Harmayani
@una.ac.id
Teknik Informatika Fakultas Teknik
Universitas Asahan
RESEARCH, TEACHING, or OTHER INTERESTS
Computer Science, Human-Computer Interaction, Information Systems, Information Systems and Management
Scopus Publications
Scholar Citations
Scholar h-index
Scholar i10-index
Scopus Publications
Puput Dani Prasetyo Adi, Khairul, Samsir Samsir, Harmayani, Firman Edi, Iwan Purnama, Muhammad Yasir, Agoestina Mappadang, Fairuz Iqbal Maulana, Gatot Suharto Abdul Fatah,et al.
IEEE
This research reviews the Performance of LoRaWAN using RF96 and simulation OMNeT++. RF96 is one of the chips used for LoRaWAncommunication; as a whole, LoRa Communication is formed from Radio Frequency communication between LoRa end-nodes and TCP/IP on the programming side of the Gateway and Application Server. This article explicitly discusses RF communication systems with many nodes or multi-nodes. This research uses OMNET++ simulation, which integrates and equalizes all components of RF96, starting from Bit-rate ability, SF, Bandwidth, CR, and other details integrated into one OMNET++ parameter. The parameters that can be calculated are RSSI, SNR, Bit-Rate Error Rate, Packet Loss, etc. This system, in detail, compares the number of end nodes communicated. The development of the results of this analysis is more of an approach to the Internet of Things; moreover, in this research, tests were conducted with 5, 10, 15, 20, 25, 50, 75, and 100 Nodes of LoRa, and analyzed in detail the output of parameters and Quality of Services from LoRaWancommunication.
Pratomo Setiaji, Bambang Widjanarko, Yuda Syahidin, Hidayatulah Himawan, Nurintan Asyiah Siregar, Harmayani, Lukman Samboteng, Nur’ Ainun Gulo, and Reni Kartikaningsih
IOP Publishing
Abstract The backpropagation algorithm has many training and activation functions that can be used to influence or maximize prediction results, all of which have their respective advantages and disadvantages. The purpose of this paper is to analyze one of the training functions of the backpropagation algorithm which can be used as a reference for use in data prediction problems in the form of models and best performance. The training function is the Bayesian Regularization method. This method is able to train the network by optimizing the Levenberg-Marquardt by updating the bias and weights. The research dataset used to analyze the data in this paper is Formal Education Participation in Indonesia 2015-2020 which consists of the School Participation Rate, the Gross Enrollment Rate, and the Pure Enrollment Rate. The 2015-2016 dataset is used as training data with a 2017 target, while the 2018-2019 dataset is the test data with a 2020 target. The models used are 2-10-1, 2-15-1, and 2-20-1. Based on the analysis and calculation process, the results of the 2-15-1 model are the best with an epoch of 217 iterations and an MSE of 0.00002945, this is because the epoch is not too large and has the smallest MSE compared to the other 2 models.
Dahlan Abdullah, F Fajriana, M Maryana, Lidya Rosnita, Andysah Putera Utama Siahaan, Robbi Rahim, P Harliana, H Harmayani, Zainuddin Ginting, Cut Ita Erliana,et al.
IOP Publishing
Image magnification is one of the branches in digital image processing that is often required in various applications such as in the field of medicine, multimedia, and in satellite imagery. As technology grows, more and more methods are used for image enlargement. In this study, the image enlargement process performs by using Bi-Cubic spline interpolation method, and the result of image try to compare between the original one and picture after enlargement.