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p-Index From 2020 - 2025
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All Journal Indonesian Journal of Computing, Engineering, and Design
Lin, Ding Bing
Unknown Affiliation
Arts Chemical Engineering, Chemistry & Bioengineering Computer Science & IT Engineering Industrial & Manufacturing Engineering Materials Science & Nanotechnology Mechanical Engineering

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An FMCW Radar-Based Intelligent System for Non-Contact Detection and Monitoring of Pneumonia Symptoms Purnomo, Ariana Tulus; Frandito, Raffy; Limantoro, Edrick Hensel; Djajasoepena, Rafie; Bhakti, Muhammad Agni Catur; Lin, Ding Bing
Indonesian Journal of Computing, Engineering, and Design (IJoCED) Vol. 6 No. 1 (2024): IJoCED
Publisher : Faculty of Engineering and Technology, Sampoerna University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35806/ijoced.v6i1.395

Abstract

Pneumonia is one of the most common contagious respiratory diseases, and one of its symptoms is shortness of breath. This symptom underscores the need for non-contact monitoring methods, which our paper addresses by proposing a strategy that uses Frequency-Modulated Continuous Wave (FMCW) radar to extract breathing waveforms and then classifies them with an eXtreme Gradient Boosting (XGBoost) model. The model performs well on our dataset, using stratified k-fold cross-validation and Mel-Frequency Cepstral Coefficients (MFCC) feature extraction. This intelligent system can correctly identify deep and deep-quick breathing patterns with 98% and 87.5% recall scores, respectively. Integrating FMCW and XGBoost offers a promising solution for early detection and real-time monitoring of pneumonia
Emotion Recognition in Javanese Music: A Comparative Study of Classifier Models with a Human-Annotated Dataset Septianto, Moh Erwin; Purnomo, Ariana Tulus; Lin, Ding Bing; Kim, Chang Soo
Indonesian Journal of Computing, Engineering, and Design (IJoCED) Vol. 7 No. 2 (2025): IJoCED
Publisher : Faculty of Engineering and Technology, Sampoerna University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35806/ijoced.v7i2.544

Abstract

With advancements in machine learning and the increasing availability of music datasets, Music Emotion Recognition (MER) has gained significant attention. However, research focusing on Indonesian traditional music, particularly Javanese music, remains limited. Understanding emotions in Javanese music is crucial for preserving cultural heritage and enabling emotion-aware applications tailored to Indonesian traditional music. This study investigates the effectiveness of three well-established machine learning models, 1D Convolutional Neural Networks (1D-CNNs), support Vector Machines (SVMs), and XGBoost, in classifying emotions in Javanese music using a manually annotated dataset. The dataset consists of 100 Javanese songs from various genres, including Dangdut, Koplo, and Campur Sari, annotated based on the Thayer emotion model. The models’ performance was assessed using different data split ratios, with accuracy rates exceeding 70%. Among the tested classifiers, SVM exhibited the highest and most stable accuracy. 
Co-Authors Ariana Tulus Purnomo Frandito, Raffy Kim, Chang Soo Limantoro, Edrick Hensel Muhammad Agni Catur Bhakti Rafie Djajasoepena Septianto, Moh Erwin