Scientific Journal of Informatics
Scientific Journal of Informatics published by the Department of Computer Science, Semarang State University, a scientific journal of Information Systems and Information Technology which includes scholarly writings on pure research and applied research in the field of information systems and information technology as well as a review-general review of the development of the theory, methods, and related applied sciences.
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<![CDATA[Expert System for Determination of Type Lenses Glasses Using Forward Chaining Method ]]>
<![CDATA[Pramesti, Atikah Ari]]>;
<![CDATA[Arifudin, Riza]]>;
<![CDATA[Sugiharti, Endang]]>
<![CDATA[ Scientific Journal of Informatics Vol 3, No 2 (2016): November 2016 ]]>
Publisher : <![CDATA[Universitas Negeri Semarang ]]>
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DOI: 10.15294/sji.v3i2.7914
<![CDATA[One of the branches of computer science that is widely used by humans to help her work is the establishment of an expert system. In this study we will design an expert system for determining the type of spectacle lenses using a forward chaining method. In forward chaining method, starting with the initial information (early symptoms) and moved forward to fit more information to find the information in accordance with the rules of the knowledge base and production, and will be concluded in the form of the type of disorder diagnosis of eye disorders and provide solutions in the form of lenses of eyeglasses. Result from this study is that the match calculation of algorithm of forward chaining method between system and manual calculations produce the same output.]]>
<![CDATA[Performance Test of Openflow Agent on Openflow Software-Based Mikrotik RB750 Switch ]]>
<![CDATA[Kartadie, Rikie]]>
<![CDATA[ Scientific Journal of Informatics Vol 3, No 2 (2016): November 2016 ]]>
Publisher : <![CDATA[Universitas Negeri Semarang ]]>
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DOI: 10.15294/sji.v3i2.7987
<![CDATA[A network is usually developed by several devices such as router, switch etc. Every device forwards data package manipulation with complicated protocol planted in its hardware. An operator is responsible for running configuration either to manage rules or application applied in the network. Human error may occur when device configuration run manually by operator. Some famous vendors, one of them is MikroTik, has also been implementing this OpenFlow on its operation. It provides the implementation of SDN/OpenFlow architecture with affordable cost. The second phase research result showed that switch OF software-based MikroTik resulted higher latency value than both mininet and switch OF software-based OpenWRT. The average gap value of switch OF software-based MikroTik is 2012 kbps lower than the value of switch OF software-based OpenWRT. The average gap value of throughput bandwidth protocol UDP switch OF software-based MikroTik is 3.6176 kBps lower than switch OF software-based OpenWRT and it is 8.68 kBps lower than mininet. The average gap throughput jitter protokol UDP of switch OF software-based MiktoTik is 0.0103ms lower than switch OF software-based OpenWRT and 0.0093ms lower than mininet. ]]>
<![CDATA[The Effect of Best First and Spreadsubsample on Selection of a Feature Wrapper With Naïve Bayes Classifier for The Classification of the Ratio of Inpatients ]]>
<![CDATA[Wijaya, M Rizky]]>;
<![CDATA[Saptono, Ristu]]>;
<![CDATA[Doewes, Afrizal]]>
<![CDATA[ Scientific Journal of Informatics Vol 3, No 2 (2016): November 2016 ]]>
Publisher : <![CDATA[Universitas Negeri Semarang ]]>
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DOI: 10.15294/sji.v3i2.7910
<![CDATA[Diabetes can lead to mortality and disability, so patients should be inpatient again to undergo treatment again to be saved. On previous research about feature selection with greedy stepwise forward fail to predict classification ratio inpatient of patient with the result of recall and precision 0 on data training 60%, 75%, 80%, and 90% and there is suggestion to handle unbalanced class data problem by comparison of data readmitted 6293 and the otherwise 64141. The research purposed to know the effect of choosing the best model using best first instead of greedy stepwise forward and data sampling with spreadsubsample to resolve unbalanced class data problem. The data used was patient data from 130 American Hospital in 1999 until 2008 with 70434 data. The method that used was best first search and spreadsubsample. The result of this research are precision found 0.4 and 0.333 on training dataset 75% and 90% with best first method, while spreadsubsample method found that value of precision and recall is more significantly increased. Spreadsubsample has more effect with the result of precision and recall rather than using best first method.]]>
<![CDATA[An Identification of Tuberculosis (Tb) Disease in Humans using Nave Bayesian Method ]]>
