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The Recognition Of Semaphore Letter Code Using Haar Wavelet And Euclidean Function Ade Putra, Leonardus Sandy; Sumarno, Linggo; Abdi Gunawan, Vincentius
Proceeding of the Electrical Engineering Computer Science and Informatics Vol 5: EECSI 2018
Publisher : IAES Indonesia Section

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (454.973 KB) | DOI: 10.11591/eecsi.v5.1693

Abstract

Semaphore are one way of communicating over long distances using the semaphore flags. In Indonesia semaphore is used in scout activities as a method to send information in the form of a sentence containing the message. Sending the semaphore letter code tends to be difficult. Based on the need to semaphore learning, this research proposes an algorithm with image processing as a way to correct the movement of the semaphore letter code based on the image obtained by using the webcam. Digital image processing, Wavelet feature extraction, and Euclidean distance function are applied in this study to determine the best recognition rate of variation decimation and distance variation to sending semaphore letter code using the webcam. This study resulted in the best recognition rate of 95.4% in the 1 st decimation, recognition rate reached 94.6% in decimation 2, and recognition rate reached 94.2% in decimation 3. The result of the introduction of the semaphore letter code is on the introduction of movement as far as 3 to 5 meters
Musical Instrument Tone Recognition Using DCT Based Feature Extraction And Gaussian Windowing Sumarno, Linggo
SENATIK STT Adisutjipto Vol 5 (2019): Peran Teknologi untuk Revitalisasi Bandara dan Transportasi Udara [ISBN XXX-XXX-XXXXX-
Publisher : Sekolah Tinggi Teknologi Adisutjipto

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28989/senatik.v5i0.295

Abstract

The conducted research studied a feature extraction method in a musical instrument tone recognition system. The purpose of this study was to obtain a number of feature extraction coefficients that are smaller than those obtained in previous studies. The studied feature extraction was a DCT (Discrete Cosine Transform)-based segment averaging and Gaussian windowing. The testing of the musical instrument's tone recognition system was carried out using pianica, tenor recorder, and bellyra musical instruments, each of which represented many, several, and one significant local peaks in the transform domain. The test results showed that the optimal number of feature extraction coefficient was 8 coefficients, which could give a recognition rate of up to 100%. The test results were achieved using a Gaussian window with a sigma value of 2-6, and a 128 points DCT.
PENGENALAN NADA ALAT MUSIK MENGGUNAKAN EKSTRAKSI CIRI PERATAAN SEGMEN BERBASIS DST, DAN PENGKLASIFIKASI SVM Sumarno, Linggo
Jurnal Teknologi Vol 11 No 2 (2018): Jurnal Teknologi
Publisher : Jurnal Teknologi, Fakultas Teknologi Industri, Institut Sains & Teknologi AKPRIND Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

Pengenalan nada alat musik oleh komputer merupakan suatu upaya untuk membuat komputer dapat meniru kemampuan manusia dalam mengenali nada alat musik. Makalah ini mengusulkan suatu metode ekstraksi ciri dalam suatu pengenalan nada alat musik. Secara lebih detil, metode ekstraksi ciri yang yang diusulkan adalah perataan segmen berbasis DST (Discrete Sine Transform). Alat musik yang digunakan adalah pianika dan belira. Pianika merupakan representasi dari alat musik yang mempunyai nada polifonik, sedangkan belira merupakan representasi dari alat musik yang mempunyai nada monofonik. Pengklasifikasi yang digunakan dalam pengenalan nada ini adalah SVM (Support Vector Machine). Hasil percobaan memperlihatkan bahwa metode ekstraksi ciri yang diusulkan hanya memerlukan 8 koefisien esktraksi ciri untuk merepresentasikan suatu nada, baik nada polifonik maupun monofonik. Kemudian, pengklasifikasi SVM yang digunakan hanya memerlukan fungsi kernel linear. Penggunaan 8 koefisien ekstraksi ciri dan fungsi kernel linear tersebut sudah dapat memberikan tingkat pengenalan tertinggi hingga 100%.
Feature Extraction of Musical Instrument Tones using FFT and Segment Averaging Linggo Sumarno; I. Iswanjono
TELKOMNIKA (Telecommunication Computing Electronics and Control) Vol 15, No 3: September 2017
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/telkomnika.v15i3.3381

