Article Per Year (5 Year)
p-Index From 2020 - 2025
0.23
P-Index
This Author published in this journals
All Journal PROSIDING SEMINAR NASIONAL FISIKA (E-JOURNAL)
Yusuf Niko Fitranto
Unknown Affiliation
Electrical & Electronics Engineering Energy Physics Other

Published : 1 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 1 Documents
Search

 1 
ANALYSIS OF COCONUT MATURITY LEVEL RECOGNITION USING PRINCIPAL COMPONENT ANALYSIS (PCA) METHOD BASED ON COCONUT KNOCKING SOUND DATA IN OPEN SPACES: ANALISIS PENGENALAN TINGKAT KEMATANGAN KELAPA MENGGUNAKAN METODE PRINCIPAL COMPONENT ANALYSIS (PCA) BERDASARKAN DATA SUARA KETUKAN KELAPA DI RUANG TERBUKA Yusuf Niko Fitranto; Bambang Heru Iswanto; Haris Suhendar
PROSIDING SEMINAR NASIONAL FISIKA (E-JOURNAL) Vol. 13 (2025): PROSIDING SEMINAR NASIONAL FISIKA (E-JOURNAL) SNF2024
Publisher : Program Studi Pendidikan Fisika dan Program Studi Fisika Universitas Negeri Jakarta, LPPM Universitas Negeri Jakarta, HFI Jakarta, HFI

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21009/03.1301.FA09

Abstract

Coconuts delivered from farmers generally have different variations in maturity. This study aims to identify the maturity of coconut fruit based on acoustic feature analysis using the Principal Component Analysis (PCA) method. This method combines data acquisition of coconut tapping sounds with multivariate statistical analysis to acoustically recognise the type of coconut maturity. The research experiment involved 40 coconut samples and the recording of coconut knocking sound was done by knocking the coconut three times from each coconut sample using a coconut knocking device in an open space. Time and frequency domain acoustic features were extracted from the resulting audio signals. Subsequently, PCA analysis was used to reduce the dimensionality of the acoustic features and identify patterns that represent the ripeness level of the coconut. The results of PCA visualisation obtained differences in acoustic features from young and old coconut maturity levels can be identified. From the results of analysis using PCA, the first two principal components explained about 40.28% and the second principal component explained about 30.07% of the data variation. Visualisation of the data using a scree plot shows that the young coconut group is clearly separated from the old coconut group.
Co-Authors Bambang Heru Iswanto Haris Suhendar