Quality of Service merupakan metode pengukuran tentang seberapa baik jaringan yang terpasang dan juga merupakan suatu usaha untuk mendefinisikan karakteristik dan sifat dari satu layanan. Dengan dibuatnya sistem pembayaran online yang terdapat di PT. PLN (Persero) Jember, layanan internet yang digunakan hendaknya harus memenuhi standar TIPHON (Telecommunications and Internet Protocol Harmonization Over Networks). Maka diperlukan optimasi kinerja QoS sebagai salah satu cara untuk mengetahui seberapa besar kualitas layanan data yang harus dipenuhi. Parameter QoS yang digunakan untuk analisis layanan komunikasi data adalah jitter, packet loss, throughtput, dan delay. Dari hasil analisis data menunjukan bahwa pada jam sibuk (09.00-11.00 WIB) dan non sibuk (11.00-13.00 WIB) mendapatkan hasil rata – rata indeks QoS sebesar 2,125 dalam kategori “kurang memuaskan”. Dengan kapasitas bandwidth yang disediakan sebesar 3 Mbps. Kemudian dari hasil perhitungan optimasi bandwidth yang diperlukan sebesar 7,154 Mbps dan disimulasikan mendapatkan rata–rata indeks QoS yang sebesar 3,5 dalam kategori “sangat memuaskan”.
The COVID-19 pandemic has become the focus of world problems that need to be resolved. This is because the rate of spread is speedy and able to take down the world's health system. Therefore, many researchers are focusing their research on solving this problem by doing an initial screening on the X-Ray image of the subject's lungs. One of them is by using Deep Learning. Several articles that talk about implemented Deep Learning for classifying X-Ray images have been published. But most of them are comparing different architecture CNN (Convolutional Neural Network). In this study, the authors try to create a multi-classifier Deep Learning system that consists of nine different CNN architectures and combined with three different Majority Vote techniques. The target of this research is to maximize the performance of classification and to minimize errors because the final decision is a compilation of decisions contained in each CNN architecture. Several models of CNN are tested in this study, both the model which used Majority Vote and Conventional CNN. The results show that the proposed model achieves an accuracy value average F1-Score 0.992 and Accuracy 0.993, according to 5 K-Fold test. The best model is CNN, which used Soft Majority Vote.
Pengolahan citra digital memiliki manfaat yang bisa digunakan dalam lingkup yang beragam, salah satunya dalam lingkungan perkebunan kopi. Dengan memanfaatkan pengolahan citra, citra daun yang didapat dalam perkebunan kopi, bisa diketahui jenis kopi beserta penyakit yang diderita. Untuk mengetahui jenis daun akan menggunakan metode euclidean distance, dimana daun yang digunakan sebagai objek penelitian merupakan daun kopi robusta dan daun kopi arabika. Untuk penyakit pada daun kopi terdapat berbagai macam, namun penyakit yang digunakan sebagai objek penelitian hanyalah penyakit brown eye spot. Pendeteksian penyakit dilakukan menggunakan metode hough transform, dikarenakan metode ini dapat digunakan untuk mendeteksi lingkaran yang merupakan gejala dari penyakit brown eye spot . Tujuan dalam penelitian ini yaitu untuk menganalisa keefektifan dari metode yang digunakan, yang pertama yaitu tingkat akurasi euclidean distance untuk mendeteksi daun uji coba antara daun kopi arabika dan daun kopi robusta. Metode yang kedua menganalisa tingkat keefektifan tingkat akurasi dalam pendeteksian penyakit brown eye spot pada daun uji coba menggunakan hough transform. Uji coba dilakukan terhadap 7 daun kopi arabika dan 4 daun kopi robusta menggunakan Matlab R2017a, dimana hasil tidak terjadi kekeliruan terhadap pendeteksian tehrhadap daun uji coba, ketujuh daun kopi arabika dikenali sebagai daun kopi arabika, dan keempat daun kopi robusta dikenali sebagai daun kopi robusta. Pada metode kedua untuk pendeteksian penyakit brown eye spot pada daun uji coba didapatkan keakurasian pada daun arabika sebesar 55% dan untuk daun kopi robusta sebesar 50%.
Indonesia is an archipelagic country that has a very wide sea area. Thus, Indonesian sea has a huge potential of natural resources that can be utilized to grow the nation's economy. There are many occupations and efforts that can be done to increase the income from the sea and also to conserve it. Fishery is one of the most effective way to gain the sea resources; however, fishery is limited by the weather condition on the sea. This is also a problem that happened in Puger Beach. Puger Beach is located in the south Jember and it faces the Hindia Ocean, which means the weather condition is more dangerous for fishermen than other part of coastal. To ensure the safety of the fishermen, the weather condition on the sea must be evaluated and predicted before the fishery. This study designed a system to provide fishermen in Puger Beach an information about sea and beach weather condition which consist of wave height prediction, wind speed, temperature, humidity and weather prediction. The wind speed is obtained from self-designed anemometer system, the temperature is measured using LM35 sensor, and the humidity is assessed using DHT22. The wave height in the sea was predicted by calculating the wind speed value and effective average fetch value using neural network algorithm. The weather on the sea and on the beach were predicted by rain and light sensor. This weather prediction would be classified into three different results, namely raining, cloudy and bright. After some experiments, the result showed that the device can provide the information needed for fishermen and it has a high sensing accuracy. The humidity measurement had an average error of 1.1%, the temperature measurement had 1.42% average error, and 2.37% for the wind speed measurement. The wave height measurement system worked out and found the average wave height in Puger Beach 0.37 meters.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.