Abstract

A feature extraction for musical instrument tones that based on a transform domain approach was proposed in this paper. The aim of the proposed feature extraction was to get the lower feature extraction coefficients. In general, the proposed feature extraction was carried out as follow. Firstly, the input signal was transformed using FFT (Fast Fourier Transform). Secondly, the left half of the transformed signal was divided into a number of segments. Finally, the averaging results of that segments, was the feature extraction of the input signal. Based on the test results, the proposed feature extraction was highly efficient for the tones, which have many significant local peaks in the Fourier transform domain, because it only required at least four feature extraction coefficients, in order to represent every tone.
Water Pump Mechanical Faults Display at Various Frequency Resolutions Linggo Sumarno; Tjendro Tjendro; Wiwien Widyastuti; R.B. Dwiseno Wihadi
TELKOMNIKA (Telecommunication Computing Electronics and Control) Vol 12, No 1: March 2014
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/telkomnika.v12i1.29

Abstract

When an electrical machine suffered a mechanical fault, it generally emits certain sounds. These sounds came from the vibration. Therefore, based on the vibration, it could be detected if there was a mechanical fault in an electrical machine. This paper discussed the graphical display of the vibration of electrical machines in the form of household water pumps which were in good condition, faulty bearing, faulty impeller, or faulty foot valve. Vibration could be displayed in the time domain, or in the frequency domain, by using the three axes, i.e. X, Y, and Z. In the frequency domain, the vibration could be displayed at various frequency resolutions. Based on the observations, the higher frequency resolution, the lower detail in the graphical display of frequency domain would be shown. Although there was lower detail in the graphical display of frequency domain, at frequency resolution of 11.7 Hz in the X axis, showed that it could be visually distinguished among water pumps which were in good condition, faulty bearing, faulty impeller, or faulty foot valve.
The influence of sampling frequency on tone recognition of musical instruments Linggo Sumarno; Kuntoro Adi
TELKOMNIKA (Telecommunication Computing Electronics and Control) Vol 17, No 1: February 2019
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/telkomnika.v17i1.11608

Abstract

Sampling frequency of musical instruments tone recognition generally follows the Shannon sampling theorem. This paper explores the influence of sampling frequency that does not follow the Shannon sampling theorem, in the tone recognition system using segment averaging for feature extraction and template matching for classification. The musical instruments we used were bellyra, flute, and pianica, where each of them represented a musical instrument that had one, a few, and many significant local peaks in the Discrete Fourier Transform (DFT) domain. Based on our experiments, until the sampling frequency is as low as 312 Hz, recognition rate performance of bellyra and flute tones were influenced a little since it reduced in the range of 5%. However, recognition rate performance of pianica tones was not influenced by that sampling frequency. Therefore, if that kind of reduced recognition rate could be accepted, the sampling frequency as low as 312 Hz could be used for tone recognition of musical instruments.
DCT based feature extraction and support vector machine classification for musical instruments tone recognition Linggo Sumarno; Rifai Chai
Bulletin of Electrical Engineering and Informatics Vol 10, No 5: October 2021
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v10i5.3158

Abstract

The conducted research proposes a feature extraction and classification combination method that is used in a tone recognition system for musical instruments. It is expected that by implementing this combination, the tone recognition system will require fewer feature extraction coefficients than those previously investigated. The proposed combination comprises of feature extraction using discrete cosine transform (DCT) and classification using support vector machine (SVM). Bellyra, clarinet, and pianica tones were used in the experiment, with each indicating a tone with one, several, or many major local peaks in the transform domain. Based on the results of the tests, the proposed combination is efficient enough to be used in a tone recognition system for musical instruments. This is indicated in recognizing a tone, it only needs at least eight feature extraction coefficients.
PENGARUH PENYEKALAAN PADA EKSTRAKSI CIRI PENGABURAN DAN PERATAAN BLOK YANG MENGGUNAKAN TAPIS GAUSSIAN 2D Linggo Sumarno
Seminar Nasional Aplikasi Teknologi Informasi (SNATI) 2012
Publisher : Jurusan Teknik Informatika, Fakultas Teknologi Industri, Universitas Islam Indonesia

