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All Journal TELKOMNIKA (Telecommunication Computing Electronics and Control) Industrial and Systems Engineering Assessment Journal (INASEA) CommIT (Communication & Information Technology) CESS (Journal of Computer Engineering, System and Science) Proceeding of the Electrical Engineering Computer Science and Informatics JURNAL PENDIDIKAN TAMBUSAI ComTech: Computer, Mathematics and Engineering Applications JARES (Journal of Academic Research and Sciences) INFOKUM Journal of Green Engineering for Sustainability
Fergyanto E. Gunawan
Bina Nusantara University
Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Energy Engineering Environmental Science Industrial & Manufacturing Engineering Mathematics Social Sciences

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Journal : TELKOMNIKA (Telecommunication Computing Electronics and Control)

 1 
Face Recognition on Linear Motion-blurred Image Fergyanto E. Gunawan; Jeklin Harefa; 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.v16i3.5480

Abstract

Most face recognition algorithms are generally capable to achieve a high level of accuracy when the image is acquired under wellcontrolled conditions. The face should be still during the acquisition process; otherwise, the resulted image would be blur and hard for recognition. Enforcing persons to stand still during the process is impractical; extremely likely that recognition should be performed on a blurred image. It is important to understand the relation between the image blur and the recognition accuracy. The ORL Database was used in the study. All images were in PGM format of 92 × 112 pixels from forty different persons, ten images per person. Those images were randomly divided into training and testing datasets with 50-50 ratio. Singular value decomposition was used to extract the features. The images in the testing datasets were artificially blurred to represent a linear motion, and recognition was performed. The blurred images were also filtered using various methods. The accuracy levels of the recognition on the basis of the blurred faces and filtered faces were compared. The performed numerical study suggests that at its best, the image improvement processes are capable to improve the recognition accuracy level by less than five percent.
Vibration-Based Damaged Road Classification Using Artificial Neural Network Yudy Purnama; Fergyanto E. Gunawan
TELKOMNIKA (Telecommunication Computing Electronics and Control) Vol 16, No 5: October 2018
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/telkomnika.v16i5.7574

Abstract

It is necessary to develop an automated method to detect damaged road because manually monitoring the road condition is not practical. Many previous studies had demonstrated that the vibration-based technique has potential to detect damages on roads. This research explores the potential use of Artificial Neural Network (ANN) for detecting road anomalies based on vehicle accelerometer data. The vehicle is equipped with a smart-phone that has a 3D accelerometer and geo-location sensors. Then, the vehicle is used to scan road network having several road anomalies, such as, potholes, speedbump, and expansion joints. An ANN model consisting of three layers is developed to classify the road anomalies. The first layer is the input layer containing six neurons. The numbers of neurons in the hidden layer is varied between one and ten neurons, and its optimal number is sought numerically. The prediction accuracy of 84.9% is obtained by using three neurons in conjunction with the maximum acceleration data in x, y, and z-axis. The accuracy increases slightly to 86.5%, 85.2%, and 85.9% when the dominant frequencies in x, y, and z-axis, respectively, are taken into account beside the previous data.
Predicting the Level of Emotion by Means of Indonesian Speech Signal Fergyanto E. Gunawan; Kanyadian Idananta
TELKOMNIKA (Telecommunication Computing Electronics and Control) Vol 15, No 2: June 2017
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/telkomnika.v15i2.3965

Abstract

Understanding human emotion is of importance for developing better and facilitating smooth interpersonal relations. It becomes much more important because human thinking process and behavior are strongly influenced by the emotion. Align with these needs, an expert system that capable of predicting the emotion state would be useful for many practical applications. Based on a speech signal, the system has been widely developed for various languages. This study intends to evaluate to which extent Mel-Frequency Cepstral Coefficients (MFCC) features, besides Teager energy feature, derived from Indonesian speech signal relates to four emotional types: happy, sad, angry, and fear. The study utilizes empirical data of nearly 300 speech signals collected from four amateur actors and actresses speaking 15 prescribed Indonesian sentences. Using support vector machine classifier, the empirical findings suggest that the Teager energy, as well as the first coefficient of MFCCs, are a crucial feature and the prediction can achieve the accuracy level of 86%. The accuracy increases quickly with a few initial MFCC features. The fourth and more features have negligible effects on the accuracy.
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.
Co-Authors Anak Agung Ngurah Perwira Redi Ardiyanto, Ardi Ariska Dithya Pratiwi Aryanti, Jenny Benfano Soewito Candra, Sevenpri Candra, Sevenpri Deasy Lastya Sari Fitra, Zhakia Irsalina Fredlina Margaret Gunawan, Jeanifer Hapsara, Manik Hartono Wijaya Helen Storyna Hidayat, Lutfi Ito Setiawan Jeanifer Gunawan Jeklin Harefa Kanyadian Idananta Kusnandar, Erwin Livia Ashianti Manik Hapsara Marina Wahyuni Paedah Molina, Prima Muhammad Asrol Nensi, Sri Wahyu Nobumasa Sekishita Novendra, Rizki Oktarina, Rienna Pangestu, Audy Raka Retno Waluyo Ribka Intan Putri Sinaga Sofwan Ali Fakhrudin Susanto Rackman Teresa, Teresa Wanandi, Krisantus Wizi Dewi Taniasi Yansyah, Sendra Pebri Yudy Purnama