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All Journal Perfecting a Video Game with Game Metrics ComTech: Computer, Mathematics and Engineering Applications
Livia Ashianti
Bina Nusantara University
Computer Science & IT Engineering Mathematics

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A Simple Classifier for Detecting Online Child Grooming Conversation Fergyanto E. Gunawan; Livia Ashianti; Nobumasa Sekishita
TELKOMNIKA (Telecommunication Computing Electronics and Control) Vol 16, No 3: June 2018
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/telkomnika.v16i3.6745

Abstract

The massive proliferation of social media has opened possibilities for the perpetrator conducting the crime of online child grooming. Because the pervasiveness of the problem scale, it may only be tamed effectively and efficiently by using an automatic grooming conversation detection system. The current study intends to address the issue by using Support Vector Machine and k-nearest neighbors’ classifiers. Besides, the study also proposes a low-computational cost classification method, which classifies a conversation using the number of the existing grooming conversation characteristics. All proposed methods are evaluated using 150 textual conversations of which 105 are grooming, and 45 are non-grooming. We identify that grooming conversations possess 17 features of grooming characteristics. The results suggest that the SVM and k-NN can identify grooming conversations at 98.6% and 97.8% of the level of accuracy. Meanwhile, the proposed simple method has 96.8% accuracy. The empirical study also suggests that two among the seventeen characteristics are insignificant for the classification.
Analysis And Voice Recognition In Indonesian Language Using MFCC And SVM Method Harvianto Harvianto; Livia Ashianti; Jupiter Jupiter; Suhandi Junaedi
ComTech: Computer, Mathematics and Engineering Applications Vol. 7 No. 2 (2016): ComTech
Publisher : Bina Nusantara University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21512/comtech.v7i2.2252

Abstract

Voice recognition technology is one of biometric technology. Sound is a unique part of the human being which made an individual can be easily distinguished one from another. Voice can also provide information such as gender, emotion, and identity of the speaker. This research will record human voices that pronounce digits between 0 and 9 with and without noise. Features of this sound recording will be extracted using Mel Frequency Cepstral Coefficient (MFCC). Mean, standard deviation, max, min, and the combination of them will be used to construct the feature vectors. This feature vectors then will be classified using Support Vector Machine (SVM). There will be two classification models. The first one is based on the speaker and the other one based on the digits pronounced. The classification model then will be validated by performing 10-fold cross-validation.The best average accuracy from two classification model is 91.83%. This result achieved using Mean + Standard deviation + Min + Max as features.
Co-Authors Fergyanto E. Gunawan Harvianto Harvianto Jupiter Jupiter Nobumasa Sekishita Suhandi Junaedi