Garuda - Garba Rujukan Digital

Reinforced Intrusion Detection Using Pursuit Reinforcement Competitive Learning Tiyas, Indah Yulia Prafitaning; Barakbah, Ali Ridho; Harsono, Tri; Sudarsono, Amang
EMITTER International Journal of Engineering Technology Vol 2, No 1 (2014)
Publisher : Politeknik Elektronika Negeri Surabaya (PENS)

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (10388.993 KB)

Today, information technology is growing rapidly,all information can be obtainedmuch easier. It raises some new problems; one of them is unauthorized access to the system. We need a reliable network security system that is resistant to a variety of attacks against the system. Therefore, Intrusion Detection System (IDS) required to overcome the problems of intrusions. Many researches have been done on intrusion detection using classification methods. Classification methodshave high precision, but it takes efforts to determine an appropriate classification model to the classification problem. In this paper, we propose a new reinforced approach to detect intrusion with On-line Clustering using Reinforcement Learning. Reinforcement Learning is a new paradigm in machine learning which involves interaction with the environment.It works with reward and punishment mechanism to achieve solution. We apply the Reinforcement Learning to the intrusion detection problem with considering competitive learning using Pursuit Reinforcement Competitive Learning (PRCL). Based on the experimental result, PRCL can detect intrusions in real time with high accuracy (99.816% for DoS, 95.015% for Probe, 94.731% for R2L and 99.373% for U2R) and high speed (44 ms).The proposed approach can help network administrators to detect intrusion, so the computer network security systembecome reliable.Keywords: Intrusion Detection System, On-Line Clustering,Â Reinforcement Learning, Unsupervised Learning.

Evacuation System in a Building Using Cellular Automata for Pedestrian Dynamics ., Muarifin; Harsono, Tri; Barakbah, Aliridho
EMITTER International Journal of Engineering Technology Vol 3, No 1 (2015)
Publisher : Politeknik Elektronika Negeri Surabaya (PENS)

Show Abstract | Download Original | Original Source | Check in Google Scholar

The sense of safety in public facilities for pedestrians can be shown by the availability of good infrastructure, particularly the building. One of the aspects that can make pedestrians feel comfortable and safe is the availability of evacuation facilities in emergency situation. When a disaster strikes, people would start to panic and this will cause problems, especially during an evacuation.During panic in an evacuation process, pedestrians tend to act blindly and walk randomly and mindlessly. They might follow one another when they get panic. This is called as herding behavior. Regarding the evacuation systems, cellular automata is the basic method used to represent human motion. The movement of pedestrian is an important aspect during an evacuation process and this can be analyzed and implemented by using Cellular Automata. It is a simple method yet it can solve complex problems.Total evacuation time becomes the indicators in measuring the efficiency of this system. The result of comparison method shows that the proposed method could work better in certain conditions. In addition, the results of the experiments during panic and normal situation show similar characteristics especially regarding density aspect, yet evacuation time during panic situation takes longer time. The experimentâs results by using the actual data also has similar tendency with the evacuation time.Keywords: evacuation time, cellular automata, panic behavior,Â pedestrian

Differential Spatio-temporal Multiband Satellite Image Clustering using K-means Optimization With Reinforcement Programming Rachmawan, Irene Erlyn Wina; Barakbah, Ali Ridho; Harsono, Tri
EMITTER International Journal of Engineering Technology Vol 3, No 1 (2015)
Publisher : Politeknik Elektronika Negeri Surabaya (PENS)

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (946.111 KB)

Deforestration is one of the crucial issues in Indonesia because now Indonesia has worlds highest deforestation rate. In other hand, multispectral image delivers a great source of data for studying spatial and temporal changeability of the environmental such as deforestration area. This research present differential image processing methods for detecting nature change of deforestration. Our differential image processing algorithms extract and indicating area automatically. The feature of our proposed idea produce extracted information from multiband satellite image and calculate the area of deforestration by years with calculating data using temporal dataset. Yet, multiband satellite image consists of big data size that were difficult to be handled for segmentation. Commonly, K- Means clustering is considered to be a powerfull clustering algorithm because of its ability to clustering big data. However K-Means has sensitivity of its first generated centroids, which could lead into a bad performance. In this paper we propose a new approach to optimize K-Means clustering using Reinforcement Programming in order to clustering multispectral image. We build a new mechanism for generating initial centroids by implementing exploration and exploitation knowledge from Reinforcement Programming. This optimization will lead a better result for K-means data cluster. We select multispectral image from Landsat 7 in past ten years in Medawai, Borneo, Indonesia, and apply two segmentation areas consist of deforestration land and forest field. We made series of experiments and compared the experimental results of K-means using Reinforcement Programming as optimizing initiate centroid and normal K-means without optimization process.Keywords: Deforestration, Multispectral images, landsat, automaticÂ clustering, K-means.

