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All Journal Journal of Physics and Its Applications Indonesian Journal of Data and Science
Aulia, Jasmine
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Astronomy Computer Science & IT Decision Sciences, Operations Research & Management Earth & Planetary Sciences Materials Science & Nanotechnology Mathematics Physics

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Hybrid CNN-LSTM and Cox Model for Bipolar Risk Analysis Using Social Media Data Amanda, Rizki; Aulia, Jasmine
Indonesian Journal of Data and Science Vol. 6 No. 2 (2025): Indonesian Journal of Data and Science
Publisher : yocto brain

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56705/ijodas.v6i2.265

Abstract

Introduction: Mental disorders such as bipolar disorder are becoming increasingly prominent, particularly with the rise of emotional expression through social media. Early detection remains a significant challenge due to the lack of non-invasive, real-time assessment methods. Methods: This study proposes a hybrid deep learning approach combining Convolutional Neural Network–Long Short-Term Memory (CNN-LSTM) and the Cox Proportional Hazards (Cox PH) model to analyze the risk and timing of bipolar disorder onset. A dataset of 3,511 tweets from 517 Twitter users was collected. The CNN-LSTM model classified bipolar risk levels based on text data, while the Cox PH model estimated the time-to-event for high-risk conditions using behavioral features and predicted risk labels. Results: The hybrid model demonstrated strong predictive performance. The risk label significantly influenced the time to high-risk condition (hazard ratio = 5.39, p < 0.005). The model achieved a concordance index (C-index) of 0.816, indicating high reliability in survival prediction. Conclusions: This case study highlights the potential of integrating deep learning and survival analysis for early bipolar disorder detection using social media data. The proposed non-invasive method can support mental health monitoring while raising awareness of ethical and privacy considerations
Characteristics of Blue, Red, and Green Lasers for an Object Recognition System as Unique Markers Sugiarto, Iyon Titok; Aulia, Jasmine; Radila, Zahra; Azhari, Zaenal Afif; Tresna, Wildan Panji
Journal of Physics and Its Applications Vol 7, No 4 (2025): November 2025
Publisher : Diponegoro University Semarang Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.14710/jpa.v7i4.27483

Abstract

Computer Vision (CV) is an automation technology with applications in national defense, particularly for enabling automated object targeting systems. This study focuses on developing a unique marker detection system to support such targeting capabilities. The markers consist of laser beams characterized by distinct colors, shapes, sizes, and blinking patterns, designed to be identifiable only by a programmed computer system. Incorporating these laser properties as input parameters is essential for effective object recognition. Experimental results indicate that the detection threshold was calibrated to identify red, green, and blue colored objects under indoor lighting conditions of 71.3 Lux. The CV system successfully identified a circular marker positioned 680 cm away from triangular and square markers. In distance estimation tests using a Logitech C615 HD camera, the system achieved average error rates of 4% for circles, 5% for rectangles, and 6% for triangles. Overall, the system demonstrated a tracking accuracy of 95.24% for unique markers placed at distances ranging from 50 to 300 cm.
Co-Authors Amanda, Rizki Azhari, Zaenal Afif Radila, Zahra Sugiarto, Iyon Titok Wildan Panji Tresna