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Yeni Kustiyahningsih
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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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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
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Search results for , issue "Vol 9 No 4 (2018)" : 5 Documents clear
SEGMENTATION AND COUNTING THE NUMBER OF TEETH PANORAMIC DENTAL IMAGE Nur Nafi'iyah; Endang Setyati
Jurnal Ilmiah Kursor Vol 9 No 4 (2018)
Publisher : Universitas Trunojoyo Madura

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28961/kursor.v9i4.181

Abstract

There are many methods for segmentation human teeth or dental radiographs. The most frequently used segmentation method is thresholding. In developing a system of segmentation in human teeth based on panoramic tooth photographs, several very important steps are needed. Segmentation is the separation of teeth from the background and separation of each tooth. The purpose of this study, which is to separate dental image per tooth by segmentation. In addition, the other most important process is feature extraction, which is the process of knowing the most important part of the image of the tooth to be processed. Stages in this study, namely: improving the image with CLAHE, then thresholding, the results of thresholding are segmented using integral projections, the results of segmentation extracted features using tooth centroid. Thresholding or binarization is changing grayscale image into binary forms, the algorithm used is iterative adhaptive thresholding. The accuracy value of the thresholding process or separating teeth from the background is 66.67%. Segmentation is done twice, namely: separating the maxilla and mandible, and separating each tooth. Separates the maxilla and mandible using a horizontal integral projection algorithm. Whereas to separate each tooth using vertical integral projection. In order for the separation process of each tooth to produce the best, the tooth panoramic photo is divided into three parts, namely: the left, right, and center. However, from the process of separation of each tooth the accuracy is 33.33%.
MITIGATION HANDLING OF SQL INJECTION ATTACKS ON WEBSITES USING OWASP FRAMEWORK imam riadi; Rusydi Umar; wasito sukarno
Jurnal Ilmiah Kursor Vol 9 No 4 (2018)
Publisher : Universitas Trunojoyo Madura

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28961/kursor.v9i4.182

Abstract

The development of the security system on the application of a website is now more advanced. But a software that has vulnerability will threaten all fields such as information system of health, defense, finance, and education. Information technology security issues will become the threat that made managers of the website (webadmin) alerted. This paper is focused on how to handle various application web attacks, especially attacks that uses SQL Injection, using The Open Web Application Security Project (OWASP), the aim is raise awareness about application security web and how to handle an occurred attack.
GREY FORECASTING MODEL IMPLEMENTATION FOR FORECAST OF CAPTURED FISHERIES PRODUCTION muhammad shodiq; Budi Warsito; Rachmat Gernowo
Jurnal Ilmiah Kursor Vol 9 No 4 (2018)
Publisher : Universitas Trunojoyo Madura

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28961/kursor.v9i4.170

Abstract

The increasing need for fish causes problems related to production in the fisheries sector. In fisheries production all information related to (fishing ground) is well known, but on the other hand it is not easy to predict the amount of production due to unclear information. This is also related to the number of ships that make trips, the length (time) of the trip, the type of fishing gear, weather conditions, the quality of human resources, natural environmental factors, and others. The purpose of this study is to apply Grey forecasting model or GM (1,1) to predict fisheries production. Grey forecasting models are used to build forecast models with limited amounts of data with short-term forecasts that will produce accurate forecasts. This study employs the data of captured fish from 2010 to 2018 to analyze calculations using the GM model (1,1). The results showed that the Grey forecasting model or GM (1.1) produced accurate forecasts with an ARPE error value of 9.60% or the accuracy of the forecast model reached 90.39%.
A FUZZY TIME SERIES-MARKOV CHAIN MODEL TO FORECAST FISH FARMING PRODUCT Bagus Dwi Saputra; Rachmat Gernowo; Budi Warsito
Jurnal Ilmiah Kursor Vol 9 No 4 (2018)
Publisher : Universitas Trunojoyo Madura

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28961/kursor.v9i4.167

Abstract

Price is one of the important things that need to concern as defining factor of the profit or loss of product selling as the result of price fluctuations that are very difficult to control. Price fluctuations are caused by many factors including weather, stock availability, demand and others. One of the steps to solve the price fluctuations problem is by making a forecast of fish incoming prices. The purpose of this study is to apply Markov chain’s fuzzy time series to forecast farming fish prices. Markov chain fuzzy time series is one of the prediction methods to predict time series data that has advantages in the implentation of historical data, flexible, and high level of data forecasting accuracy. This study used fish prices at November 2018. The results showed that markov chain fuzzy time series showed very accurate forecasting results with a mean error percentage of absolute percentage error (MAPE) of 1.4% so the accuracy of the Markov chain fuzzy time series method is 98, 6%.
CAN K-NEAREST NEIGHBOR METHOD BE USED TO PREDICT SUCCESS IN INDONESIA STATE UNIVERSITY STUDENT SELECTION Harits Ar Roysid; Aris Maulana; Utomo Pujianto
Jurnal Ilmiah Kursor Vol 9 No 4 (2018)
Publisher : Universitas Trunojoyo Madura

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28961/kursor.v9i4.186

Abstract

Seleksi Nasional Masuk Perguruan Tinggi Negeri (SNMPTN) is one of the selection pathways for student admissions to enter state universities (PTN) in Indonesia. This study aims to predict the chance of being accepted in the desired PTN and the lack of early monitoring of students for SNMPTN. The data source from the grades reports card of SMAN 1 Pakong, SMAN 8 Kediri, and SMAN 1 Pamekasan by using the average input of compulsory subjects, majors (Science / Social Sciences) and semester 1 to semester 5 which later the output to be accepted or not accepted An imbalanced dataset potentially affect the performance of the classification method used. Hence, we need to eliminate the imbalance class using SMOTE. Using 10-fold cross validation, this study compared K-Nearest Neighbor (KNN) without SMOTE and K-NN with SMOTE. The goal is to find the best prediction model between the two methods. The prediction model is applied to software for teachers to monitor student grades and ensuring students to pass the SNMPTN. The results show that KNN without SMOTE has higher accuracy than KNN with SMOTE. However, KNN with SMOTE outperform than KNN without SMOTE in precision and recall, KNN with SMOTE with K = 3 reached 80.08% Accuracy, 74.42% Precision and 91.68% Recall.

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