<![CDATA[Trihartati S., Agustin]]>;
<![CDATA[Adi, C. Kuntoro]]>
<![CDATA[ Scientific Journal of Informatics Vol 3, No 2 (2016): November 2016 ]]>
Publisher : <![CDATA[Universitas Negeri Semarang ]]>
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DOI: 10.15294/sji.v3i2.7918
<![CDATA[Tuberculosis (TB) is a disease that can cause a death if not recognized or not treated properly. To reduce the death rate of tuberculosis patients, the health experts need to diagnose that disease as early as possible. Based on the main indication data, laboratory test results and the rontgen photo, Nave Bayesian approach in data mining techniques could be optimized to diagnose tuberculosis. Nave Bayes classifiers predict class membership probabilities with a class that has the highest probability value. The output of the system is an identification Tuberculosis type of the patients. Testing of the system using 237 data sample with variation of cross-validation in 3, 5, 7 and 9-fold cross validation gives an average accuracy 85,95%.]]>
<![CDATA[Identification of Tuberculosis Patient Characteristics Using K-Means Clustering ]]>
<![CDATA[Sari, Betha Nur]]>
<![CDATA[ Scientific Journal of Informatics Vol 3, No 2 (2016): November 2016 ]]>
Publisher : <![CDATA[Universitas Negeri Semarang ]]>
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DOI: 10.15294/sji.v3i2.7909
<![CDATA[In Indonesia, tuberculosis remains one of the major health problems unresolved. Indonesia is second ranked in the world as the country with the most tuberculosis cases. The purpose of this research is to study how K-means clustering applied to the treatment of tuberculosis patients data in order to identify the characteristics of tuberculosis patients. The results of K-means clustering validated by gene shaving and silhoutte coefficient. The experiment results indicate the optimum clusters value obtained from the K-mean clustering that has been validated by gene shaving and silhouette coefficient. K-means clustering divided four groups of tuberculosis patients based on their characteristics. There were divided at a category of disease (pulmonary TB, Extra Pulmonary TB and both), the age of the patient and the results of treatment of tuberculosis.]]>
<![CDATA[Expert System for Determination of Type Lenses Glasses Using Forward Chaining Method ]]>
<![CDATA[Pramesti, Atikah Ari]]>;
<![CDATA[Arifudin, Riza]]>;
<![CDATA[Sugiharti, Endang]]>
<![CDATA[ Scientific Journal of Informatics Vol 3, No 2 (2016): November 2016 ]]>
Publisher : <![CDATA[Universitas Negeri Semarang ]]>
Show Abstract
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DOI: 10.15294/sji.v3i2.7914
<![CDATA[One of the branches of computer science that is widely used by humans to help her work is the establishment of an expert system. In this study we will design an expert system for determining the type of spectacle lenses using a forward chaining method. In forward chaining method, starting with the initial information (early symptoms) and moved forward to fit more information to find the information in accordance with the rules of the knowledge base and production, and will be concluded in the form of the type of disorder diagnosis of eye disorders and provide solutions in the form of lenses of eyeglasses. Result from this study is that the match calculation of algorithm of forward chaining method between system and manual calculations produce the same output.]]>
<![CDATA[Performance Test of Openflow Agent on Openflow Software-Based Mikrotik RB750 Switch ]]>
<![CDATA[Kartadie, Rikie]]>
<![CDATA[ Scientific Journal of Informatics Vol 3, No 2 (2016): November 2016 ]]>
Publisher : <![CDATA[Universitas Negeri Semarang ]]>
Show Abstract
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DOI: 10.15294/sji.v3i2.7987
<![CDATA[A network is usually developed by several devices such as router, switch etc. Every device forwards data package manipulation with complicated protocol planted in its hardware. An operator is responsible for running configuration either to manage rules or application applied in the network. Human error may occur when device configuration run manually by operator. Some famous vendors, one of them is MikroTik, has also been implementing this OpenFlow on its operation. It provides the implementation of SDN/OpenFlow architecture with affordable cost. The second phase research result showed that switch OF software-based MikroTik resulted higher latency value than both mininet and switch OF software-based OpenWRT. The average gap value of switch OF software-based MikroTik is 2012 kbps lower than the value of switch OF software-based OpenWRT. The average gap value of throughput bandwidth protocol UDP switch OF software-based MikroTik is 3.6176 kBps lower than switch OF software-based OpenWRT and it is 8.68 kBps lower than mininet. The average gap throughput jitter protokol UDP of switch OF software-based MiktoTik is 0.0103ms lower than switch OF software-based OpenWRT and 0.0093ms lower than mininet.]]>
