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 157 Documents
 1   2   3   4   5 
SEGMENTATION OF MOVING OBJECTS BASED ON MINKOWSKI DISTANCE USING K-MEANS CLUSTERING Moch Arief Soeleman; Moch. Hariadi; Eko Mulyanto; Mauridhi H. Purnomo
Jurnal Ilmiah Kursor Vol 8 No 3 (2016)
Publisher : Universitas Trunojoyo Madura

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28961/kursor.v8i3.75

Abstract

Segmentation of moving objects is one of the challenging research areas for video surveillance application. The success of object changing position for segmentation is when the moving object completely separate the foreground from its background of frame. It depends on many factors, including the use of suitable clustering method to differentiate the pixels of the foreground and background. This paper propose to use k-means as clustering method for moving object segmentation. The method is evaluated on several distance measures. Several steps are performed to conduct the moving object segmentation, such as frame subtraction, median filtering, and noise removal. These steps are proposed to improve the achievement of moving object segmentation. The performance are evaluated by using Mean of Square Error and Peak Signal to Noise Error. The value of both measurement are 135.02 and 25.52. The experimental result shows that the moving object segmentation performs the best result on Minkowski distance.
THE IMAGE RETRIEVAL OBJECT GANESHA IMAGE USING INVARIANT MOMENT METHOD Hendro Nugroho; Eka Prakarsa Mandyartha
Jurnal Ilmiah Kursor Vol 9 No 2 (2017)
Publisher : Universitas Trunojoyo Madura

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28961/kursor.v9i2.98

Abstract

In the findings of the statue of Ganesha in Trowulan Mojokerto area is no longer intact, because the statue of Ganesha is found to have been on the surface of soil or underground, so the archaeologist is very difficult to categorize the findings. This research proposes to overcome the above problems it is necessary to the Image Retrieval system (image retrieval) that can provide information about the results of the discovery of such historic objects. For the image taken as Image Retrieval as an example of research trials is the Ganesha Arca. From the Ganesha Statue is searched for feature extraction value by using Moment Invariant method, after which to get the result of image retrieval using Manhattan method. Image Retrieval information system work is image of Ganesa Arca in pre-processing with size 200x260 pixel BMP, then image in edge detection using Roberts method and extraction of Moment Invariant feature and inserted into database as data traning. For data testing the same process with data traning so searched the closest distance using Manhattan method. From the results of 15 image testing statues Ganesha level to the accuracy of the true states there is 62% and stated wrong 38%. Research can be further developed using various methods to improve image retrieval accuracy
COMPARISON OF FUZZY SUBTRACTIVE CLUSTERING AND FUZZYC-MEANS Annisa Eka Haryati; Sugiyarto Sugiyarto; Rizki Desi Arindra Putri
Jurnal Ilmiah Kursor Vol 11 No 1 (2021)
Publisher : Universitas Trunojoyo Madura

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21107/kursor.v11i1.254

Abstract

Multivariate statistics have related problems with large data dimensions. One method that can be used is principal component analysis (PCA). Principal component analysis (PCA) is a technique used to reduce data dimensions consisting of several dependent variables while maintaining variance in the data. PCA can be used to stabilize measurements in statistical analysis, one of which is cluster analysis. Fuzzy clustering is a method of grouping based on membership values ​​that includes fuzzy sets as a weighting basis for grouping. In this study, the fuzzy clustering method used is Fuzzy Subtractive Clustering (FSC) and Fuzzy C-Means (FCM) with a combination of the Minkowski Chebysev distance. The purpose of this study was to compare the cluster results obtained from the FSC and FCM using the DBI validity index. The results obtained indicate that the results of clustering using FCM are better than the FSC.
POINT CORRESPONDENCE CORRECTION BASED ON SURFACE CURVATURE FEATURES Eko Mulyanto Yuniarno; Mochamad Hariadi; Mauridhi Hery Purnomo
Jurnal Ilmiah Kursor Vol 6 No 4 (2012)
Publisher : Universitas Trunojoyo Madura

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

3D computer model of a real object has been widely used in various applications such as motion capture, computer vision and computer graphics. To build a 3D computer model, multiview data point cloud of real object from different view point that obtained from a 3D scanner must be registered to placing the multiview data point cloud into a common coordinate system. Correspondence to find pair point matching is an important step in registration. False correspondence will affected to the registration quality.A novel technique of point correspondence correction between two point clouds is presented in this paper. The correspondence technique is started by selecting pair point matching candidate base on two reference point constraint then followed by correspondence correction using surface curvature feature. We tested the technique by applying thecorrespondence correction technique into three registration algorithm registration which is ICP, ICP-AIF and ICP-SCF then compare it with the original registration algorithm The result shows that registration algorithm using correspondence correction 63% faster, 23% more accurate and to find 530% more correct pair matching point than the original registration algorithm.
SPEECH RECOGNITION OF KV-PATTERNED INDONESIAN SYLLABLE USING MFCC, WAVELET AND HMM Syahroni Hidayat; Risanuri Hidayat; Teguh Bharata Adji
Jurnal Ilmiah Kursor Vol 8 No 2 (2015)
Publisher : Universitas Trunojoyo Madura

