scholarly journals PENERAPAN ALGORITMA EVOLVING NEURAL NETWORK UNTUK PREDIKSI CURAH HUJAN

2016 ◽  
Vol 1 (1) ◽  
pp. 1-8
Author(s):  
Subhan Panji Cipta
Keyword(s):  
Neural Network ◽  
Flood Control ◽  
Early Stage ◽  
Economic Value ◽  
Weather Forecast ◽  
Decision Makers ◽  
Test Results ◽  
The Public ◽  
Rainfall Prediction ◽  
Better Than

Weather and climate information have contributed as one consideration for decision makers. This arises because the information the weather / climate has economic value in a variety of activities , ranging from agriculture to flood control . From the data obtained implied that the current rainfall prediction not so accurate . Forecasts are often given to the public on a regular basis is the weather forecast , not the amount of rainfall. This study uses an algorithm Evolving Neural Network (ENN) as an approach to predict the rainfall , the data processing and calculations will use MatLab 2009b . The parameters used in this study is time , rainfall , humidity and temperature. The results also compared with the test results and predictions BPNN BMKG. From the results of research conducted from early stage to test and measurement , the application of this ENN has a rainfall prediction with accuracy better than the BPNN and prediction algorithms BMKG.  

scholarly journals Generating Probabilistic Next-Day Severe Weather Forecasts from Convection-Allowing Ensembles Using Random Forests

2020 ◽  
Vol 35 (4) ◽  
pp. 1605-1631
Author(s):  
Eric D. Loken ◽  
Adam J. Clark ◽  
Christopher D. Karstens
Keyword(s):  
Random Forests ◽  
Weather Forecast ◽  
Severe Weather ◽  
Decision Makers ◽  
Limited Information ◽  
Weather Forecasts ◽  
Skill Scores ◽  
Weather Forecasters ◽  
Forecast Data ◽  
Better Than

AbstractExtracting explicit severe weather forecast guidance from convection-allowing ensembles (CAEs) is challenging since CAEs cannot directly simulate individual severe weather hazards. Currently, CAE-based severe weather probabilities must be inferred from one or more storm-related variables, which may require extensive calibration and/or contain limited information. Machine learning (ML) offers a way to obtain severe weather forecast probabilities from CAEs by relating CAE forecast variables to observed severe weather reports. This paper develops and verifies a random forest (RF)-based ML method for creating day 1 (1200–1200 UTC) severe weather hazard probabilities and categorical outlooks based on 0000 UTC Storm-Scale Ensemble of Opportunity (SSEO) forecast data and observed Storm Prediction Center (SPC) storm reports. RF forecast probabilities are compared against severe weather forecasts from calibrated SSEO 2–5-km updraft helicity (UH) forecasts and SPC convective outlooks issued at 0600 UTC. Continuous RF probabilities routinely have the highest Brier skill scores (BSSs), regardless of whether the forecasts are evaluated over the full domain or regional/seasonal subsets. Even when RF probabilities are truncated at the probability levels issued by the SPC, the RF forecasts often have BSSs better than or comparable to corresponding UH and SPC forecasts. Relative to the UH and SPC forecasts, the RF approach performs best for severe wind and hail prediction during the spring and summer (i.e., March–August). Overall, it is concluded that the RF method presented here provides skillful, reliable CAE-derived severe weather probabilities that may be useful to severe weather forecasters and decision-makers.

scholarly journals Automatic Cigarette Object Concealment in Video using R-CNN

2021 ◽  
Vol 5 (1) ◽  
pp. 21
Author(s):  
Kadek Utari Widiarsini ◽  
Duman Care Khrisne ◽  
I Made Arsa Suyadnya
Keyword(s):  
Neural Network ◽  
Causes Of Death ◽  
Test Results ◽  
Tobacco Products ◽  
Application Performance ◽  
Nicotiana Rustica ◽  
Heart Attacks ◽  
The Public ◽  
The World ◽  
Lung Infections

Cigarettes are packaged processed tobacco products, produced from the Nicotiana Tabacum, Nicotiana Rustica plants and other species or synthetics that contain nicotine with or without additives. Smoking is known to the public as one of the causes of death in the world that is quite large such as asthma, lung infections, oral cancer, throat cancer, lung cancer, heart attacks, strokes, dementia, erectile dysfunction (impotence), and so on. This research aims to build an application that can recognize cigarettes automatically and conceal pictures so that people especially minors are not affected by cigarettes. The application is built using the Region-based Convolutional Neural Network (R-CNN) method. The study uses images that have cigarette objects in them. The test is carried out to find out the application performance such as the level of application accuracy in recognizing cigarette objects. Based on the test results with a sample of 126 cigarette images, the application built is able to recognize cigarette objects by obtaining an accuracy value of 63%.

scholarly journals ANALISIS SENTIMEN PT TIKI JALUR NUGRAHA EKAKURIR (PT TIKI JNE) PADA MEDIA SOSIAL TWITTER MENGGUNAKAN MODEL FEED FORWARD NEURAL NETWORK

