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Journal : Jurnal Transformatika

GAME SCORING SUPPORTING OBJECTS MENGGUNAKAN AGEN CERDAS BERBASIS ARTIFICIAL INTELLIGENCE Astrid Novita Putri; Rastri Prathivi
Jurnal Transformatika Vol 13, No 2 (2016): January 2016
Publisher : Jurusan Teknologi Informasi Universitas Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26623/transformatika.v13i2.331

Abstract

Game are activity most structure, one that ordinary is done in fun and also education tool and help to develop practical skill, as training, education, simulation or psychological. On its developing current game have until 3D. In one game, include in First Person Shutter  necessary scoring  one that intent to motivate that player is more terpacu to solve game until all through,  on scoring  Super Mario's game Boss, Compass does count scoring haven't utilized Artifical Intelligent so so chanted, while player meet with supporting objects example ammor  ability really guns directly dead, so is so easy win. Therefore at needs a count scoring  interesting so more motivated in finishing problem Scoring accounting point for First Person Shutter's game .This modelling as interesting daring in one game, since model scoring  one that effective gets to motivate that player is more terpacu in plays and keep player for back plays. Besides model scoring  can assign value that bound up with game zoom.On Research hits scoring this game will make scoring bases some criterion which is health Point, Attack point, Defence point, And  Magic  what do at have  supporting objects ,then in this research do compare two method are methodic statistic and Fuzzy. Result of this research 83,4 % on testing's examination and on eventually gets to be concluded that fuzzy's method in trouble finish time more long time but will player more challenging to railroad.  
ANALISA PENDETEKSIAN WORM dan TROJAN PADA JARINGAN INTERNET UNIVERSITAS SEMARANG MENGGUNAKAN METODE KALSIFIKASI PADA DATA MINING C45 dan BAYESIAN NETWORK Rastri Prathivi; Vensy Vydia
Jurnal Transformatika Vol 14, No 2 (2017): January 2017
Publisher : Jurusan Teknologi Informasi Universitas Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26623/transformatika.v14i2.440

Abstract

Worm attacks become a dangerous threat and cause damage in the Internet network. If the Internet network worms and trojan attacks the very disruption of traffic data as well as create bandwidth capacity has increased and wasted making the Internet connection is slow. Detecting worms and trojan on the Internet network, especially new variants of worms and trojans and worms and trojans hidden is still a challenging problem. Worm and trojan attacks generally occur in computer networks or the Internet which has a low level of security and vulnerable to infection. The detection and analysis of the worm and trojan attacks in the Internet network can be done by looking at the anomalies in Internet traffic and internet protocol addresses are accessed.This research used experimental research applying C4.5 and Bayesian Network methods to accurately classify anomalies in network traffic internet. Analysis of classification is applied to an internet address, internet protocol and internet bandwidth that allegedly attacked and trojan worm attacks.The results of this research is a result of analysis and classification of internet addresses, internet protocol and internet bandwidth to get the attack worms and trojans.