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28961/kursor.v8i2.63

Abstract

The Indonesian language is an agglutinative language which has complex suffixes and affixes attached on its root. For this reason there is a high possibility to recognize Indonesian speech based on its syllables. The syllable-based Indonesian speech recognition could reduce the database and recognize new Indonesian vocabularies which evolve as the result of language development. MFCC and WPT daubechies 3rd (DB3) and 7th (DB7) order methods are used in feature extraction process and HMM with Euclidean distance probability is applied for classification. The results shows that the best recognition rateis 75% and 70.8% for MFCC and WPT method respectively, which come from the testing using training data test. Meanwhile, for testing using external data test WPT method excel the MFCC method, where the best recognition rate is 53.1% for WPT and 47% for MFCC. For MFCC the accuracy increased asthe data length and the frame length increased. In WPT, the increase in accuracy is influenced by the length of data, type of the wavelet and decomposition level. It is also found that as the variation of state increased the recognition for both methods decreased.
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.
Hierarchical Clustering for Functionalities E-Commerce Adoption Evi Triandini; Fajar Astuti Hermawati; I Ketut Putu Suniantara
Jurnal Ilmiah Kursor Vol 10 No 3 (2020)
Publisher : Universitas Trunojoyo Madura

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21107/kursor.v10i3.230

Abstract

Web functionality is one driver for e-commerce adoption. It is appeared the level of technological capabilities as well as the accentuation of the strategy put on e-commerce by the organization. Web functionality is related to the level of e-commerce relocation. Website with more functionality will give way better benefits for shoppers and trade partners. Functionalities of web are components that support the achievement of adoption benefits. Hierarchical clustering and ranking availability of e-commerce functionality is a challenging task. Ward Linkage algorithm was used to measure distance. This study proposed to get a grouping of e-commerce functionalities that influence e-commerce adoption and to get the ranking of the groups that most influence the achievement of these benefits. Result shows that functionalities that supports the achievement of every benefit of e-commerce has been clustered into two or three clusters, where each cluster also has been ranked to facilitate the achievement of these benefits
PEMISAHAN GIGI PADA DENTAL PANORAMIC RADIOGRAPH DENGAN MENGGUNAKAN INTEGRAL PROJECTION YANG DIMODIFIKASI Bilqis Amaliah; Anny Yuniarti; Anindita Sigit Nugroho; Agus Zainal Arifin
Jurnal Ilmiah Kursor Vol 6 No 2 (2011)
Publisher : Universitas Trunojoyo Madura

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

Tidak mudah untuk mengetahui identitas seorang korban, jika sebagian besar tubuhnya sudah tak berbentuk lagi. Terdapat banyak cara untuk mengidentifikasi korban yang meninggal dunia, antara lain dengan DNA, sidik jari dan citra gigi. Gigi merupakan bagian dari tubuh yang biasanya masih utuh, karena struktur gigi yang padat. Sehingga peneliti mengajukan penelitian tentang identifikasi korban dengan menggunakan citra gigi. Terdapat beberapa tahap untuk identifikasi korban menggunakan citra gigi. Tahapan awal dan sangat menentukan adalah tahap pemisahan citra gigi. Dengan semakin akuratnya hasil dari pemisahan citra gigi, maka akan semakin akurat pula hasil identifikasi korban menggunakan citra gigi. Pemisahan citra gigi yang dilakukan adalah menggunakan metode Integral Projection yang dimodifikasi. Metode Integral Projection yang dimodifikasi ini digunakan untuk memberi garis pemisah antara satu gigi dengan gigi lainnya. Citra gigi yang digunakan adalah dental panoramic radiograph. Keberhasilan Integral Projection biasa dalam memisahkan antara gigi adalah 88,23 %, sedangkan dengan menggunakan Integral Projection yang dimodifikasi meningkat menjadi 93,47 %. Kata Kunci: Dental Panoramic Radiograph, Segmentasi, Integral Projection. Abstract It’s not easy to find out the identity of a victim, if most of his body was not shaped anymore. There are some ways to identify a victims, for example are using DNA matching, fingerprints and dental image. Teeth are part of the body that usually remains intact, because the solid tooth structure. Because of that, identify victim using dental image are purposed. There are several stages for victim identification using dental images. The first stage and the important one is teeth separation. The more accurate the results of the teeth separation, the more accurate the identification victim using dental images. Teeth separation is using modified integral projection method. The modified integral projection method is to make a line between the teeth so that the result is more accurate than the ordinary integral projection. In this research, dental panoramic radiographs are used. Accuration of ordinary integral projection is 88,23 %, and modified projection integral is 93,47 %.
QUALITY IMPROVEMENT OF OBJECT EXTRACTION FOR KEYFRAME DEVELOPMENT BASED ON CLOSED-FORM SOLUTION USING FUZZY CMEANS AND DCT-2D Ruri Suko Basuki; Mochamad Hariadi; Mauridhi Hery Purnomo
Jurnal Ilmiah Kursor Vol 7 No 2 (2013)
Publisher : Universitas Trunojoyo Madura

