A Mobile Ad Hoc Network (MANET) protocol requires proper settings to perform data transmission optimally. To overcome this problem, it is necessary to select the correct routing protocol and use the routing protocol’s default parameter values. This study examined the effect of route request parameters, such as RREQ_RETRIES and MAX_RREQ_TIMOUT, on the Ad Hoc On-demand Distance Vector (AODV) protocol, which was then compared with the default AODV performance Optimized Link State Routing (OLSR) protocols. The performance metrics used for measuring performance were Packet Delivery Ratio (PDR), throughput, delay, packet loss, energy consumption, and routing overhead. The results show that the OLSR protocol has a smaller delay than the AODV protocol, while in other measurements, the AODV protocol is better than OLSR. By reducing the combination value of RREQ_RETRIES, MAX_RREQ_TIMEOUT in AODV routing to (2, 10 s) and (3, 5 s), the protocol’s performance can be improved. The two combinations result in an average increase in throughput performance of 3.09%, a decrease in delay of 17.7%, a decrease in packet loss of 27.15%, and an increase in PDR of 4.8%. For variations in the speed of movement of nodes, 20 m/s has the best performance, while 5 m/s has the worst performance.
Background: The COVID-19 pandemic has made people spend more time on online meetings more than ever. The prolonged time looking at the monitor may cause fatigue, which can subsequently impact the mental and physical health. A fatigue detection system is needed to monitor the Internet users well-being. Previous research related to the fatigue detection system used a fuzzy system, but the accuracy was below 85%. In this research, machine learning is used to improve accuracy.Objective: This research examines the combination of the FaceNet algorithm with either k-nearest neighbor (K-NN) or multiclass support vector machine (SVM) to improve the accuracy.Methods: In this study, we used the UTA-RLDD dataset. The features used for fatigue detection come from the face, so the dataset is segmented using the Haar Cascades method, which is then resized. The feature extraction process uses FaceNet's pre-trained algorithm. The extracted features are classified into three classes—focused, unfocused, and fatigue—using the K-NN or multiclass SVM method.Results: The combination between the FaceNet algorithm and K-NN, with a value of resulted in a better accuracy than the FaceNet algorithm with multiclass SVM with the polynomial kernel (at 94.68% and 89.87% respectively). The processing speed of both combinations of methods has allowed for real-time data processing.Conclusion: This research provides an overview of methods for early fatigue detection while working at the computer so that we can limit staring at the computer screen too long and switch places to maintain the health of our eyes.
One algorithm to classify textual data in automatic organizing of documents application is KNN, by changing word representations into vectors. The distance calculation in the KNN algorithm becomes essential in measuring the closeness between data elements. This study compares four distance calculations commonly used in KNN, namely Euclidean, Chebyshev, Manhattan, and Minkowski. The dataset used data from Youtube Eminem’s comments which contain 448 data. This study showed that Euclidian or Minkowski on the KNN algorithm achieved the best result compared to Chebycev and Manhattan. The best results on KNN are obtained when the K value is 3.
Digital Forensics is one of the technological fields contained many sub-fields that can assist technically in collecting digital evidence to be presented in a trial in accordance with applicable law. The example of digital forensics sub-field is Image Forensics, which aims to digitally collect and look for evidentiary facts in determining the authenticity of an image or document that contained images. Various criminal and pornographic cases involving image files are still happening nowadays, therefore forensics on images as evidence is an important key to assist the court in making decisions. This research examines the authenticity of documents in the form of digital letters using National Institute of Standard and Technology (NIST) method by applying the forensic ELA (Error Level Analysis). Several previous researches have proven that the forensic ELA is able to detect modifications that have been made to images. Differences with previous researches and this research are the authors also checked the metadata of the images before performing the ELA examination using Fotoforensics. The results of the analysis shows a high level of consistency in the images and writings due to the accumulation of white dots in several places such as in headers, logos, header’s writings, text contents, footnotes, and signatures.
Unit Pengelolaan dan Pelayanan Teknologi Informasi (UP2TI) adalah salah satu unit di Fakultas Sains Matematika Universitas Diponegoro yang bergerak di bidang pelayanan dan pengelolaan segala sesuatu yang berkaitan dengan teknologi informasi. UP2TI mengelola perangkat jaringan seperti router, switch, server, dan access point di FSM UNDIP. Dengan banyaknya perangkat jaringan yang dikelola, admin UP2TI mengalami kesulitan jika proses monitoring perangkat jaringan dilakukan secara manual yaitu hanya mengandalkan laporan dari client jika ada permasalahan pada jaringan dan juga belum ada sistem untuk memonitor aktifitas pengguna internet.Solusi atas permasalahan tersebut dengan membuat aplikasi monitoring jaringan. Aplikasi monitoring bisa juga disebut sebagai Network Management System yaitu suatu sistem yang berfungsi untuk membantu system administrator dalam memonitor dan mengontrol perangkat jaringan yang kompleks SNMP merupakan sebuah protocol aplikasi pada jaringan TCP/ IP yang dapat digunakan sebagai protocol dalam Network Management System.Aplikasi monitoring yang dikembangkan juga melakukan pembacaan log squid proxy untuk mengetahui aktivitas pengguna internet. Pengembangan aplikasi ini menggunakan metode waterfall dengan bahasa pemograman PHP dengan framework CodeIgniter dan sistem manajemen basis data MySQL. Setelah pengembangan selesai dilakukan, dilanjutkan pengujian secara black-box. Hasil akhir dari penelitian ini adalah aplikasi yang memudahkan admin dalam memonitoring perangkat dan aktivitas pengguna pada jaringan Fakultas Sains Matematika Universitas Diponegoro
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