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All Journal ELKOMIKA: Jurnal Teknik Energi Elektrik, Teknik Telekomunikasi, & Teknik Elektronika JMECS (Journal of Measurements, Electronics, Communications, and Systems) Makara Journal of Technology
Suhartono Tjondronegoro
Institut Teknologi Bandung
Chemical Engineering, Chemistry & Bioengineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Engineering Materials Science & Nanotechnology Mechanical Engineering

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Deteksi Automatis Skema Modulasi Sinyal OFDM menggunakan Ciri Statistik dan Klasifikasi PSO PAMBUDI, AFIEF DIAS; TJONDRONEGORO, SUHARTONO; WIJANTO, HEROE
Jurnal Elkomika Vol 3, No 2 (2015): Jurnal Elkomika
Publisher : Jurnal Elkomika

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Abstract

Abstrak Pengenalan format modulasi dari sinyal yang dideteksi merupakan salah satu bahasan penting pada sistem intelligent receiver yang digunakan untuk aplikasi di bidang militer maupun komersial. Oleh karena itu, pada penelitian ini dilakukan klasifikasi skema modulasi pada sinyal OFDM yaitu QPSK, 16-QAM dan 64-QAM. Sinyal OFDM tersebut disimulasikan melewati kanal frequency selective fading dan additive white gaussian noise. Sistem klasifikasi yang dibuat menggunakan ekstraksi ciri statistik dan pengklasifikasi berupa diagram keputusan dengan threshold yang dioptimasi menggunakan algoritma particle swarm optimization (PSO). Pada proses klasifikasi ditambahkan sistem voting dengan skenario penggunaan jumlah simbol OFDM sebanyak 1, 5, 10, 15 dan 20. Hasil akurasi klasifikasi yang optimum didapatkan pada penggunaan lima simbol OFDM yaitu 100 %, 99 %, 96 % untuk klasifikasi QPSK, 16-QAM, 64-QAM pada minimum SNR receiver standar WiMAX IEEE 802.16e. Kata kunci: klasifikasi skema modulasi OFDM, ciri statistik, PSO. Abstract Modulation recognition of the detected signal is one of important topics on intelligent receiver system used for military and commercial applications. (Therefore) This research explored the classifications of the OFDM signal modulation scheme namely QPSK, 16-QAM and 64-QAM. The OFDM signal was simulated to pass through frequency selective fading channel and additive white gaussian noise. The classification system was developed using statistical feature extraction with a decision diagrams (tree diagram) as a classifier optimized by PSO algorithm. The increasing number of OFDM symbols in the classification process that applied a voting system improved the accuracy of the classification of each modulation scheme. The optimum accuracy of the classification had been obtained when five OFDM symbols were applied in the classification scenario. The accuracy was 100% for QPSK classification, 99 % for 16-QAM classification and 96 % for 64-QAM classification on the minimum SNR accepted by the receiver of a system that applied a standard WiMax IEEE 802.16e.   Keywords: classification modulation schemes OFDM, statistical characteristics, PSO.
Deteksi Automatis Skema Modulasi Sinyal OFDM menggunakan Ciri Statistik dan Klasifikasi PSO PAMBUDI, AFIEF DIAS; TJONDRONEGORO, SUHARTONO; WIJANTO, HEROE
ELKOMIKA: Jurnal Teknik Energi Elektrik, Teknik Telekomunikasi, & Teknik Elektronika Vol 3, No 2 (2015): ELKOMIKA
Publisher : Institut Teknologi Nasional, Bandung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26760/elkomika.v3i2.133

