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All Journal ELKOMIKA: Jurnal Teknik Energi Elektrik, Teknik Telekomunikasi, & Teknik Elektronika JOURNAL OF ELECTRICAL AND SYSTEM CONTROL ENGINEERING SOSIOHUMANIKA ATIKAN JMECS (Journal of Measurements, Electronics, Communications, and Systems) Jurnal Mnemonic Jurnal Ilmu Komputer dan Informatika eProceedings of Engineering
Bambang Hidayat
Program Studi Teknik Telekomunikasi Fakultas Teknik Elektro – Universitas Telkom Jln. Telekomunikasi Dayeuhkolot Bandung, Indonesia
Arts Humanities Computer Science & IT Control & Systems Engineering Education Electrical & Electronics Engineering Engineering Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Materials Science & Nanotechnology Social Sciences

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Journal : JOURNAL OF ELECTRICAL AND SYSTEM CONTROL ENGINEERING

 1 
DETEKSI AKOR DAN MELODI PADA FILE WAV GITAR FINGERSTYLE MENGGUNAKAN METODE DWPT & K-NN Fauzi, Muhammad Ilham; Magdalena, Rita; Hidayat, Bambang
JESCE (JOURNAL OF ELECTRICAL AND SYSTEM CONTROL ENGINEERING) Vol 3, No 2 (2020): Journal Of Electrical And System Control Engineering Februari
Publisher : Universitas Medan Area

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (823.993 KB) | DOI: 10.31289/jesce.v3i2.3324

Abstract

Saat ini teknik fingerstyle cukup populer di kalangan para pemain gitar akustik Indonesia. Hal tersebut dapat dibuktikan dengan terbentuknya komunitas Indonesian Fingerstyle Guitar Community (IFGC). Teknik fingerstyle mampu menghasilkan komposisi musik layaknya komposisi musik band, seperti akor, melodi, bass, maupun perkusi.  Keterbatasan kemampuan indera pendengaran yang berupa ketidakpekaan terhadap nada merupakan salah satu penyebab sulitnya pemain gitar dalam megulik komposisi akor dan melodi pada musik fingerstyle. Oleh karena itu, pada penelitian ini dibuat sistem yang mampu mendeteksi komposisi akor dan melodi pada musik fingerstyle menggunakan metode Onset Detection, Discrete Wavelet Packet Transform (DWPT), Welch?s Method, dan Pitch Class Profile (PCP). Metode K-Nearest Neighbor digunakan sebagai metode klasifikasi pada penelitian ini. Data yang digunakan sebagai data latih sebanyak 355 data rekaman akor dan 125 data rekaman nada tunggal. Data yang diujikan pada penelitian ini yaitu 195 data rekaman akor, 75 rekaman nada tunggal, dan 8 musik fingerstyle yang setiap musiknya direkam sebanyak 5 kali. Hasil terbaik yang diperoleh yaitu 99,07% pada pendeteksian akor tunggal, 100% pada pendeteksian nada tunggal, dan sebesar 83,11% akurasi rata-rata pada pendeteksian 40 musik fingerstyle.
Deteksi Nada Dasar Alat Musik Panting Menggunakan Compressive Sensing dengan MFCC dan SVM Ramadhani, Shinta; Budiman, Gelar; Hidayat, Bambang
JOURNAL OF ELECTRICAL AND SYSTEM CONTROL ENGINEERING Vol. 6 No. 2 (2023): Journal of Electrical and System Control Engineering
Publisher : Universitas Medan Area

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31289/jesce.v6i2.8338

Abstract

During this time, Panting’s calibration process has been done with mere reliance on human hearing.  Thus, this study is intended to assist the calibration process of Panting. The expirement has been done by using Compressive Sensing, MFCC and SVM. Moreover, the six scenarios are applied to the classification process are classification using five audios with different compression ratios and the original audio. This study results the best state of the Panting’s note recognition system is given by using the compressed audio with ratio of 2,5%. It is proven by its 97,96% of accuracy that is computed in 0,06274 second of duration.
Deteksi Kelas Ruangan Berdasarkan Reverberation Time dengan Metode Linear Predictive Coding (LPC) dan K-Nearest Neighbor (KNN) Imanadi, Muhammad Tsabit Imanadi; Usman, Koredianto; Hidayat, Bambang
JOURNAL OF ELECTRICAL AND SYSTEM CONTROL ENGINEERING Vol. 6 No. 2 (2023): Journal of Electrical and System Control Engineering
Publisher : Universitas Medan Area

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31289/jesce.v6i2.8360

Abstract

Identification of the size of the evidence file conversation can be one of the tools for various purposes such as in the police world. Determining the class of the room from the recording can be an additional clue in case processing.  One way for the police to identify the class of a room is by creating a room class detection system. The class of the room can be determined by measuring the reverberation time using the LPC algorithm by extracting the characteristics of the training data in the form of audio. After obtaining the characteristics, the system will store these characteristics in the form of a dataset for testing. Then, the test data for which the room class is not yet known is inputted into the test system. KNN will classify the test data based on the previously trained dataset. The last process of the system will issue the value of accuracy and computational time from system testing. This study uses MATLAB calculation software as a calculation and simulation process, using 63 training data and 18 test data.  The  accuracy of  the  system  test  for detecting room class based on reverberation time using the LPC and KNN methods has resulted in a number with the largest accuracy value of 83.33% and computation time along 4,94657 seconds with a K value of 3, LPC order of 12, number of frames 240, and the Hanning window.
Co-Authors BACHERAMSYAH, TASYA FIKRIYAH Darwindra, Darwindra Faradilla Zenda, Siti Fauzi, Muhammad Ilham Gelar Budiman Herliana, Tiara Virlianda Hermina, Nanda Putri Imanadi, Muhammad Tsabit Imanadi Koredianto Usman Nur Ibrahim Putri, Tasya Busrizal Qothrunnada, Fadia Ramadhani, Shinta Rita Magdalena Sa’idah, Sofia Sai’dah, Sofia Sangkala, Muh Aslam Mahdi Sa’idah, Sofia Setiaji, Hartoko Carolus Ferdy Sjafril Darana SOFIA SAIDAH Suhardjo Suhardjo Suhartono Tjondronegoro Tasya, Weni