Penerapan Metode Support Vector Machine pada Sistem Deteksi Intrusi secara Real-time

Intrusion detection system is a system for detecting attacks or intrusions in a network or computer system, generally intrusion detection is done with comparing network traffic pattern with known attack pattern or with finding unnormal pattern of network traffic. The raise of internet activity has increase the number of packet data that must be analyzed for build the attack or normal pattern, this situation led to the possibility that the system can not detect the intrusion with a new technique, so it needs a system that can automaticaly build a pattern or model.This research have a goal to build an intrusion detection system with ability to create a model automaticaly and can detect the intrusion in real-time environment with using support vector machine method as a one of data mining method for classifying network traffic audit data in 3 classes, namely: normal, probe, and DoS. Audit data was established from preprocessing of network packet capture files that obtained from Tshark. Based on the test result, the sistem can help system administrator to build a model or pattern automaticaly with high accuracy, high attack detection rate, and low false positive rate. The sistem also can run in real-time environment

Support Vector Machine based Image Classification for Deaf and Mute People

A hand gesture recognition system provides a natural, innovative and modern way of nonverbal communication. It has a wide area of application in human computer interaction and sign language. The whole system consists of three components: hand detection, gesture recognition and human-computer interaction (HCI) based on recognition; in the existing technique, ANFIS(adaptive neuro-fuzzy interface system) to recognize gestures and makes it attainable to identify relatively complex gestures were used. But the complexity is high and performance is low. To achieve high accuracy and high performance with less complexity, a gray illumination technique is introduced in the proposed Hand gesture recognition. Here, live video is converted into frames and resize the frame, then apply gray illumination algorithm for color balancing in order to separate the skin separately. Then morphological feature extraction operation is carried out. After that support vector machine (SVM) train and testing process are carried out for gesture recognition. Finally, the character sound is played as audio output

Support Vector Machine untuk Klasifikasi Penutup Lahan Menggunakan Citra Radarsat 2 dengan Dual Polarisasi Hh-hv

Radarsat 2 citra. satelit yang memiliki spesifikasi baru dan lebih baik dari generasi tua dengan resolusi spasial tinggi. Radarsat 2 dapat digunakan untuk klasifikasi tutupan lahan dengan mesin vektor Dukungan algoritma bahwa mesin pembelajaran algoritma yang akan menjadi alternatif kecuali kemungkinan maksimum bahkan algoritma yang tidak dapat digunakan untuk dual polarisasi HH-HV yang SVM bisa mengatasinya. Support Vector Machine algoritma dapat memberikan informasi tutupan lahan otomatis yang dapat mengembangkan metode aplikasi untuk monitoring tutupan lahan untuk negara tropis seperti Indonesia. Metode yang kami gunakan untuk mengumpulkan Region of Interest dan uji validasi oleh survei lapangan. Penelitian ini menggunakan smartphone GPS android dengan 120 tempat sampel dengan perhitungan error 6,5%. Setelah itu kita menguji akurasi dengan meja kebingungan matriks. Hasil tutupan lahan klasifikasi menunjukkan bahwa klasifikasi keseluruhan adalah 66,67% dan memiliki koefisien kappa sebagai 0,55821446 yang menunjukkan hasil mengindikasikan tidak sesuai dengan

A Support Vector Machine (SVM) and Speeded Up Robust Features (SURF) for Indonesian Car Licence Plate Identification System

Volume 7 Issue 10 (October 201

Predicting Pancreatic Cancer Using Support Vector Machine

This report presents an approach to predict pancreatic cancer using Support Vector Machine Classification algorithm. The research objective of this project it to predict pancreatic cancer on just genomic, just clinical and combination of genomic and clinical data. We have used real genomic data having 22,763 samples and 154 features per sample. We have also created Synthetic Clinical data having 400 samples and 7 features per sample in order to predict accuracy of just clinical data. To validate the hypothesis, we have combined synthetic clinical data with subset of features from real genomic data. In our results, we observed that prediction accuracy, precision, recall with just genomic data is 80.77%, 20%, 4%. Prediction accuracy, precision, recall with just synthetic clinical data is 93.33%, 95%, 30%. While prediction accuracy, precision, recall for combination of real genomic and synthetic clinical data is 90.83%, 10%, 5%. The combination of real genomic and synthetic clinical data decreased the accuracy since the genomic data is weakly correlated. Thus we conclude that the combination of genomic and clinical data does not improve pancreatic cancer prediction accuracy. A dataset with more significant genomic features might help to predict pancreatic cancer more accurately