Abstract

ABSTRAKPengenalan format modulasi dari sinyal yang dideteksi merupakan salah satu bahasan penting pada sistem intelligent receiver yang digunakan untuk aplikasi di bidang militer maupun komersial. Oleh karena itu, pada penelitian ini dilakukan klasifikasi skema modulasi pada sinyal OFDM yaitu QPSK, 16-QAM dan 64-QAM. Sinyal OFDM tersebut disimulasikan melewati kanal frequency selective fading dan additive white gaussian noise. Sistem klasifikasi yang dibuat menggunakan ekstraksi ciri statistik dan pengklasifikasi berupa diagram keputusan dengan threshold yang dioptimasi menggunakan algoritma particle swarm optimization (PSO). Pada proses klasifikasi ditambahkan sistem voting dengan skenario penggunaan jumlah simbol OFDM sebanyak 1, 5, 10, 15 dan 20. Hasil akurasi klasifikasi yang optimum didapatkan pada penggunaan lima simbol OFDM yaitu 100 %, 99 %, 96 % untuk klasifikasi QPSK, 16-QAM, 64-QAM pada minimum SNR receiver standar WiMAX IEEE 802.16e.Kata kunci: klasifikasi skema modulasi OFDM, ciri statistik, PSO.ABSTRACTModulation recognition of the detected signal is one of important topics on intelligent receiver system used for military and commercial applications. (Therefore) This research explored the classifications of the OFDM signal modulation scheme namely QPSK, 16-QAM and 64-QAM. The OFDM signal was simulated to pass through frequency selective fading channel and additive white gaussian noise. The classification system was developed using statistical feature extraction with a decision diagrams (tree diagram) as a classifier optimized by PSO algorithm. The increasing number of OFDM symbols in the classification process that applied a voting system improved the accuracy of the classification of each modulation scheme. The optimum accuracy of the classification had been obtained when five OFDM symbols were applied in the classification scenario. The accuracy was 100% for QPSK classification, 99 % for 16-QAM classification and 96 % for 64-QAM classification on the minimum SNR accepted by the receiver of a system that applied a standard WiMax IEEE 802.16e.Keywords: classification modulation schemes OFDM, statistical characteristics, PSO.
Segmental Sinusoidal Model for Speech Signal Coding Setiawan, Florentinus Budi; Soegijoko, Soegijardjo; Sugihartono, Sugihartono; Tjondronegoro, Suhartono
Makara Journal of Technology Vol. 10, No. 2
Publisher : UI Scholars Hub

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Abstract

Segmental Sinusoidal Model for Speech Signal Coding. Periodic signal can be decomposed by sinusoidal component with Fourier series. With this characteristic, it can be modeled referring by sinusoidal form. By the sinusoidal model, signal can be quantized in order to encode the speech signal at the lower rate. The recent sinusoidal method is implemented in speech coding. By using this method, a block of the speech signal with 20 ms to 30 ms width is coded based on Fourier series coefficients. The new method proposed is quantization and reconstruction of speech signal by the segmental sinusoidal model. A segment is defined as a block of the speech signal from certain peak to consecutive peak. The length of the segment is variable, instead of the fixed block like the recent sinusoidal method. Coder consists of the encoder and the decoder. Encoder works to code speech signal at variable rate. Then coded signal will be transmitted to receiver. On the receiver, coded signal will be reconstructed, so that the reconstruction signal has the near quality compared with the original signal. The experimental results show that the average of segmental SNR is more than 20 dB.
Simple ML Detector for Multiple Antennas Communication System Taqwa, Ahmad; Soegijoko, Soegijardjo; Sugihartono, Sugihartono; Tjondronegoro, Suhartono
Makara Journal of Technology Vol. 13, No. 2
Publisher : UI Scholars Hub

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Abstract

Simple ML Detector for Multiple Antennas Communication System. In order to support providing broadband wireless communication services against limited and expensive frequency bandwidth, we have to develop a bandwidth efficient system. Therefore, in this paper we propose a closed-loop MIMO (Multiple-Input-Multiple-Output) system using ML (Maximum Likelihood) detector to optimize capacity and to increase system performance. What is especially exciting about the benefits offered by MIMO is that a high capacity and performance can be attained without additional frequency-spectral resource. The grand scenario of this concept is the attained advantages of transformation matrices having capability to allocate transmitted signals power suit to the channel. Furthermore, product of these matrices forms parallel singular channels. Due to zero inter-channels correlation, thus we can design ML detector to increase the system performance. Finally, computer simulations validates that at 0 dB SNR our system can reach optimal capacity up to 1 bps/Hz and SER up to 0.2 higher than opened-loop MIMO.
Deteksi Automatis Skema Modulasi Sinyal OFDM menggunakan Ciri Statistik dan Klasifikasi PSO PAMBUDI, AFIEF DIAS; TJONDRONEGORO, SUHARTONO; WIJANTO, HEROE
ELKOMIKA: Jurnal Teknik Energi Elektrik, Teknik Telekomunikasi, & Teknik Elektronika Vol 3, No 2: Published July - December 2015
Publisher : Institut Teknologi Nasional, Bandung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26760/elkomika.v3i2.133