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Abstract

Ekstraksi ciri mempunyai peran yang penting dalam bidang pengenalan karakter, terutama untuk mengurangi jumlah data citra yang akan diproses. Pengaburan dan perataan blok adalah satu dari beberapa metode ekstraksi ciri yang menggunakan pendekatan multiresolusi. Untuk melakukan pengaburan suatu citra, diperlukan adanya tapis pelewat rendah 2D. Tapis Gaussian 2D merupakan salah satu tapis pelewat rendah yang dapat digunakan untuk keperluan tersebut. Berdasarkan hasil percobaan, terlihat bahwa penyekalaan pada ekstraksi ciri pengaburan dan perataan blok mempunyai sedikit pengaruh dalam meningkatkan kinerja tingkat pengenalan. Penyekalaan tersebut dapat sedikit meningkatkan kinerja tingkat pengenalan hingga sekitar 0,5%, bila dibandingkan dengan tanpa penyekalaan.
Segmentasi Kata Tulisan Tangan Menggunakan Jendela Blackman Linggo Sumarno
Seminar Nasional Aplikasi Teknologi Informasi (SNATI) 2013
Publisher : Jurusan Teknik Informatika, Fakultas Teknologi Industri, Universitas Islam Indonesia

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Abstract

Dalam pengenalan kata tulisan tangan, salah satu strateginya adalah mengenali huruf demi huruf yang menyusun kata tersebut. Dengan strategi ini, bila kata yang akan dikenali ditulis dengan ragam latin, pengenalan huruf demi hurufnya menjadi persoalan yang rumit, karena tidak jelasnya segmentasi antara huruf yang satu dengan huruf yang lain. Untuk mengatasi persoalan tersebut, dapat digunakan suatu metode segmentasi yang dinamakan segmentasi lebih. Tulisan ini membahas metode segmentasi lebih menggunakan jendela Blackman. Secara ringkas, proses segmentasi dalam tulisan ini sebagai berikut: Masukan – Pengolahan awal – Segmentasi – Keluaran. Masukan berupa sebuah citra kata terisolasi dalam format biner, serta keluaran berupa sejumlah citra segmen huruf. Pengolahan awal berfungsi untuk mengkoreksi slope dan slant dari citra masukan. Koreksi-koreksi ini diperlukan karena metode segmentasi yang digunakan sensitif terhadap slope dan slant. Segmentasi berfungsi untuk memecah citra kata menjadi sejumlah citra segmen huruf. Berdasarkan hasil pengujian secara subyektif terlihat bahwa, untuk semua jendela Blackman yang lebarnya 8, 12, dan 16 titik, dengan nilai alpha masing-masing mulai dari 0 ; 0,12 ; dan 0,40 dapat digunakan secara efektif untuk keperluan segmentasi. Secara umum, jendela Blackman dengan kelebaran mulai dari 8 titik, bila kelebaran dan nilai alpha-nya makin naik, dapat digunakan secara efektif untuk keperluan segmentasi.
The Recognition Of Semaphore Letter Code Using Haar Wavelet And Euclidean Function Leonardus Sandy Ade Putra; Linggo Sumarno; Vincentius Abdi Gunawan
Proceeding of the Electrical Engineering Computer Science and Informatics Vol 5: EECSI 2018
Publisher : IAES Indonesia Section

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (454.973 KB) | DOI: 10.11591/eecsi.v5.1693

Abstract

Semaphore are one way of communicating over long distances using the semaphore flags. In Indonesia semaphore is used in scout activities as a method to send information in the form of a sentence containing the message. Sending the semaphore letter code tends to be difficult. Based on the need to semaphore learning, this research proposes an algorithm with image processing as a way to correct the movement of the semaphore letter code based on the image obtained by using the webcam. Digital image processing, Wavelet feature extraction, and Euclidean distance function are applied in this study to determine the best recognition rate of variation decimation and distance variation to sending semaphore letter code using the webcam. This study resulted in the best recognition rate of 95.4% in the 1 st decimation, recognition rate reached 94.6% in decimation 2, and recognition rate reached 94.2% in decimation 3. The result of the introduction of the semaphore letter code is on the introduction of movement as far as 3 to 5 meters