2020 ◽  
Vol 8 (2) ◽  
pp. 103
Author(s):  
Salma Farah Aliyah ◽  
Hasbi Yasin ◽  
Suparti Suparti ◽  
Budi Warsito ◽  
Tatik Widiharih
Keyword(s):  
Neural Network ◽  
Evaluation Model ◽  
Numerical Data ◽  
Test Results ◽  
Feed Forward Neural Network ◽  
The Public ◽  
Freight Forwarding ◽  
Initial Source ◽  
Accuracy Performance ◽  
Fold Cross Validation

In the 2000s until now, e-commerce systems have continued to develop throughout the world and even in Indonesia. PT Tiki Jalur Nugraha Ekakurir (PT Tiki JNE) is a freight forwarding company that provides convenience for the public in carrying out online shopping activities, and shipping other goods. The large volume of shipments makes PT Tiki JNE have several problems in service that have led to several kinds of responses from users. Sentiment analysis on Twitter social media can be an option to see how PT Tiki JNE’s users respond to services that have been provided. These responses are classified into positive sentiments and negative sentiments. In this research data processing is performed using text mining as the initial source of numerical data from document data which will later be classified using the Artificial Neural Network model with the Resilient Backpropagation algorithm. Data labeling is done manually and sentiment scoring. The test results show that the best model obtained is FFNN 867-7-1 by using the evaluation model 10-Fold Cross Validation to get an overall accuracy performance of 80.27%, kappa accuracy of 39.13%, precision of 69.04%, recall of 70.56%, and f-measure of 69.8% which can be interpreted that the model used is quite good. Analysis of the results using wordcloud shows the tendency of opinion sentiment categories depending on the words used in the tweet.

scholarly journals MODEL CNN LENET DALAM PENGENALAN JENIS GOLONGAN KENDARAAN PADA JALAN TOL

2020 ◽  
Vol 12 (2) ◽  
pp. 65
Author(s):  
Anggay Luri Pramana ◽  
Endang Setyati ◽  
Yosi Kristian
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Vehicle Classification ◽  
Test Results ◽  
Field Of Study ◽  
Neural Network Method ◽  
The Public ◽  
Network Method

Research in the field of transportation, especially vehicle classification with various methods, is a widely developed field of study. Vehicles can be categorized by shape, dimension, logo, and  type. The vehicle dataset is also not difficult to find because it is general in nature. Based on the research that has been done, the introduction of group types based on the number of axles with CNN, the dataset is not yet available to the public. In this paper, we discuss the introduction of the types of groups using the Convolutional Neural Network method. The architecture used is the LeNet model. The trial scenario is carried out in 4 stages, namely 25 epochs, 50 epochs, 75 epochs and 100 epochs. Based on the test results, the accuracy obtained continues to increase at 50 epochs and 100 epochs iterations. Starting from an accuracy of 82%, 94% to the highest accuracy of 95%. Likewise in the prediction the data has increased from 80%, 85% to the highest accuracy that can be 86%. From 50 epochs to 75 epochs, the accuracy of both training and testing has decreased.

Study on Wood Drying Based on Neural Network and Expert System

2011 ◽  
Vol 467-469 ◽  
pp. 1164-1169
Author(s):  
Hong E Ren ◽  
Long Chen ◽  
Ji Feng Guo
Keyword(s):  
Neural Network ◽  
Expert System ◽  
Control Method ◽  
Linear Process ◽  
Time Variable ◽  
Test Results ◽  
Drying Method ◽  
Wood Drying ◽  
Drying Model ◽  
Better Than

Wood drying is a complicated non-linear process that is lagged,time-variable and coupling. For them,it is difficult to build up ideal drying model,which is corresponding to reality.The paper put forward the drying method of vacuum dehumidification.This drying method is based on neural network and expert system. The test results showed that the control method were better than traditional PID control.The control system could meet requirement of precision and a fast convergence and small overshoot.

scholarly journals Automatic Detection of Tuberculosis from Chest X-Rays using Convolutional Neural Network

2020 ◽  
Vol 9 (5) ◽  
pp. 72-77
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Early Stage ◽  
Automatic Detection ◽  
X Rays ◽  
X Ray ◽  
Chest X Ray ◽  
Causes Of Deaths ◽  
Graph Cut Segmentation ◽  
Better Than

Tuberculosis is one of the single infectious diseases which is one among the top ten causes of deaths. Eradication is only possible by timely diagnosis of disease and treatment at its early stage. But unfortunately, timely detection is lagging due to many reasons. In this angle we present a novel scheme for automatic detection of tuberculosis from chest X-ray images. The proposed method accurately detects the malady by performing graph cut segmentation followed by classification using convolutional neural network. The classifier facilitates the chest X-rays to be classified as normal or abnormal. Simulation results show that the accuracy of 94%, sensitivity of 96% and specificity of 84% obtained from the proposed system are comparable and even better than the existing reported methods.

