and Flutter SDK. The former has been implemented with Kotlin and the latter with Dart. The benchmarking process examines factors such as execution time and CPU usage during data and disk operations. During the analysis, attention was paid to the length and volume the source code, community support and the availability of libraries. The comparative analysis shows that a mobile application written using Android SDK is often not only faster and more efficient, but also has greater community support and the number of libraries available. In addition, an analysis of the source code volume showed that Flutter has more concise but more difficult to navigate code than Android.