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Journal : TELKOMNIKA (Telecommunication Computing Electronics and Control)

Spectral-based Features Ranking for Gamelan Instruments Identification using Filter Techniques Aris Tjahyanto; Yoyon K Suprapto; Diah P Wulandari
TELKOMNIKA (Telecommunication Computing Electronics and Control) Vol 11, No 1: March 2013
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/telkomnika.v11i1.895

Abstract

 In this paper, we describe an approach of spectral-based features ranking for Javanese gamelan instruments identification using filter techniques. The model extracted spectral-based features set of the signal using Short Time Fourier Transform (STFT). The rank of the features was determined using the five algorithms; namely ReliefF, Chi-Squared, Information Gain, Gain Ratio, and Symmetric Uncertainty. Then, we tested the ranked features by cross validation using Support Vector Machine (SVM). The experiment showed that Gain Ratio algorithm gave the best result, it yielded accuracy of 98.93%.
Gamelan Music Onset Detection based on Spectral Features Diah P. Wulandari; Aris Tjahyanto; Yoyon K. Suprapto
TELKOMNIKA (Telecommunication Computing Electronics and Control) Vol 11, No 1: March 2013
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/telkomnika.v11i1.3162

Abstract

This research detects onsets of percussive instruments by examining the performance on the sound signals of gamelan instruments as one of  traditional music instruments in Indonesia. Onset plays important role in determining musical rythmic structure, like beat, tempo, measure, and is highly required in many applications of music information retrieval. Four onset detection methods that employ spectral features, such as magnitude, phase, and the combination of both are compared in this paper. They are phase slope (PS), weighted phase deviation (WPD), spectral flux (SF), and rectified complex domain (RCD). Features are extracted by representing the sound signals into time-frequency domain using overlapped Short-time Fourier Transform (STFT) and by varying the window length. Onset detection functions are processed through peak-picking using dynamic threshold. The results showed that by using suitable window length and parameter setting of dynamic threshold, F-measure which is greater than 0.80 can be obtained for certain methods.
Comparison of stemming algorithms on Indonesian text processing Afian Syafaadi Rizki; Aris Tjahyanto; Rahmat Trialih
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.v17i1.10183

Abstract

Stemming is one of the stages performed on the process of extracting information from the text. Stemming is a process of converting words into their roots. There is an indication that the most accurate stemmer algorithm is not the only way to achieve the best performance in information retrieval (IR). In this study, seven Indonesian stemmer algorithms and an English stemmer algorithm are compared, they are Nazief, Arifin, Fadillah, Asian, Enhanched confix stripping (ECS), Arifiyanti and Porter. The data used are 2,734 tweets collected from the official twitter account of PLN. First, the aims are to analyze the correlation between stemmer accuracy and information retrieval performance in Indonesian text language. Second, is to identify the best algorithm for Indonesian text processing purpose. This research also proposed improved algorithm for stemming Indonesian text. The result shows that correlation found in the previous research does not occur for the Indonesian language. The result also shows that the proposed algorithm was the best for Indonesian text processing purpose with weighted scoring value of 0.648.