Abstract

ABSTRAKPengenalan format modulasi dari sinyal yang dideteksi merupakan salah satu bahasan penting pada sistem intelligent receiver yang digunakan untuk aplikasi di bidang militer maupun komersial. Oleh karena itu, pada penelitian ini dilakukan klasifikasi skema modulasi pada sinyal OFDM yaitu QPSK, 16-QAM dan 64-QAM. Sinyal OFDM tersebut disimulasikan melewati kanal frequency selective fading dan additive white gaussian noise. Sistem klasifikasi yang dibuat menggunakan ekstraksi ciri statistik dan pengklasifikasi berupa diagram keputusan dengan threshold yang dioptimasi menggunakan algoritma particle swarm optimization (PSO). Pada proses klasifikasi ditambahkan sistem voting dengan skenario penggunaan jumlah simbol OFDM sebanyak 1, 5, 10, 15 dan 20. Hasil akurasi klasifikasi yang optimum didapatkan pada penggunaan lima simbol OFDM yaitu 100 %, 99 %, 96 % untuk klasifikasi QPSK, 16-QAM, 64-QAM pada minimum SNR receiver standar WiMAX IEEE 802.16e.Kata kunci: klasifikasi skema modulasi OFDM, ciri statistik, PSO.ABSTRACTModulation recognition of the detected signal is one of important topics on intelligent receiver system used for military and commercial applications. (Therefore) This research explored the classifications of the OFDM signal modulation scheme namely QPSK, 16-QAM and 64-QAM. The OFDM signal was simulated to pass through frequency selective fading channel and additive white gaussian noise. The classification system was developed using statistical feature extraction with a decision diagrams (tree diagram) as a classifier optimized by PSO algorithm. The increasing number of OFDM symbols in the classification process that applied a voting system improved the accuracy of the classification of each modulation scheme. The optimum accuracy of the classification had been obtained when five OFDM symbols were applied in the classification scenario. The accuracy was 100% for QPSK classification, 99 % for 16-QAM classification and 96 % for 64-QAM classification on the minimum SNR accepted by the receiver of a system that applied a standard WiMax IEEE 802.16e.Keywords: classification modulation schemes OFDM, statistical characteristics, PSO.
Audio Steganography using Modified Enhanced Least Significant Bit in 802.11n Setiaji, Hartoko Carolus Ferdy; Tjondronegoro, Suhartono; Hidayat, Bambang
JMECS (Journal of Measurements, Electronics, Communications, and Systems) Vol. 1 No. 1 (2015): JMECS
Publisher : Universitas Telkom

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25124/jmecs.v1i1.1479

Abstract

Steganography is a technique to improve the security of data, which is by inserting messages or confidential information using a medium called the host or carrier or cover. A wide variety of digital media can be used as a host, among others audio, image, video, text, header, IP datagram, and so forth. For audio steganography, the embedded audio is called stego-audio. Steganography can be cracked by using steganalysis. By exploiting the weaknesses of each steganography method. Many steganography method has been developed to increase its performance. This work proposed audio steganography scheme called Modified Enhanced Least Significant Bit (MELSB) which is modified version of Enhanced Least Significant Bit (ELSB). This method using Modified Bit Selection Rule to increase SNR and robustness of stego-audio. SNR result after applying MELSB scheme is increased. MELSB scheme also increase robustness of stego-audio. MELSB still work fine until amplification level 1.07. MELSB also work fine against noise addition better than ELSB and LSB. It give BER and CER with value 0 at SNR 33 dB. MELSB work fine in real-time condition on 802.11n WLAN if there is no transcoding and noise addition between sender’s and recipient’s computer.
Co-Authors AFIEF DIAS PAMBUDI Ahmad Taqwa Bambang Hidayat Florentinus Budi Setiawan Florentinus Setiawan, Florentinus Heroe Wijanto Setiaji, Hartoko Carolus Ferdy Soegijardjo Soegijoko Sugihartono Sugihartono