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Scientific Journal of Informatics
Published by Universitas Negeri Semarang
ISSN : 24077658     EISSN : 24600040     DOI : -
Core Subject : Science,
Computer Science & IT
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.
Arjuna Subject : -
Articles 13 Documents
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Search results for , issue " Vol 3, No 2 (2016): November 2016" : 13 Documents clear
Expert System for Determination of Type Lenses Glasses Using Forward Chaining Method Pramesti, Atikah Ari; Arifudin, Riza; Sugiharti, Endang
Scientific Journal of Informatics Vol 3, No 2 (2016): November 2016
Publisher : Universitas Negeri Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15294/sji.v3i2.7914

Abstract

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.
Performance Test of Openflow Agent on Openflow Software-Based Mikrotik RB750 Switch Kartadie, Rikie
Scientific Journal of Informatics Vol 3, No 2 (2016): November 2016
Publisher : Universitas Negeri Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15294/sji.v3i2.7987

Abstract

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. 
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 Wijaya, M Rizky; Saptono, Ristu; Doewes, Afrizal
Scientific Journal of Informatics Vol 3, No 2 (2016): November 2016
Publisher : Universitas Negeri Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15294/sji.v3i2.7910

Abstract

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.

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