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All Journal International Journal of Power Electronics and Drive Systems (IJPEDS) Jurnal Telematika
Sudjana, Oetomo
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Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering

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Penerapan Metode Mel Frequency Ceptral Coefficient dan Learning Vector Quantization untuk Text-Dependent Speaker Identification Widodo, Sukoreno Mukti; Siswanto, Elisafina; Sudjana, Oetomo
Jurnal Telematika Vol. 11 No. 1 (2016)
Publisher : Yayasan Petra Harapan Bangsa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61769/telematika.v11i1.147

Abstract

Layanan keamanan pada umumnya menggunakan kata sandi untuk membatasi dan mengontrol akses layanan tersebut.Kata sandi yang biasa digunakan sering kali berbentuk teks.Penggunaan kata sandi dengan bentuk teks dianggap masih kurang aman karena sering kali terjadi kebocoran. Maka dari itu dibutuhkanlah bentuk lain dari kata sandi, untuk meningkatkan keamanan dalam mengakses layanan atau data tertentu. Salah satunya adalah dalam bentuk suara. Sistem ini berbasis pada input berupa file audio dengan data ucapan yang bergantung pada teks atau text-dependent dengan output adalah identitas pembicara yang teridentifikasi. Pada penelitian ini, sistem pengenalan pembicara dibuat untuk dapat mengenali suara pembicara dengan menggunakan Mel-Frequency Cepstral Coefficients yang digunakan untuk melakukan ekstraksi fitur dari data suara sehingga dihasilkan fitur-fitur yang mewakili pembicara tersebut dan metode Learning Vector Quantization yang digunakan untuk melatih data-data hasil ekstraksi dan mencocokan data latih dengan data baru sehingga didapatkan identitas dari pembicara berdasarkan suara tersebut. Dari hasil pengujian pada sistem ini, didapatkan identification rate tertinggi adalah 88.9% dengan menggunakan data dengan durasi sekitar 8 detik. Security services generally use a password to restrict and control access to its services. Many password used is often in the text form. This type of password is considered less secure because it can be obtained by unauthorized people. Other forms of password are required to increase the security in accessing services or specific data such as voices. This system is based on the input of an audio file such as utterance that depends on text or text-dependent. In this study, the speaker recognition system is made to recognize the speaker of an audio file using Mel-Frequency Ceptral Coefficients for extracting voice data to produce features that represent the speaker and Learning Vector Quantization (LVQ) to train the data extraction and matching training data with new data to obtain the identity of the speaker based on the sound. From the experiment result, obtained the highest identification rate is 88.9% using data with a duration about 8 seconds.
Discrete-time Luenberger observer design for Lithium-ion battery state-of-charge with stability guarantee Septanto, Harry; Kurniawan, Edi; Prakosa, Jalu Ahmad; Hafiz, Samsul; Atman, Made Widhi Surya; Sudjana, Oetomo
International Journal of Power Electronics and Drive Systems (IJPEDS) Vol 15, No 4: December 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijpeds.v15.i4.pp2145-2154

Abstract

State-of-charge (SOC) estimation is particularly important as it provides information about the remaining energy capacity of the battery, allowing for better planning and utilization. Accurate SOC estimation is challenging because it cannot be directly measured from the battery. Instead, it is estimated by analyzing measurable variables such as current and voltage. To address this challenge, a discrete-time observer-based SOC estimation approach is proposed in this paper. This approach utilizes a second-order equivalent circuit model and a piecewise linear approximation to represent the relationship between SOC and open circuit voltage (OCV). The proposed observer-based approach utilizes these models to estimate the SOC with assured asymptotic stability under specific assumptions to simplify the design process. Simulations in Python are conducted to evaluate the performance of the designed observer. In the simulations, the SOC estimation under various conditions, such as model uncertainty, disturbances, and measurement noise, is also covered. In addition, three different observer gains are considered in the simulations. Lastly, simulation studies indicate that the estimated SOC values converge to the real SOC values, with some different behavior depending on the regarded situations.
Co-Authors Atman, Made Widhi Surya Edi Kurniawan Hafiz, Samsul Jalu Ahmad Prakosa Septanto, Harry Siswanto, Elisafina Widodo, Sukoreno Mukti