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Journal of Informatics and Data Science (J-IDS)
Published by Universitas Negeri Medan
ISSN : -     EISSN : 29640415     DOI : https://doi.org/10.24114/j-ids.xxx
Core Subject : Science, Engineering,
Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management
Journal of Informatics and Data Science (J-IDS) is a scientific journal managed by the Computer Science Study Program, Faculty of Mathematics and Natural Sciences, Medan State University, Indonesia which contains scientific writings on pure research and applied research in the field of computer science and data science as well as summarizing general developments in related theories, methods and applied sciences. Focus dan Scope J-IDS covers: Artificial Intelligence Science Computation Data Mining Data Science Big Data Natural Language Processing Computer Vision Expert System Text and Web Mining Parallel Processing
Arjuna Subject : Ilmu Komputer (semua kategori) - Kecerdasan Buatan
Articles 2 Documents
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Search results for , issue "Vol. 3 No. 2 (2024): November 2024" : 2 Documents clear
Application of Random Forest for Heart Disease Classification with SMOTE Approach to Balance Data K, Fachriz
Journal of Informatics and Data Science Vol. 3 No. 2 (2024): November 2024
Publisher : Universitas Negeri Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24114/j-ids.v3i2.66481

Abstract

In order to increase the accuracy and efficiency in heart disease detection, this work intends to develop a Random Forest algorithm based on machine learning into a heart disease prediction model. There are 255 samples in the dataset including 17 independent variables covering lifestyle and health elements. This work uses the SMote (Synthetic Minority Over-sampling Technique) technique to balance the class distribution by including synthetic data to the minority class given the data imbalance between the "Yes" (heart disease) and "No" (no heart disease) classes. With an accuracy of 94.7% and an AUC of 0.983, the Random Forest model built showed quite good results indicating that this model can effectively separate persons with and without heart disease. This work shows that the application of SMOTE considerably enhances model performance in handling data imbalance issues and helps to build machine learning-based predictive systems for heart disease classification. This work is novel in the use of the SMOTE technique to overcome data imbalance in heart disease prediction, so providing an efficient solution for data-driven medical decision making.
Melody Transcription from Monophony Audio with Fast Fourier Transform Simanjorang, Rio Givent A; Kana Saputra S; Said Iskandar Al Idrus; Zulfahmi Indra
Journal of Informatics and Data Science Vol. 3 No. 2 (2024): November 2024
Publisher : Universitas Negeri Medan

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

Abstract

Music has been an inseparable part of human life since ancient times. One form of music that is often studied is monophonic music, which consists of a single note played at a time. In the digital era, melody transcription has become an important aspect of music processing, allowing sound to be converted into musical notation. This study focuses on melody transcription from monophonic sound recordings using the Fast Fourier Transform (FFT) method. The research aims to analyze the accuracy of FFT in extracting frequency components from monophonic signals and converting them into musical notation. The research methodology involves collecting monophonic sound recordings from piano and guitar, preprocessing the audio to remove noise and normalize volume, applying FFT to extract frequency features, and mapping these frequencies into musical notation. The evaluation process is conducted using Dynamic Time Warping (DTW) and a confusion matrix to measure accuracy, precision, recall, and F1-score. The results show that the FFT-based transcription system achieves an accuracy rate of 99.24% for piano and 98.86% for guitar. The study also highlights the impact of noise and audio quality on transcription accuracy, as well as the limitations of FFT in detecting closely spaced frequencies. Despite these limitations, FFT proves to be an efficient method for melody transcription in simple monophonic music. Future research could explore hybrid approaches combining FFT with other pitch detection algorithms to improve transcription accuracy.

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