Para penyedia layanan informasi atau Internet Service Provider (ISP) akan berusaha untuk memberikan segala upaya demi kepuasan para pelanggan agar dapat mengakses internet dengan nyaman. Dengan adanya internet, user dapat mengakses website yang diinginkan. Website e-Learning Universitas Syiah Kuala merupakan sebuah website yang dapat dikunjungi oleh mahasiswa dan dosen di Universitas Syiah Kuala untuk keperluan pembelajaran. Penelitian ini ditujukan untuk menganalisis Quality of Service (QoS) jaringan internet pada website e-Learning USK. Quality of Service (QoS) dari website e-Learning USK dapat di analisis dengan menggunakan Wireshark. Dengan adanya Wireshark dapat memudahkan untuk memperoleh nilai parameter dari throughput, packet loss, dan Delay. Nilai parameter yang dibandingkan ialah dari 3 (tiga) provider yang berbeda. Hasil dari penelitian ini adalah provider Telkomsel merupakan provider yang direkomendasikan saat mengakses website e-Learning Universitas Syiah Kuala, karena nilai Quality of Service (QoS) yang dihasilkan lebih unggul dibandingkan provider lainnya. Provider Telkomsel memperoleh nilai throughput tertinggi dibandingkan provider lain, yaitu senilai 1.823,20 kb/s. Nilai packet loss yang diperoleh lebih unggul dibandingkan provider lain, hanya mengalami sekali packet loss senilai 0,02%. Nilai delay yang diperoleh sangat bagus karena nilai yang dihasilkan 150 ms.
Since the entry of Islam, many ancient relics in the archipelago were written using Jawi script. Due to human or natural factors, these ancient relics will be damaged or destroyed. To avoid the loss of this ancient heritage data, the data must be stored in digital documents. In order to convert digital documents into machine-readable text format, the use of Optical Character Recognition (OCR) technology is inevitable. In this research, OCR technology is implemented on isolated Jawi scripts. Freeman Chain Code (FCC) is used to extract the isolated Jawi script features. Subsequently, the FCC feature is fed into Support Vector Machine (SVM) in order to classify the character. The decision rule classification is applied to the class of SVM classification in the Jawi script form. The results of the SVM classification into 19 classes reached 81.58%, while the results for merging into 15 classes produced better results with the accuracy 84.21%. Feature extraction of dot location is divided into the top, middle, and bottom. Feature extraction of the number of dotss is done by counting the number of dots, while feature extraction of the presence of holes is carried out by detecting the presence of holes in the characters. These features are applied to the class of results from SVM classification with decision-making rules. The percentage of success in applying the decision rules to the results of the classification of incorporation into 15 classes by SVM reached 92.86%. Further research will be conducted to determine the effect of the feature of the location of the dot and the number of dots on the shape of the main part of the character.
People are frequently shocked when someone passes away suddenly without any prior symptoms. One of the contributing factors is a heart attack. This condition might occur anywhere and at any time. A sudden heart attack can be highly perilous for a person who is alone, without family members or friends because the family cannot be informed of the victim's condition or their location. Therefore, it is vital to raise awareness of heart attacks. With the support of the Internet of Things, this study aims to develop a wearable device that people may use to monitor their heart health and connect with hospitals to get alerts in case of a heart attack. This system also provides family members with access to a web-based patient monitoring tool. The heart beat is considered as the parameter in developing this system. There are three types of evaluation which are conducted in this study, namely: 1) Sub-system evaluation; 2) Black-box testing; and 3) Integrating system testing. The three evaluation results show that all assembled hardware components are work properly and the system effectively satisfies the objectives of monitoring, buzzer activation, hospital and patient family notification, and so forth, with 1.96% average sensor error, which is still considerably acceptable.
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