Cluster Oriented Spatio Temporal Multidimensional Data Visualization of Earthquakes in Indonesia Shodiq, Mohammad Nur; Barakbah, Ali Ridho; Harsono, Tri
EMITTER International Journal of Engineering Technology Vol 3, No 1 (2015)
Publisher : Politeknik Elektronika Negeri Surabaya (PENS)

Show Abstract | Download Original | Original Source | Check in Google Scholar

Spatio temporal data clustering is challenge task. The result of clustering data are utilized to investigate the seismic parameters. Seismic parameters are used to describe the characteristics of earthquake behavior. One of the effective technique to study multidimensional spatio temporal data is visualization. But, visualization of multidimensional data is complicated problem. Because, this analysis consists of observed data cluster and seismic parameters. In this paper, we propose a visualization system, called as IES (Indonesia Earthquake System), for cluster analysis, spatio temporal analysis, and visualize the multidimensional data of seismic parameters. We analyze the cluster analysis by using automatic clustering, that consists of get optimal number of cluster and Hierarchical K-means clustering. We explore the visual cluster and multidimensional data in low dimensional space visualization. We made experiment with observed data, that consists of seismic data around Indonesian archipelago during 2004 to 2014.Keywords: Clustering, visualization, multidimensional data, seismicÂ parameters.

Spatio-Temporal Deforestation Measurement Using Automatic Clustering Wina Rachmawan, Irene Erlyn; Barakbah, Ali Ridho; Harsono, Tri
EMITTER International Journal of Engineering Technology Vol 4, No 1 (2016)
Publisher : Politeknik Elektronika Negeri Surabaya (PENS)

Deforestation is one of the crucial issues in Indonesia. In 2012, deforestation rate in Indonesia reached 0.84 million hectares, exceeding Brazil. According to the 2009 Guinness World Records, Indonesias deforestation rate was 1.8 million hectares per year between 2000 and 2005. An interesting view is the fact that Indonesia government denied the deforestation rate in those years and said that the rate was only 1.08 million hectares per year in 2000 and 2005. The different problem is on the technique how to deal with the deforestation rate. In this paper, we proposed a new approach for automatically identifying the deforestation area and measuring the deforestation rate. This approach involves differential image processing for detecting Spatio-temporal nature changes of deforestation. It consists series of important features extracted from multiband satellite images which are considered as the dataset of the research. These data are proceeded through the following stages: (1) Automatic clustering for multiband satellite images, (2) Reinforcement Programming to optimize K-Means clustering, (3) Automatic interpretation for deforestation areas, and (4) Deforestation measurement adjusting with elevation of the satellite. For experimental study, we applied our proposed approach to analyze and measure the deforestation in Mendawai, South Borneo. We utilized Landsat 7 to obtain the multiband images for that area from the year 2001 to 2013. Our proposed approach is able to identify the deforestation area and measure the rate. The experiment with our proposed approach made a temporal measurement for the area and showed the increasing deforestation size of the area 1.80 hectares during those years.

A Prediction System of Dengue Fever Using Monte Carlo Method Roziqin, Mochammad Choirur; Basuki, Achmad; Harsono, Tri
EMITTER International Journal of Engineering Technology Vol 4, No 1 (2016)
Publisher : Politeknik Elektronika Negeri Surabaya (PENS)

Dengue fever is an acute disease that clinically can cause death because there is no prediction system to estimate dengue fever cases so it resulted in the growing of dengue fever cases every year. Original data gathering in Jember area that uses technique of partial data gathering has caused data missing. To make this secondary data can be processed in prediction stage there is need to conduct missing imputation by using Monte Carlo method with four different randomization method, followed by data normality test with chi-square, then continued to regression stage. We use MSE (Mean Square Error) to measure prediction error. The smallest MSE result of regression is the best regression model for prediction.

Secure Communication and Information Exchange using Authenticated Ciphertext Policy Attribute-Based Encryption in Mobile Ad-hoc Network Huda, Samsul; Sudarsono, Amang; Harsono, Tri
EMITTER International Journal of Engineering Technology Vol 4, No 1 (2016)
Publisher : Politeknik Elektronika Negeri Surabaya (PENS)

MANETs are considered as suitable for commercial applications such as law enforcement, conference meeting, and sharing information in a student classroom and critical services such as military operations, disaster relief, and rescue operations. Meanwhile, in military operation especially in the battlefield in freely medium which naturally needs high mobility and flexibility. Thus, applying MANETs make these networks vulnerable to various types of attacks such aspacket eavesdropping, data disseminating, message replay, message modification, and especially privacy issue. In this paper, we propose a secure communication and information exchange in MANET with considering secure adhoc routing and secure information exchange. Regarding privacy issue or anonymity, we use a reliable asymmetric encryption which protecting user privacy by utilizing insensitive user attributes as user identity, CP-ABE (Ciphertext-Policy Attribute-Based Encryption) cryptographic scheme. We also design protocols to implement the proposed scheme for various battlefied scenarios in real evironment using embedded devices. Our experimental results showed that the additional of HMAC (Keyed-Hash Message Authentication Code) and AES (Advanced Encryption standard) schemes using processor 1.2GHz only take processing time about 4.452 ms,Â we can confirm that our approach by using CP-ABE with added HMAC and AES schemes make low overhead.

