cover
Article Per Year (5 Year)
All
Home Page
OAI Link
Editorial Team
Contact
Reviewer
Google Scholar
Contact Name
Yeni Kustiyahningsih
Contact Email
ykustiyahningsih@trunojoyo.ac.id
Phone
+6282139239387
Journal Mail Official
kursor@trunojoyo.ac.id
Editorial Address
Informatics Department, Engineering Faculty University of Trunojoyo Madura Jl. Raya Telang - Kamal, Bangkalan 69162, Indonesia Tel: 031-3012391, Fax: 031-3012391
Location
Kab. bangkalan,
Jawa timur
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 6 Documents
 1 
Search results for , issue "Vol 8 No 4 (2016)" : 6 Documents clear
ONLINE KERNEL AMGLVQ FOR ARRHYTHMIA HEARBEATS CLASSIFICATION Elly Matul Imah; R. Sulaiman
Jurnal Ilmiah Kursor Vol 8 No 4 (2016)
Publisher : Universitas Trunojoyo Madura

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28961/kursor.v8i4.108

Abstract

This study proposes Online Kernel Adaptive Multilayer Generalized Learning Vector Quantization (KAMGLVQ) for handling imbalanced data sets. KAMGLVQ is extended version of AMGLVQ that used kernel function to handling non-linear classification problems. Basically AMGLVQ is vector quantization based learning. The vector quantization based learning is very simple algorithm that can be applied to the multiclass problem and the complexity of LVQ can be controlled during training process. KAMGLVQ works at online kernel learning system that integrating feature extraction and classification. The architecture network of KAMGLVQ consists of three layers, input layer, hidden layer, and an output layer. The hidden layer of KAMGLVQ is adaptive; this algorithm will generate a number of hidden layer nodes. The algorithm implement on real ECG signals from the MIT-BIH arrhythmias database and synthetic data. The experiments showed that KAMGLVQ able improve the accuracy of classification better than SVM or back-propagation NN; also able to reduce the time computational cost.
A COMBINATION DEEP BELIEF NETWORKS AND SHALLOW CLASSIFIER FOR SLEEP STAGE CLASSIFICATION Intan Nurma Yulita; Rudi Rosadi; Sri Purwani; Rolly Maulana Awangga
Jurnal Ilmiah Kursor Vol 8 No 4 (2016)
Publisher : Universitas Trunojoyo Madura

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28961/kursor.v8i4.97

Abstract

In this research, it is proposed to use Deep Belief Networks (DBN) in shallow classifier for the automatic sleep stage classification. The automatic classification is required to minimize Polysomnography examination time because it needs more than two days for analysis manually. Thus the automatic mechanism is required. The Shallow classifier used in this research includes Naïve Bayes (NB), Bayesian Networks (BN), Decision Tree (DT), Support Vector Machines (SVM), and K-Nearest Neighbor (KNN). The results obtained that many methods of the shallow classifier are increasing precision, recall, and F-Measure if they use DBN output as input for classification. Experiments that have been done indicate a significant increase of Naive Bayes after being combined with DBN. The high-level features generated by DBN are proven to be useful in helping Naive Bayes' performance. On the other hand, the combination of KNN with DBN shows a decrease because high-level features of DBN make it harder to find neighbors that optimize the performance of KNN.
A DATA ANALYSIS OF THE IMPACT OF NATURAL DISASTER USING K-MEANS CLUSTERING ALGORITHM Prihandoko Prihandoko; Bertalya Bertalya
Jurnal Ilmiah Kursor Vol 8 No 4 (2016)
Publisher : Universitas Trunojoyo Madura

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28961/kursor.v8i4.109

Abstract

Indonesia is one of the country with a lot of natural disasters occurred every year. The victims of natural disasters, are quite high in terms of the number of deaths, missing people, injuries, sufferings and the number of refugees. Unfortunately, the number of victims is growing from year to year in the last ten years. Thus, based on this condition, this research is carried out in order to analyze the data of the natural disasters and their victims for the last five years. The analysis is intended to know what is the main cause of natural disaster. The series of data about the natural disaster and the weather condition is collected from the government office website. The analysis was carried out by implementing clustering technique to the data, by using k-means algorithm, after data preprocessing completed. The result of the research shows that the weather condition is not the main cause of the occurrence of natural disaster, but the geographical condition is the main trigger of the problem. In addition, this research also found that the data published by the government need to be updated regularly.
PRE-PROCESSED LATENT SEMANTIC ANALYSIS FOR AUTOMATIC ESSAY GRADING Ruth Ema Febrita; Wayan Firdaus Mahmudy
Jurnal Ilmiah Kursor Vol 8 No 4 (2016)
Publisher : Universitas Trunojoyo Madura