<![CDATA[The Effect of Best First and Spreadsubsample on Selection of a Feature Wrapper With Nave Bayes Classifier for The Classification of the Ratio of Inpatients ]]>
<![CDATA[Wijaya, M Rizky]]>;
<![CDATA[Saptono, Ristu]]>;
<![CDATA[Doewes, Afrizal]]>
<![CDATA[ Scientific Journal of Informatics Vol 3, No 2 (2016): November 2016 ]]>
Publisher : <![CDATA[Universitas Negeri Semarang ]]>
Show Abstract
|
Download Original
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Original Source
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DOI: 10.15294/sji.v3i2.7910
<![CDATA[Diabetes can lead to mortality and disability, so patients should be inpatient again to undergo treatment again to be saved. On previous research about feature selection with greedy stepwise forward fail to predict classification ratio inpatient of patient with the result of recall and precision 0 on data training 60%, 75%, 80%, and 90% and there is suggestion to handle unbalanced class data problem by comparison of data readmitted 6293 and the otherwise 64141. The research purposed to know the effect of choosing the best model using best first instead of greedy stepwise forward and data sampling with spreadsubsample to resolve unbalanced class data problem. The data used was patient data from 130 American Hospital in 1999 until 2008 with 70434 data. The method that used was best first search and spreadsubsample. The result of this research are precision found 0.4 and 0.333 on training dataset 75% and 90% with best first method, while spreadsubsample method found that value of precision and recall is more significantly increased. Spreadsubsample has more effect with the result of precision and recall rather than using best first method.]]>
<![CDATA[Model of Multilevel Sub-Image to Find the Position of Region of Interest ]]>
<![CDATA[Hartono, Budi]]>;
<![CDATA[Lusiana, Veronica]]>
<![CDATA[ Scientific Journal of Informatics Vol 3, No 2 (2016): November 2016 ]]>
Publisher : <![CDATA[Universitas Negeri Semarang ]]>
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DOI: 10.15294/sji.v3i2.7915
<![CDATA[Searching image is based on the image content, which is often called with searching of image object. If the image data has similarity object with query image then it is expected the searching process can recognize it. The position of the image object that contains an object, which is similar to the query image, is possible can be found at any positionon image data so that will become main attention or the region of interest (ROI). This image object can has different wide image, which is wider or smaller than the object on the query image. This research uses two kinds of image data sizes that are in size of 512X512 and in size of 256X256 pixels.Through experimental result is obtained that preparing model of multilevel sub-image and resize that has same size with query image that is in size of 128X128 pixels can help to find ROI position on image data. In order to find the image data that is similar to the query image then it is done by calculating Euclidean distance between query image feature and image data feature.]]>
<![CDATA[Comparison Performance of Genetic Algorithm and Ant Colony Optimization in Course Scheduling Optimizing ]]>
<![CDATA[Ashari, Imam Ahmad]]>;
<![CDATA[Muslim, Much Aziz]]>;
<![CDATA[Alamsyah, Alamsyah]]>
<![CDATA[ Scientific Journal of Informatics Vol 3, No 2 (2016): November 2016 ]]>
Publisher : <![CDATA[Universitas Negeri Semarang ]]>
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DOI: 10.15294/sji.v3i2.7911
<![CDATA[Scheduling problems at the university is a complex type of scheduling problems. The scheduling process should be carried out at every turn of the semester's. The core of the problem of scheduling courses at the university is that the number of components that need to be considered in making the schedule, some of the components was made up of students, lecturers, time and a room with due regard to the limits and certain conditions so that no collision in the schedule such as mashed room, mashed lecturer and others. To resolve a scheduling problem most appropriate technique used is the technique of optimization. Optimization techniques can give the best results desired. Metaheuristic algorithm is an algorithm that has a lot of ways to solve the problems to the very limit the optimal solution. In this paper, we use a genetic algorithm and ant colony optimization algorithm is an algorithm metaheuristic to solve the problem of course scheduling. The two algorithm will be tested and compared to get performance is the best. The algorithm was tested using data schedule courses of the university in Semarang. From the experimental results we conclude that the genetic algorithm has better performance than the ant colony optimization algorithm in solving the case of course scheduling.]]>
