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All Journal Jurnal Teknik Elektro Mechatronics, Electrical Power, and Vehicular Technology Techno.Com: Jurnal Teknologi Informasi ELKOMIKA: Jurnal Teknik Energi Elektrik, Teknik Telekomunikasi, & Teknik Elektronika Makara Journal of Science International Journal of Electrical, Computer, and Biomedical Engineering (IJECBE)
Benyamin Kusumoputro
Department Of Electrical Engineering, Universitas Indonesia
Computer Science & IT Electrical & Electronics Engineering Engineering Materials Science & Nanotechnology Medicine & Pharmacology

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Journal : Makara Journal of Science

 1 
PERLUASAN METODE MFCC 1D KE 2D SEBAGAI ESKTRAKSI CIRI PADA SISTEM IDENTIFIKASI PEMBICARA MENGGUNAKAN HIDDEN MARKOV MODEL (HMM) Buono, Agus; Jatmiko, Wisnu; Kusumoputro, Benyamin
Makara Journal of Science Vol. 13, No. 1
Publisher : UI Scholars Hub

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The Extention of MFCC Technique from 1D to 2D as Feature Extractor for Speaker Identification System Using HMM. In this paper, we introduce an extension of Mel-Frequency Cepstrum Coefficients (1D-MFCC) methodology to bispectrum data, referred to as 2D-MFCC, for feature extraction. 2D-MFCC is based on 2D bispectrum data rather than 1D spectrum vector yielded by Fourier transform, so the filter in 1D-MFCC must be extend to 2D filter and using 2D cosine transform to get the mel-cepstrum coefficients from the filtered bispectrum values. Based on 2D-MFCC, we develop a speaker recognition system with Hidden Markov Model (HMM) as classifier. The experimental results show that the recognition rate is around 88%, 92% and 99% for 20, 40 and 60 data training, respectively
PENDETEKSIAN JENIS DAN KELAS AROMA DENGAN MENGGUNAKAN METODE ONE-VS-ONE DAN METODE ONE-VS-REST Rustam, Zuherman; Kusumoputro, Benyamin; Widjaja, Belawati
Makara Journal of Science Vol. 7, No. 3
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Aroma classification using one-vs-one and one-vs-rest methods. Computational Intelligence used in pattern classification problem can be divided into two different parts, one based on Neural Network and the other based on Statistical Learning. The Statistical Learning discovered by Vapnik on 70-est decade. For the pattern classification, Vapnik developed hyperplane optimal separation, which is known as Support Vector Machines Method (SVM). In the beginning, SVM was designed only to solve binary classification problem, where data existing are classified into two classes. To classify data whose consist of more than two classes, the SVM method can not directly be used. There are several methods can be used to solve SVM multiclasses classification problem, they are One-vs-One Method and One-vs-Rest Method. Both of this methods are the extension of SVM binary classification, they will be discussed in this article so that we can see their performance in aroma classification process. Data of aroma used in this experiment is consisted of three classes of aroma, each of them has six classes. The division of this class is based on alcohol concentration mixed into each of those aromas. For example, for aroma A, there are six kinds of aroma A with different alcohol concentration: 0%, 15%, 25%, 30%, 45% and 75%. The performance of these methods is measured based on their ability to recognize and classify aroma, precisely and match with the right class or variety of data existed.
SISTEM PENGENALAN WAJAH 3-D MENGGUNAKAN PENAMBAHAN GARIS CIRI PADA METODE PERHITUNGAN JARAK TERPENDEK DALAM RUANG EIGEN Lina, Lina; Kusumoputro, Benyamin
Makara Journal of Science Vol. 7, No. 1
Publisher : UI Scholars Hub

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3-D Face Recognition System using Additional Feature Lines in Nearest Feature Line Method in Eigenspace Representation. In this paper, the authors propose a new method in 3-D face recognition system using additional feature lines in Nearest Feature Line method, called the Modified Nearest Feature Line method. The additional feature lines can be acquired by projecting each feature point to other feature lines in the same class without increasing the number of feature points. With these additional lines, the system will have the ability to capture more variations of face images, so it can increase the recognition rate of the system. The authors also propose KL-TSubspace1 and KL-TSubspace2 as methods in transforming the 3-D face images from its spatial domain to their eigenspace domain. The experiments use the 3-D human faces of Indonesian people in various expressions and positions. Then, the system is applied to recognize unknown face images with different viewpoints. Experimental results shown that the system using KL-TSubspace2 and Modified Nearest Feature Line method can have the highest recognition rate of 99.17%.
PENGEMBANGAN SISTEM PENCIUMAN ELEKTRONIK DENGAN 16 BUAH SENSOR KUARSA DAN ALGORITMA NEURAL PROPAGASI BALIK UNTUK PENGENALAN AROMA CAMPURAN Kusumoputro, Benyamin; Jatmiko, Wisnu
Makara Journal of Science Vol. 6, No. 3
Publisher : UI Scholars Hub

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An artificial odor recognition system is developed for discriminating odors. This artificial system consisted of 16 quartz resonator crystals as the sensor array, a frequency modulator and a frequency counter for each sensor that are connected directly to a microcomputer. We have already shown that the artificial odor recognition system with 4 sensors is high enough to discriminate simple odor correctly, however, when it was used to discriminate compound odors, the recognition capability of this system is dropped significantly to be about 40%. Results of experiments show that the developed artificial system with 16 sensors could discriminate compound aroma based on 6 gradient of alcohol concentrations with high recognition rate of 89.9% for non batch processing system, and 82.4% for batch processing of the classes of odors
PENGEMBANGAN SISTEM PENCIUMAN ELEKTRONIK DENGAN 16 BUAH SENSOR KUARSA DAN ALGORITMA NEURAL PROPAGASI BALIK UNTUK PENGENALAN AROMA CAMPURAN Kusumoputro, Benyamin; Jatmiko, Wisnu
Makara Journal of Science Vol. 6, No. 3
Publisher : UI Scholars Hub

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Abstract

An artificial odor recognition system is developed for discriminating odors. This artificial system consisted of 16 quartz resonator crystals as the sensor array, a frequency modulator and a frequency counter for each sensor that are connected directly to a microcomputer. We have already shown that the artificial odor recognition system with 4 sensors is high enough to discriminate simple odor correctly, however, when it was used to discriminate compound odors, the recognition capability of this system is dropped significantly to be about 40%. Results of experiments show that the developed artificial system with 16 sensors could discriminate compound aroma based on 6 gradient of alcohol concentrations with high recognition rate of 89.9% for non batch processing system, and 82.4% for batch processing of the classes of odors
Co-Authors Agus Buono Agus Buono Akhmad Faqih Annisa, Selly Aries Subiantoro Belawati Widjaja Dede Sutarya, Dede Feri Yusivar Hamdi, Azhari Haris Al Hapsoro Agung Nugroho HAQQI, MUHAMMAD SABILA Hendri Murfi Heri Sutanto Lina . Lina Lina M. Ridwan Muhammad Ashari Muhammad Rif’an Ponix Irwanto Pratama, Saras Rif'an, Muhammad Rina Sripomo Saragi, Dian Putra Wahidin Wahab Widjaja, Belawati Wisnu Jatmiko Zuherman Rustam