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

QUALITY IMPROVEMENT OF OBJECT EXTRACTION FOR KEYFRAME DEVELOPMENT BASED ON CLOSED-FORM SOLUTION USING FUZZY CMEANS AND DCT-2D aRuri Suko Basuki, bMochamad Hariadi, cMauridhi Hery Purnomo a,b,cFaculty of Industrial Technology, Dept. of Electrical Engineering Institut Teknologi Sepuluh Nopember, Kampus ITS Keputih, Sukolilo, Surabaya, Jawa Timur, Indonesia a Faculty of Computer Science, Dian Nuswantoro University Jalan Imam Bonjol, Semarang, Indonesia E-mail: a rurisb@research.dinus.ac.id Abstrak Penelitian ini bertujuan untuk meningkatkan kualitas ekstraksi obyek pada citra tunggal hasil pemecahan frame dari video sekuensial yang terkompresi. Kualitas hasil ekstraksi obyek dengan algoritma closed-form solution menurun karena adanya beberapa perubahan nilai intensitas pada channel RGB. Sehingga di sekitar batas tepi obyek hasil ekstraksi terlihat kasar baik secara visual maupun hasil pengukuran dengan Mean Squared Error (MSE) antara obyek hasil ekstraksi dengan ground truth. Untuk meningkatkan kualitas hasil ekstraksi objek, nilai threshold pada unknown region ditentukan melalui adaptive threshold yang diperoleh dengan mengaplikasikan algoritma Fuzzy C-Means (FCM). Pemilihan algoritma FCM karena dalam penelitian sebelumnya algoritma ini menunjukkan hasil yang lebih robust dibandingkan algoritma Otsu untuk mendapatkan nilai threshold yang optimal. Sedangkan untuk menghaluskan obyek di sekitar daerah batas tepi digunakan filter Discrete Cosine Transform (DCT) – 2D. Dari 10 obyek yang digunakan dan dievaluasi dengan MSE menunjukkan peningkatan rata-rata sebesar 31.55%. Namun pendekatan ini tidak begitu robust pada citra yang memiliki kemiripan warna. Penggabungan pendekatan ini dengan optimasi cost function dalam alpha region pada basis spectrum diharapkan mampu meningkatkan kinerja algoritma ekstraksi obyek pada penelitian selanjutnya. Kata kunci: Closed-form Solution, Algoritma Fuzzy C-Means, Discrete Cosine Transform-2D. Abstract The research is aimed to improve the quality of the extraction of the object in a single image resulted from frame’s fragmentation of sequential compressed video. The quality of the extracted objects with closed-form solution algorithm decreased due to some changes in the intensity values on the RGB channel. Thus, the extraction result around the boundary edges of objects visually seemed to be rough and when it was measured with the Mean Squared Error (MSE) beween the object extraction results with ground truth. To improve the quality of the extracted object, the threshold value on unknown region was determined by adaptive threshold obtained by applying the Fuzzy C-Means algorithm (FCM). FCM algorithm is chosen since in the previous research this algorithm gives more robust results than Otsu algorithm to obtain the optimal threshold value. Meanwhile, to eliminate noise around the border area, this research applies Discrete Cosine Transform (DCT) - 2D filters. The result of 10 objects used and evaluated with the MSE showed an average increase of 31.55%. However, this approach is not so robust to images having similar color. Combination of this approach with optimization of the cost function on the alpha region based on spectrum is expected improving the performance of object extraction algorithm for the next research. Key words: Closed-form Solution, Fuzzy C-Means Algorithm, Discrete Cosine Transform-2D
CAMPUS SENTIMENT ANALYSIS E-COMPLAINT USING PROBABILISTIC NEURAL NETWORK ALGORITHM Mohammad Zoqi Sarwani; Wayan Firdaus Mahmudy
Jurnal Ilmiah Kursor Vol 8 No 3 (2016)
Publisher : Universitas Trunojoyo Madura

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28961/kursor.v8i3.88

Abstract

E-complaint is one of the technologies which is used to collect feedback from customers in the form of criticism and suggestions using electronic systems. For some companies or agencies, ecomplaint is used to provide better services to its customers. This study is aimed to perform sentiment analysis of an e-complaint service, with the case of Brawijaya University. There are three main stages for the proposed system, i.e. Text Preprocessing, Text Weighting, and PNN forthe classification. Tokenization, filtering, and stemming are done in the text preprocessing. Resulted text from the preprocessing stage is weighting using Term Inverse Document Frequent (TFIDF). To classify the negative or positive complaints, PNN are used in the last stage. For the experiments, 70 data are used as the training data, and 20 data are used as the testing data. The experimental results based on the combination of the number of training and testing dataset, showed that the accuracy achieved up to 90%.

Page 2 of 16 | Total Record : 157

 1   2   3   4   5 

Filter by Year

2011 2025


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