scholarly journals Semantic Graph Neural Network: A Conversion from Spam Email Classification to Graph Classification

2022 ◽  
Vol 2022 ◽  
pp. 1-8
Author(s):  
Weisen Pan ◽  
Jian Li ◽  
Lisa Gao ◽  
Liexiang Yue ◽  
Yan Yang ◽  
...  
Keyword(s):  
Neural Network ◽  
Classification Problem ◽  
Graph Classification ◽  
Method Performance ◽  
The Public ◽  
Semantic Graph ◽  
Public Dataset ◽  
Public Datasets ◽  
Better Than ◽  
Email Classification

In this study, we propose a method named Semantic Graph Neural Network (SGNN) to address the challenging task of email classification. This method converts the email classification problem into a graph classification problem by projecting email into a graph and applying the SGNN model for classification. The email features are generated from the semantic graph; hence, there is no need of embedding the words into a numerical vector representation. The method performance is tested on the different public datasets. Experiments in the public dataset show that the presented method achieves high accuracy in the email classification test against a few public datasets. The performance is better than the state-of-the-art deep learning-based method in terms of spam classification.

Implementasi Voip Server Berbasis IPV6 Dengan Raspberry PI

2019 ◽  
Vol 9 (01) ◽  
pp. 47-54
Author(s):  
Rabbai San Arif ◽  
Yuli Fitrisia ◽  
Agus Urip Ari Wibowo
Keyword(s):  
Internet Protocol ◽  
Wireless Transmission ◽  
Raspberry Pi ◽  
Test Results ◽  
Average Delay ◽  
Ipv6 Network ◽  
Ip Network ◽  
Selection Of ◽  
Better Than

Voice over Internet Protocol (VoIP) is a telecommunications technology that is able to pass the communication service in Internet Protocol networks so as to allow communicating between users in an IP network. However VoIP technology still has weakness in the Quality of Service (QoS). VOPI weaknesses is affected by the selection of the physical servers used. In this research, VoIP is configured on Linux operating system with Asterisk as VoIP application server and integrated on a Raspberry Pi by using wired and wireless network as the transmission medium. Because of depletion of IPv4 capacity that can be used on the network, it needs to be applied to VoIP system using the IPv6 network protocol with supports devices. The test results by using a wired transmission medium that has obtained are the average delay is 117.851 ms, jitter is 5.796 ms, packet loss is 0.38%, throughput is 962.861 kbps, 8.33% of CPU usage and 59.33% of memory usage. The analysis shows that the wired transmission media is better than the wireless transmission media and wireless-wired.

The Road to Iraq

2014 ◽  
Author(s):  
Muhammad Idrees Ahmad
Keyword(s):  
Foreign Policy ◽  
Iraq War ◽  
Empirical Investigation ◽  
Decision Makers ◽  
The Public ◽  
The Road ◽  
Cohesive Group ◽  
Political Agents

The Road to Iraq is an empirical investigation that explains the causes of the Iraq War, identifies its main agents, and demonstrates how the war was sold to decision makers and by decision makers to the public. It shows how a small but ideologically coherent and socially cohesive group of determined political agents used the contingency of 9/11 to outflank a sceptical foreign policy establishment, military brass and intelligence apparatus and provoked a war that has had disastrous consequences.

Determinants of English as language choice among Unaccompanied Migrant Children (UMC) in Makassar, Indonesia

2019 ◽  
Vol 2 (2) ◽  
pp. 1-7
Author(s):  
Andi Samsu Rijal ◽  
Andi Mega Januarti Putri
Keyword(s):  
Language Choice ◽  
Migrant Children ◽  
Immigrant Communities ◽  
The Public ◽  
International Language ◽  
Language User ◽  
Public Area ◽  
Better Than ◽  
Language Mastery

The essence of language is human activity. Communication with language is carried out through two basic human activities; speaking and listening during the interaction in a group of people. Immigrants in Makassar city communicate with immigrant communities and Makassar people. They used English and Indonesia to communicate with others. The aims of this article were to find out determinant factors of English as language choice among Unaccompanied Migrant Children (UMC) in Makassar and why they used English as their language choice to communicate with other people out of them. The data were taken from UMC in the shelter under the auspices of Makassar’s Social Office and in the public area of Makassar. This research was a qualitative approach; it was from a sociolinguistic perspective and focuses its analysis with the language choice among UMC. This research showed that most immigrants chose English as their language choice since they were in Makassar because they have acquired better than other international language and it has been mastered naturally by doing social interaction among themselves and people outside their community. UMC had more difficulties to socialize with Indonesian than the adult of Immigrants. Other than their lack of language mastery, they also have the anxiety to adapt to other immigrants and Makassar people. English was used by UMC to show their status as a foreigner who lived in a multicultural situation. Language becomes a power for a human being and it becomes a social identity for language user in one community. During the interaction of UMC in Makassar city, the role of English as an International language is shown.

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