Article Per Year (5 Year)
p-Index From 2021 - 2026
0.408
P-Index
This Author published in this journals
All Journal Telematika
Faisal, Mohammad Reza
Lambung Mangkurat University
Education

Published : 2 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 2 Documents
Search

 1 
Classification of COVID-19 Cough Sounds using Mel Frequency Cepstral Coefficient (MFCC) Feature Extraction and Support Vector Machine Mafazy, Muhammad Meftah; Faisal, Mohammad Reza; Kartini, Dwi; Indriani, Fatma; Saragih, Triando Hamonangan
Telematika Vol 16, No 2: August (2023)
Publisher : Universitas Amikom Purwokerto

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35671/telematika.v16i2.2569

Abstract

A lot of research has been carried out to detect COVID-19, such as swabs, rapid antigens, and using x-ray images. However, this method has the disadvantage that it requires taking samples through physical contact with the patient. One way to avoid physical contact is to use audio through coughing with the aim of reducing the transmission of COVID-19. Audio feature extraction such as the Mel Frequency Cepstral Coefficient (MFCC) has often been used in audio classification research, such as the classification of musical genres and so on. This study aims to compare more or less the features of audio classification performance through coughing sounds for early detection of COVID-19 using a Support Vector Machine based on the Linear and Radial Basis Function (RBF). The dataset used is the COVID-19 Cough audio dataset, before classifying, the audio data is processed into a spectrogram and then feature extraction is carried out. Classification is divided into 2 schemes, using default parameters, then using the specified configuration parameters. From the research results, the highest AUC is 0.572266 in the linear kernel-based SVM classification. Meanwhile, when using the RBF kernel, the highest AUC is 0.560181.
Comparison of Industrial Business Grouping Using Fuzzy C-Means and Fuzzy Possibilistic C-Means Methods Lestari, Mega; Kartini, Dwi; Budiman, Irwan; Faisal, Mohammad Reza; Muliadi, Muliadi
Telematika Vol 16, No 2: August (2023)
Publisher : Universitas Amikom Purwokerto

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35671/telematika.v16i2.2548

Abstract

The industrial business sector plays a role in the development of the economic sector in developing countries such as Indonesia. In this case, many industrial businesses are growing, but the data has not been processed or analyzed to produce important information that can be processed into knowledge using data mining. One of the data mining techniques used in this research is data grouping, or clustering. This research was conducted to determine the comparison results of the Cluster Validity Index on Fuzzy C-Means and Fuzzy Possibilistic C-Means methods for clustering industrial businesses in Tanah Bumbu Regency. In each process, 5 trials were conducted with the number of clusters, namely 3, 4, 5, 6, and 7, and for the attributes used: Male Labor, Female Labor, Investment Value, Production Value, and BW/BP Value. Furthermore, this study will evaluate the Cluster Validity Index, namely the Partition Entropy Index, Partition Coefficient index, and Modified Partition Coefficient Index. This research provides the best performance results in the Fuzzy C-Means method with the results of the Cluster Validity Index on the Partition Entropy Index of 0.21566, Partition Coefficient Index of 0.88078, and Modified Partition Coefficient Index of 0.82117, and the best number of clusters is 3 with the labels of low competitive industry clusters, medium competitive industry clusters, and highly competitive industry clusters.
Co-Authors Budiman, Irwan Dwi Kartini, Dwi Indriani, Fatma Lestari, Mega Mafazy, Muhammad Meftah Muliadi, Muliadi Saragih, Triando Hamonangan