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28961/kursor.v8i4.110

Abstract

In education, essay is considered as the best tool to evaluate student’s high order thinking and understanding. In the other hand, manual processing and grading essay answers by a teacher need much time and tending to subjectivity grading. Meanwhile automatic essay grading in e-learning system find the difficulties in comparing model or key answer to student’s answer because student’s can answer the question with so various way. That means a right answer also can be so various, for they have same semantic meaning. This paper proposed automatic essay grading using Latent Semantic Analysis. But before the texts being scored, they will be pre-processed using stop words removal and synonyms checking. Calibration process implemented for dealing with the various possible right answer and help to simplify the term matrix. Implementation of this approach using Java Programming Language and WordNet as lexical database for searching the synonyms of every given words. The accuracy obtained by this method is 54.9289%.
MEDICINAL PLANT SPECIES IDENTIFICATION SYSTEM USING TEXTURE ANALYSIS AND MEDIAN FILTER Prihastuti Harsani; Arie Qurania; Triasti nurmiatiningsih
Jurnal Ilmiah Kursor Vol 8 No 4 (2016)
Publisher : Universitas Trunojoyo Madura

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28961/kursor.v8i4.112

Abstract

Identification of plants can be done through objects - objects in plants by asking an expert or through a specimen (herbarium) that have been identified previously. Identification is done by matching the pictures in the book of flora or monograph. Computer-aided identification can be done using digital image processing methods which utilize digital image matching object plant with a picture on the book. Identification key that is used is the image of the leaves. This study develops previous research has identified using the method of fractal and Euclidian Distance. Accuracy obtained in each of the identification system for the fractal dimension and fractal code is of 68% and 51%. Improved accuracy is the main objective of this study. The proposed method is a method of texture analysis and median filter. Texture analysis is used as feature extraction technique while the median filter is image enhancement techniques. Based on the trials, the results of the identification of texture analysis method and median filter to increase to 78%. Median filter is used as a technique to improve the image quality leaves. The use of an identification system to be tested in the web application of information systems of medicinal plants.
KWH METER IMAGE ENHANCEMENT USING COLOR SPACE TRANSFORMATION FOR IMPROVING CHARACTER SEGMENTATION ACCURACY Shinta Puspasari; Lastri Widya Astuti
Jurnal Ilmiah Kursor Vol 8 No 4 (2016)
Publisher : Universitas Trunojoyo Madura

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28961/kursor.v8i4.113

Abstract

This study proposes an approach to enhance image quality of power meter (KWH meter) using a color space transformation. Power meter is used to measure the power consumption of the customer. The amount of usage can be seen by looking at the character of numbers as indicators of measurement. Billing can be done automatically using character recognizer on imaging device by applying digital image processing techniques. Acquired image of the power meter may have poor quality because data acquisition process is very sensitive to light and noise. Appliance power meter is covered with glass that can reflect light, so that the quality of the acquired image varies depends on the lighting conditions at the time of acquisition. Color space transformation widen the color contrast of KWH meter image. The performance of the proposed approach is evaluated using a data set of KWH meter images of Smart Meter Indonesia models contains 30 RGB color model images. Before performs the proposed method, segmentation effectivesess is 93%. The experimental results shows an improvement of image quality that affect the character segmentation results up to 97%. Color space transformation is proven effective for the improvement of image quality and segmentation of KWH meter.

Page 1 of 1 | Total Record : 6

 1 

Filter by Year

2016 2016


Reset
Filter By Issues
All Issue Vol. 13 No. 2 (2025) Vol. 13 No. 1 (2025) Vol. 12 No. 4 (2024) Vol. 12 No. 3 (2024) Vol. 12 No. 2 (2023) Vol 12 No 1 (2023) Vol 11 No 4 (2022) Vol 11 No 3 (2022) Vol 11 No 2 (2021) Vol 11 No 1 (2021) Vol 10 No 4 (2020) Vol 10 No 3 (2020) Vol 10 No 2 (2019) Vol 10 No 1 (2019) Vol 9 No 4 (2018) Vol 9 No 3 (2018) Vol 9 No 2 (2017) Vol 9 No 1 (2017) Vol 8 No 4 (2016) Vol 8 No 3 (2016) Vol 8 No 2 (2015) Vol 8 No 1 (2015) Vol 7 No 4 (2014) Vol 7 No 3 (2014) Vol 7 No 2 (2013) Vol 7 No 1 (2013) Vol 6 No 4 (2012) Vol 6 No 3 (2012) Vol 6 No 2 (2011) Vol 6 No 1 (2011) More Issue