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Yeni Kustiyahningsih
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ykustiyahningsih@trunojoyo.ac.id
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kursor@trunojoyo.ac.id
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Informatics Department, Engineering Faculty University of Trunojoyo Madura Jl. Raya Telang - Kamal, Bangkalan 69162, Indonesia Tel: 031-3012391, Fax: 031-3012391
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INDONESIA
Jurnal Ilmiah Kursor
Published by Universitas Trunojoyo Madura
ISSN : 02160544     EISSN : 23016914     DOI : https://doi.org/10.21107/kursor
Core Subject : Science,
Computer Science & IT Decision Sciences, Operations Research & Management
Jurnal Ilmiah Kursor is published in January 2005 and has been accreditated by the Directorate General of Higher Education in 2010, 2014, 2019, and until now. Jurnal Ilmiah Kursor seeks to publish original scholarly articles related (but are not limited) to: Computer Science. Computational Intelligence. Information Science. Knowledge Management. Software Engineering. Publisher: Informatics Department, Engineering Faculty, University of Trunojoyo Madura
Arjuna Subject : Ilmu Komputer (semua kategori) - Ilmu Komputer (Lain-Lain)
Articles 5 Documents
 1 
Search results for , issue "Vol 10 No 4 (2020)" : 5 Documents clear
FOREIGN TOURIST ARRIVAL FORECASTING TO BALI USING CASCADE FORWARD BACKPROPAGATION Ayu Nikki Asvikarani; I Made Widiartha; Made Agung Raharja
Jurnal Ilmiah Kursor Vol 10 No 4 (2020)
Publisher : Universitas Trunojoyo Madura

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21107/kursor.v10i4.252

Abstract

Bali has a recognized tourism potential in the world arena. In order to improve the quality and development of the tourism sector in the midst of global competition, it is necessary to formulate appropriate strategies by decision makers such as private parties and government. In support of more accurate decision making, the authors make a system of forecasting the number of foreign tourist visits to Bali Province using Cascade Forward Backpropagation (CFB) method with coverage of Australia, Japan, and United Kingdom which are the top 3 countries with the highest foreign tourist arrival to Bali in that years. Factors used as input in forecasting include the number of visits of foreign tourists the previous year, the population of countries of origin of foreign tourists, Gross Domestic Product at current prices of countries of origin of foreign tourists, and Relative Consumer Price Index Origin of foreign tourists. In this study, optimization of activation function parameters, hidden neurons, and learning rate to obtain forecasting results with the lowest error rate. Forecasting results using the CFB method produces a fairly good accuracy with MAPE range of 6 - 30% where the activation function tanh work better than sigmoid activation function.
SENTIMENT ANALYSIS OF ELECTRIC CARS USING RECURRENT NEURAL NETWORK METHOD IN INDONESIAN TWEETS Felisia Handayani; Metty Mustikasari
Jurnal Ilmiah Kursor Vol 10 No 4 (2020)
Publisher : Universitas Trunojoyo Madura

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21107/kursor.v10i4.233

Abstract

Sentiment analysis is computational research of the opinions of many people who are textually expressed against a particular topic. Twitter is the most popular communication tool among Internet users today to express their opinions. Deep Learning is a solution to allow computers to learn from experience and understand the world in terms of the hierarchy concept. Deep Learning objectives replace manual assignments with learning. The development of deep learning has a set of algorithms that focus on learning data representation. The recurrent Neural Network is one of the machine learning methods included in Deep learning because the data is processed through multi-players. RNN is also an algorithm that can recall the input with internal memory, therefore it is suitable for machine learning problems involving sequential data. The study aims to test models that have been created from tweets that are positive, negative, and neutral sentiment to determine the accuracy of the models. The models have been created using the Recurrent Neural Network when applied to tweet classifications to mark the individual classes of Indonesian-language tweet data sentiment. From the experiments conducted, results on the built system showed that the best test results in the tweet data with the RNN method using Confusion Matrix are with Precision 0.618, Recall 0.507 and Accuracy 0.722 on the data amounted to 3000 data and comparative data training and data testing of ratio data 80:20
DEEP LEARNING-BASED OBJECT RECOGNITION ROBOT CONTROL VIA WEB AND MOBILE USING AN INTERNET OF THINGS (IoT) CONNECTION Basuki Rahmat; Budi Nugroho
Jurnal Ilmiah Kursor Vol 10 No 4 (2020)
Publisher : Universitas Trunojoyo Madura

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21107/kursor.v10i4.242

Abstract

The paper presents the intelligent surveillance robotic control techniques via web and mobile via an Internet of Things (IoT) connection. The robot is equipped with a Kinect Xbox 360 camera and a Deep Learning algorithm for recognizing objects in front of it. The Deep Learning algorithm used is OpenCV's Deep Neural Network (DNN). The intelligent surveillance robot in this study was named BNU 4.0. The brain controlling this robot is the NodeMCU V3 microcontroller. Electronic board based on the ESP8266 chip. With this chip, NodeMCU V3 can connect to the cloud Internet of Things (IoT). Cloud IoT used in this research is cloudmqtt (https://www.cloudmqtt.com). With the Arduino program embedded in the NodeMCU V3 microcontroller, it can then run the robot control program via web and mobile. The mobile robot control program uses the Android MQTT IoT Application Panel.
PLANNING OF 5G NETWORK PATH LOSS IN GEOMETRY BASED STOCHASTIC CONCEPT BY USING LINEAR REGRESSION METHODS Achmad Ubaidillah; S. Ida Kholida
Jurnal Ilmiah Kursor Vol 10 No 4 (2020)
Publisher : Universitas Trunojoyo Madura

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21107/kursor.v10i4.245

Abstract

This research is a continuation of several previous studies that made 5G network planning using the Free Space Reference Path Loss model. In this study, a 5G network path loss planning was made using the Geometry Based Stochastic model. A forecasting system is created that connects the path loss with the distance between the transmitter and the receiver antenna using the linear regression method. It is important to look at 5G network planning on a different side. The result shows that the path loss value in the light of sight condition is better than the non-light of sight condition with the lowest value of 94.4271 dB at the frequency of 28 GHz and 99.5856 dB at the 73 GHz frequency. Linear Regression analysis shows that the best path loss calculation is the frequency 28 GHz of LOS conditions with MSE is 0.001 and the standard deviation error is 0.0319.
APPLICATION OF HYBRID GA-PSO TO IMPROVE THE PERFORMANCE OF DECISION TREE C5.0 Achmad Zain Nur; Hadi Suyono; Muhammad Aswin
Jurnal Ilmiah Kursor Vol 10 No 4 (2020)
Publisher : Universitas Trunojoyo Madura

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21107/kursor.v10i4.248

Abstract

Data mining is a data extraction process with large dimensions and information with the aim of obtaining information as knowledge to make decisions. Problems in the data mining process often occur in high-dimensional data processing. The solution to handling problems in high-dimensional data is to apply the hybrid genetic algorithm and particle swarm optimization (HGAPSO) method to improve the performance of the C5.0 decision tree classification model to make decisions quickly, precisely and accurately on classification data. In this study, there were 3 datasets sourced from the University of California, Irvine (UCI) machine learning repositories, namely lymphography, vehicle, and wine. The HGAPSO algorithm combined with the C5.0 decision tree testing method has the optimal accuracy for processing highdimensional data. The lymphography and vehicle data obtained an accuracy of 83.78% and 71.54%. The wine dataset has an accuracy of 0.56% lower than the conventional method because the data dimensions are smaller than the lymphography and vehicle dataset.

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