Decision Support System for Indonesian Government Fast E-Tendering Based on Vendor Classification Widodo, Edi Wahyu; Harsono, Tri; Barakbah, Ali Ridho
EMITTER International Journal of Engineering Technology Vol 4, No 1 (2016)
Publisher : Politeknik Elektronika Negeri Surabaya (PENS)

In the last few years in the world of auctions, electronic auctions become a hot topic for discussion, especially in Indonesia. In Indonesia, the auction has been using online electronic system since 2007 with all its advantages and disadvantages. This system is one of a fairly successful program in a good governance. Until now, there are 620 government agencies in Indonesia have been using this electronic procurement systems[19]. The Government can perform a budget efficiency nearly 5% of the total budget by using todays electronic procurement system. The current system is good enough, but there are still some deficiencies found. Some of solutions to cover the deficiency offered in this paper. Starting from the classification of goods or services according to the UNSPSC, applying business classification with ISIC Indonesia in 2009, recording the activity of vendors for consideration decision, and implementing a decision support system using AHP to facilitate the auction committee to determine the winner. All of above matters are intended to improve the effectiveness and efficiency of the current system.

Reinforced Intrusion Detection Using Pursuit Reinforcement Competitive Learning Tiyas, Indah Yulia Prafitaning; Barakbah, Ali Ridho; Harsono, Tri; Sudarsono, Amang
EMITTER International Journal of Engineering Technology Vol 2, No 1 (2014)
Publisher : Politeknik Elektronika Negeri Surabaya (PENS)

Today, information technology is growing rapidly,all information can be obtainedmuch easier. It raises some new problems; one of them is unauthorized access to the system. We need a reliable network security system that is resistant to a variety of attacks against the system. Therefore, Intrusion Detection System (IDS) required to overcome the problems of intrusions. Many researches have been done on intrusion detection using classification methods. Classification methodshave high precision, but it takes efforts to determine an appropriate classification model to the classification problem. In this paper, we propose a new reinforced approach to detect intrusion with On-line Clustering using Reinforcement Learning. Reinforcement Learning is a new paradigm in machine learning which involves interaction with the environment.It works with reward and punishment mechanism to achieve solution. We apply the Reinforcement Learning to the intrusion detection problem with considering competitive learning using Pursuit Reinforcement Competitive Learning (PRCL). Based on the experimental result, PRCL can detect intrusions in real time with high accuracy (99.816% for DoS, 95.015% for Probe, 94.731% for R2L and 99.373% for U2R) and high speed (44 ms).The proposed approach can help network administrators to detect intrusion, so the computer network security systembecome reliable.Keywords: Intrusion Detection System, On-Line Clustering,Â Reinforcement Learning, Unsupervised Learning.

Evacuation System in a Building Using Cellular Automata for Pedestrian Dynamics ., Muarifin; Harsono, Tri; Barakbah, Aliridho
EMITTER International Journal of Engineering Technology Vol 3, No 1 (2015)
Publisher : Politeknik Elektronika Negeri Surabaya (PENS)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24003/emitter.v3i1.31

The sense of safety in public facilities for pedestrians can be shown by the availability of good infrastructure, particularly the building. One of the aspects that can make pedestrians feel comfortable and safe is the availability of evacuation facilities in emergency situation. When a disaster strikes, people would start to panic and this will cause problems, especially during an evacuation.During panic in an evacuation process, pedestrians tend to act blindly and walk randomly and mindlessly. They might follow one another when they get panic. This is called as herding behavior. Regarding the evacuation systems, cellular automata is the basic method used to represent human motion. The movement of pedestrian is an important aspect during an evacuation process and this can be analyzed and implemented by using Cellular Automata. It is a simple method yet it can solve complex problems.Total evacuation time becomes the indicators in measuring the efficiency of this system. The result of comparison method shows that the proposed method could work better in certain conditions. In addition, the results of the experiments during panic and normal situation show similar characteristics especially regarding density aspect, yet evacuation time during panic situation takes longer time. The experimentâs results by using the actual data also has similar tendency with the evacuation time.Keywords: evacuation time, cellular automata, panic behavior,Â pedestrian

Title

Found 47 Documents
Search

Abstract

Abstract

Abstract

Abstract

Abstract

Abstract

Abstract

Abstract

Abstract

Abstract

Title Search

Found 47 Documents Search

Abstract

Abstract

Abstract

Abstract

Abstract

Abstract

Abstract

Abstract

Abstract

Abstract

Title

Found 47 Documents
Search