Manajemen Bandwidth Pada Mikrotik Dengan Limitasi Bertingkat Menggunakan Metode Simple Queue

Pada sebuah perusahaan, jumlah pengguna yang harus dilayani oleh jaringan internet biasanya dapat mencapai puluhan hingga ratusan host. Maka dari itu, pemilihan sebuah perangkat Router sangat penting untuk dapat menunjang pengguna dan manajemen bandwidth pada perusahaan tersebut. Dalam hal ini untuk dapat mengoptimasi penggunaan bandwidth, haruslah memilih metode yang tepat untuk memanajemennya. Metode Simple Queue dapat diimplementasikan dan dikembangkan dengan limitasi bertingkat. Yang dimaksud dengan limitasi bertingkat yaitu adanya Queue Parent dan Child Queue. Queue Parent merupakan sebuah konfigurasi yang menyatakan total bandwidth real yang dimiliki, sedangkan untuk Child Queue merupakan konfigurasi yang diterapkan untuk Client berdasarkan alamat IP Address Client. Pada Child Queue juga terdapat konfigurasi untuk menentukan limit minimum dan limit maksimum untuk setiap Client. Pada gambar 16 dan tabel 6 merupakan hasil dari pengujian limitasi bertingkat menggunakan metode Simple Queue dengan kondisi satu client aktif, dua client aktif, dan tiga client aktif mendapatkan hasil Throughput 0,52 Mbps; 0,23 Mbps; 0,17 Mbps. Berdasarkan standar throughput Telecommunications and Internet Protocol Harmonization Over Networks (TIPHON), hasil tersebut mendapat kategori sangat baik. Kemudian, untuk hasil Packet Loss mendapatkan 0,8%; 0,4%; 0,9%. Berdasarkan standar packet loss TIPHON, hasil tersebut mendapat kategori baik

A new agglomerative hierarchical clustering to model student activity in online learning

In this paper, a new technique of agglomerative hierarchical clustering (AHC), which is known as SLG (single linkage dissimilarity increment distribution, global cumulative score standard), can work well in analyzing students' activity in online learning as evidenced by obtaining the highest score in testing the validity index of cophenetic correlation coefficient (CPCC) ie 0.9237, 0.9015, 0.9967, 0.8853, 0.9875 of the five datasets compared with conventional agglomerative hierarchical clustering methods

Comparison of the feature selection algorithm in educational data mining

Student academic accomplishment is the foremost focus of every educational institution. In developing student achievement in educational institutions, the researchers finally created a new research area, namely educational data mining (EDM). How the Feature Selection algorithm works is by removing unrelated data from educational datasets; therefore, this algorithm can improve the classification performance managed in EDM techniques. This research presents an analysis of the performance of the Feature Selection (FS) algorithm from the student dataset. The results received from other FS algorithms and classifiers will help other researchers to gain some best combination regarding Feature Selection algorithms and the classification. Selecting features that are relevant for student forecast models is a sensitive problem to stakeholders in education because they must make decisions based on the results of the prediction models. For the future, our paper seeks to play a decisive part while developing quality concerning education, as well as guiding different researchers in conducting educational interventions

Building an Effective Branding Strategy: A Study Case of Raiment

Since the civilized era, branding has played a very important role in business activities which when people recognize the brand, they have a perceived perception of the object. Seems simple and practical, many people mistakenly think that brand-building is an easy thing to do and therefore many businesses are stuck or even fail because they are not built through the appropriate implementation of strategies. One of which included Raiment, an online platforms-based business which helps people to create or customize clothing products. Having a vision to simplify the process of procuring a clothing product that was previously considered inefficient, concerns started to arise after it was known that the number of sales generated by Raiment for more than 1 year operating did not have any significant progress. In fact, most of the sales generated are still related through relatives such as friends and families. The aim of this research is to help Raiment to be able to assess the related factors in building its brand and also develop the right strategy to be able to create a better brand. External and internal analysis are also used in this research to help see the root of the problems faced by Raiment. The external analysis included is Porter's Five Forces, PESTEL Analysis, and Competitor Analysis. Meanwhile, the internal analysis included is Company Analysis and Brand Audit Analysis. The results of this study indicate that the roots of the problems faced by Raiment are around unclear brand-building guidelines, lack of available budget, lack of knowledge regarding the industry, and also lack of brand marketing efforts. Several solutions were proposed to address the root causes of the problem in developing the Raiment brand, such as creating a brand positioning model to increase brand awareness, building a brand resonance model to build brand loyalty, and also creating a brand value chain model to measure returns from the allocated investment for brand marketing activities. The results of this research can also help other businesses in improving and strengthening the existence of their brands, especially in the Indonesian market. With a note, further studies related to the garment/convection industry need to be carried out due to the growing trend

