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All Journal Sinkron : Jurnal dan Penelitian Teknik Informatika Journal Information System Development
Chandra, Wenripin
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Computer Science & IT Decision Sciences, Operations Research & Management Engineering

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Prediksi Klasifikasi Perawatan pada Dataset Kanker Payudara Coimbra Memakai Metode Naive Bayes Ferawaty, Ferawaty; Chandra, Wenripin; Ivanka, Kelvin
Journal Information System Development (ISD) Vol 5, No 1 (2020): Journal Information System Development (ISD)
Publisher : UNIVERSITAS PELITA HARAPAN

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

Abstract

Breast cancer is a dreaded disease and a major cause of death. In this study, the Naïve Bayes method is used to predict the category of breast cancer treatment for the Breast Cancer Coimbra Dataset. Test results involving nine variables in the dataset resulted in 44.8% of the "Healthy Controls" category and 55.2% of the "Patient" category.Keywords : Breast Cancer, Naive Bayes, Coimbra, Classification.
Human Age Estimation Through Audio Utilising MFCC and RNN Ken Ken; Quinn, Osfredo; Pardosi, Irpan Adiputra; Chandra, Wenripin
Sinkron : jurnal dan penelitian teknik informatika Vol. 7 No. 3 (2023): Article Research Volume 7 Issue 3, July 2023
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/sinkron.v8i3.12656

Abstract

Age is one of human main attributes. Age is important factor to improve communication experience. Age estimation has been used in several applications to improve user experience. Therefore, an approach is needed to estimate the user age, one of which is through audio. In this study, Mel Frequency Cepstrum Coefficients (MFCC) and Recurrent Neural Network (RNN) will be used to estimate age through audio. MFCC is used to get features from audio data, while RNN is used to estimate age. Dataset used here was taken from corpus of user speech data on the Common Voice website. This study shows that MFCC and RNN methods are able to estimate human age through audio with highest accuracy obtained in SimpleRNN is 0.5647, and 0.7087 in LSTM.
Co-Authors Ferawaty, Ferawaty Irpan Pardosi Ivanka, Kelvin Ken Ken Quinn, Osfredo