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Yeni Kustiyahningsih
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ykustiyahningsih@trunojoyo.ac.id
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+6282139239387
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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 11 No 4 (2022)" : 5 Documents clear
CASE BASED REASONING (CBR) FOR OBESITY LEVEL ESTIMATION USING K-MEANS INDEXING METHOD I Made Satria Bimantara; I Wayan Supriana
Jurnal Ilmiah Kursor Vol 11 No 4 (2022)
Publisher : Universitas Trunojoyo Madura

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21107/kursor.v11i4.268

Abstract

As many as 600 million of the 1.9 billion adults who are overweight are obese. Obesity that is not treated immediately will be a risk factor for increasing cardiovascular, metabolic, degenerative diseases, and even death at a young age. Case Based Reasoning (CBR) can be used to estimate a person's obesity level using previous cases. The old case with the highest similarity will be the solution for the new case. Indexing methods such as the K-Means Algorithm are needed so that the search for similar cases does not involve all cases on a case base so that it can shorten the computation time at the retrieve stage and still produce optimal solutions. Cosine similarity is used to find relevant clusters of new cases and Euclidean distance similarity is used to calculate similarity between cases. Random subsampling method was used to validate the CBR system. The test results with K=2 indicate that the CBR is better than the CBR-K-Means, each of which produces an average accuracy of 88.365% and 88.270% at a threshold of 0.8. CBR-K-Means produces an average computation time at the retrieve stage of 33.55 seconds and is faster than the CBR of 35.5 seconds.
SLEEP DISORDER IDENTIFICATION FROM SINGLE LEAD ECG BY IMPROVING HYPERPARAMETERS OF 1D-CNN Iman Fahruzi
Jurnal Ilmiah Kursor Vol 11 No 4 (2022)
Publisher : Universitas Trunojoyo Madura

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21107/kursor.v11i4.302

Abstract

Disruption of the flow of breathing during sleep will result in significant heart problems if not treated seriously. An electrocardiogram (ECG) recording is one of the most used methods for detecting sleep disorders early on. An ECG is a representation of electrical activity in the heart while it is beating. The irregularities of the morphology and the complexity of the recordings have clinical significance that can be used as a tool for diagnosing sleep disorders. This study uses engineering to obtain features from ECG recordings that are carried out automatically using deep learning machine learning with a Convolutional Neural Network (CNN) model approach. The ECG recordings were processed to remove noise before being used in the CNN model. Tests are carried out on the most optimal model to get good accuracy by applying two scenarios. The test results of the two scenarios show that scenario one has an accuracy of 83.03% compared to scenario two with an accuracy of 76.88%. Meanwhile, the precision, sensitivity, cohens kappa and ROC UAC levels were 81.78%, 87.78%, 65.73% and 82.68% in scenario one testing on the CNN model with the most optimal parameter settings, respectively
E-LEARNING ADOPTION READINESS IN SECONDARY EDUCATION OF DEVELOPED AND DEVELOPING COUNTRIES: A SYSTEMATIC LITERATURE REVIEW Ratu Syafianisa Nuzulismah; Harry Budi Santoso; Panca O. Hadi Putra
Jurnal Ilmiah Kursor Vol 11 No 4 (2022)
Publisher : Universitas Trunojoyo Madura

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21107/kursor.v11i4.301

Abstract

Situation in COVID-19 pandemic forced educational activity to shift from face-to-face to blended learning or to full online learning. This situation becomes a problem in different academic levels, especially secondary education as some teachers and students were not ready. This “E-Learning Adoption Readiness in Secondary Education: A Systematic Literature Review” article summarized the influencing factors and issues of readiness in adopting e-Learning in high school, including the technologies and communication tools, as the foundation of analysis. The research objective is to identify and compare e-Learning adoption between developed and developing countries during pandemic. This article used Kitchenham and Charter method which extract data research published in databases such as Scopus and Science Direct. This research found distinct gaps between developed and developing countries in the context of e-learning readiness adoption and factors that influenced the said adoption. We conclude that there are still a numbers of basic internal and external factors that need to be considered in e-Learning adoption, especially in developing countries. The implication and recommendation for the adoption during pandemic is hoped to be insightful for future research in the same field, as there are significant differences in developing and developed countries, especially regarding IT literacy.
DESIGN AND DEVELOPMENT OF BACKEND APPLICATION FOR THESIS MANAGEMENT SYSTEM USING MICROSERVICE ARCHITECTURE AND RESTFUL API Ach. Khozaimi; Yoga Dwitya Pramudita; Firdaus Solihin
Jurnal Ilmiah Kursor Vol 11 No 4 (2022)
Publisher : Universitas Trunojoyo Madura

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21107/kursor.v11i4.313

Abstract

A thesis is a scientific work completed by students with the aim of developing the knowledge gained during the lecture period. Students at Universitas Trunojoyo Madura (UTM), Faculty of Engineering, particularly Informatics Engineering, carry out their theses manually and on paper. Thesis Management System (TMS) is software designed to help with the thesis execution process by reducing paper usage and increasing time efficiency. Monolithic system development can disrupt the service process if improvements are being made to the system. Therefore, in this research, a Thesis Management System (TMS) will be built using a microservice approach to make it easier to maintain and develop the system, for example, system scalability. As a means of communication between services, TMS is designed and developed using the REST API. TMS has undergone system performance testing to verify that it performs well under certain conditions. The results show that the number of requests increases the performance response time, CPU usage, and memory consumption, with an average resource usage of each service based on a response time of 61.64 ms, CPU usage of 8.64%, and memory usage of 89.47 Mb. As the number of requests on the service increases, so does resource usage in each service, but this has no effect on device performance because the increase is so low.
THE PARAMETRIC AND NONPARAMETRIC ESTIMATOR IN SEMIPARAMETRIC REGRESSION FOR LONGITUDINAL DATA WITH SPLINE APPROACH Tony Yulianto; Kuzairi Kuzairi; Noer Azizah; M. Fariz Fadillah Mardianto; Ira Yuditira; Faisol Faisol; Rica Amalia
Jurnal Ilmiah Kursor Vol 11 No 4 (2022)
Publisher : Universitas Trunojoyo Madura

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21107/kursor.v11i4.316

Abstract

Regression analysis aims to determine the relationship between response variables and predictor variables. There are three approaches to estimate regression curves, there are parametric, nonparametric, and semiparametric regression. In this study, the form of spline semiparametric regression curve estimator for longitudinal data assessed. Based on the estimator that be obtained by using Weighted Least Square (WLS) optimization applied to model electricity consumption in Madura by choosing a model for longitudinal data based on linear spline estimator with two knot. The good criterion of the model is using the GCV value, the coefficient of determination and the value of MSE. The best model is a model that has a high coefficient of determination and a small MSE value. This spline model has a determination coefficient value of 99,72911% and MSE 32,50458.

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