ANALISIS KUALITAS PELAYANAN PUBLIK DI BANDAR UDARA RADIN INTEN II PROVINSI LAMPUNG

Service quality and customer satisfaction are inseparable. Either service quality or customer satisfaction mostly can be measured by comparing perceived performance of the institution providing the service and the expectation of the service itself. Customer satisfaction rises when the performance is higher than the expectation. In the contrary, higher expectation rather than the performance shows users’ dissatisfaction which also shows the real description of the service quality. Radin Inten II Airport has been established as Public Service Agency. Therefore it is important to carry out some researches to discover service quality provided by the institution. Analyzed by Gap Analysis, Level of Satisfaction Analysis and Importance-Performance Analysis, this research figures the picture of service quality through five dimensions, Reliability, Responsiveness, Assurance, Empathy and Tangibles. The result of the research shows there are four indicators in Quadrant A that the institution must focus on, in order to increase the service quality, four indicators in Quadrant B, two indicators in Quadrant C and six indicators in Quadrant D. There are no indicators, of sixteen indicators being analyzed in the research, which its performance score exceeds its expectation score so it is obvious to say that there are dissatisfaction regarding services provided by the institution

Information Technology Service Management with Cloud Computing Approach to Improve Administration System and Online Learning Performance

This work discusses the development of information technology service management using cloud computing approach to improve the performance of administration system and online learning at STMIK IBBI Medan, Indonesia. The network topology is modeled and simulated for system administration and online learning. The same network topology is developed in cloud computing using Amazon AWS architecture. The model is designed and modeled using Riverbed Academic Edition Modeler to obtain values of the parameters: delay, load, CPU utilization, and throughput. The simu- lation results are the following. For network topology 1, without cloud computing, the average delay is 54 ms, load 110 000 bits/s, CPU utilization 1.1%, and throughput 440 bits/s. With cloud computing, the average delay is 45 ms, load 2 800 bits/s, CPU utilization 0.03%, and throughput 540 bits/s. For network topology 2, without cloud computing, the average delay is 39 ms, load 3 500 bits/s, CPU utilization 0.02%, and throughput database server 1 400 bits/s. With cloud computing, the average delay is 26 ms, load 5 400 bits/s, CPU utilization email server 0.0001%, FTP server 0.001%, HTTP server 0.0002%, throughput email server 85 bits/s, FTP server 100 bits/sec, and HTTP server 95 bits/s. Thus, the delay, the load, and the CPU utilization decrease; but, the throughput increases. Information technology service management with cloud computing approach has better performance

Catfish Fry Detection and Counting Using YOLO Algorithm

The development of computer vision technology is growing very fast and penetrating all sectors, including fisheries. This research focuses on detecting and counting catfish fry. This research aims to apply deep learning in detecting catfish fry objects and counting accurately so as to help farmers and buyers reduce the risk of loss.  The detection system in this research uses digital image processing techniques as a way to obtain information from the detection object. The research method uses YOLO Object Detection which has a very fast ability to identify objects. The object detected is a catfish puppy object that is given a bounding box and the detection label displays the class name and precision value. The dataset amounted to 321 images of catfish puppies from internet and photography sources that were trained to produce a new digital image model. The number of split training, validation and testing datasets is worth 831 annotation images, 83 validation images and 83 images for the testing process. The value of the training model mAP 50.39 %, Precision 61.17 % and Recall 58 %  Detection test results based on the YOLO method obtained an accuracy rate of 65.7%. The avg loss value in the final model built with YOLO is 4.6%. Based on the results of tests carried out with the number of objects 50 to 500 tail size 2-8 cm using video, objects in the image are successfully recognized with an accuracy of 63% to 70%. Calculations using the YOLO algorithm show quite good results.The development of computer vision technology is growing very fast and penetrating all sectors, including fisheries. This research focuses on detecting and counting catfish fry. This research aims to apply deep learning in detecting catfish fry objects and counting accurately so as to help farmers and buyers reduce the risk of loss.  The detection system in this research uses digital image processing techniques as a way to obtain information from the detection object. The research method uses YOLO Object Detection which has a very fast ability to identify objects. The object detected is a catfish puppy object that is given a bounding box and the detection label displays the class name and precision value. The dataset amounted to 321 images of catfish puppies from internet and photography sources that were trained to produce a new digital image model. The number of split training, validation and testing datasets is worth 831 annotation images, 83 validation images and 83 images for the testing process. The value of the training model mAP 50.39 %, Precision 61.17 % and Recall 58 %  Detection test results based on the YOLO method obtained an accuracy rate of 65.7%. The avg loss value in the final model built with YOLO is 4.6%. Based on the results of tests carried out with the number of objects 50 to 500 tail size 2-8 cm using video, objects in the image are successfully recognized with an accuracy of 63% to 70%. Calculations using the YOLO algorithm show quite good results