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All Journal ComEngApp : Computer Engineering and Applications Journal Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control Indonesian Journal of Artificial Intelligence and Data Mining Jurnal Teknologi Informasi dan Pendidikan
Prihatini, Ekawati
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Computer Science & IT Control & Systems Engineering Education Electrical & Electronics Engineering Energy Engineering

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Journal : ComEngApp : Computer Engineering and Applications Journal

 1 
Anxiety Detection for Autism Children through Vital Signs Monitoring using a Socially Assistive Robot Prihatini, Ekawati; Damsi, Faisal; Husni, Nyayu Latifah; Muslimin, Selamat; Marniati, Yessi; Ramadhan, M. Daffa
Computer Engineering and Applications Journal Vol 14 No 1 (2025)
Publisher : Universitas Sriwijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18495/comengapp.v14i1.493

Abstract

Socially Assistive Robot (SAR) to detect anxiety levels in children with Autism Spectrum Disorder (ASD), a condition often accompanied by difficulties in recognising and expressing emotions, including anxiety. Early recognition of anxiety in children with Autism Spectrum Disorder (ASD) is crucial as it can affect their behaviour and social interactions. This SAR monitors vital signs namely blood pressure, heart rate and body temperature. This study involved children with Autism Spectrum Disorder (ASD) with two conditions, namely Asperger Syndrome and Classical Autism who interacted with a Socially Assistive Robot (SAR) equipped with a tensimeter (MPS20N0040D sensor) for blood pressure, MAX30100 sensor for heart rate, and MLX90614 sensor to measure body temperature. Results show that the Socially Assistive Robot (SAR) is able to measure vital signs with high accuracy and provide an indication of anxiety levels effectively, as vital signs correlate with anxiety levels. These findings demonstrate the potential of the Socially Assistive Robot (SAR) as a reliable tool in anxiety monitoring in children with ASD, with important implications for the development of future therapeutic interventions
Development of a Littering Behavior Detection Using 3D Convolutional Neural Networks (3D CNN) Husni, Nyayu Latifah; Prihatini, Ekawati; Ulandari, Monica; Handayani, Ade Silvia
Computer Engineering and Applications Journal Vol 14 No 1 (2025)
Publisher : Universitas Sriwijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18495/comengapp.v14i1.1246

Abstract

Littering has become a significant problem that negatively impacts public health and environmental cleanliness. This research introduces an innovative solution using 3D Convolutional Neural Networks (3D CNN) technology to automatically detect littering behavior through real-time CCTV recordings. Two models were developed and tested. Model 1, which employs Conv3D, Batch Normalization, and Dropout, showed high training accuracy but exhibited fluctuations in validation accuracy, indicating potential overfitting. In contrast, Model 2, designed with a simpler structure without Batch Normalization and Dropout, achieved higher classification accuracy and efficiency. Both models significantly contribute to addressing littering in public areas, increasing awareness, and supporting environmental law enforcement. The integration of 3D CNN technology in detecting littering behavior demonstrates its potential to reduce pollution and promote environmentally responsible behavior.
Anxiety Detection for Autism Children through Vital Signs Monitoring using a Socially Assistive Robot Prihatini, Ekawati; Damsi, Faisal; Marniati, Yessi; Muslimin, Selamat; Husni, Nyayu Latifah; Ramadhan, M. Daffa
Computer Engineering and Applications Journal (ComEngApp) Vol. 14 No. 1 (2025)
Publisher : Universitas Sriwijaya

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

Abstract

Socially Assistive Robot (SAR) to detect anxiety levels in children with Autism Spectrum Disorder (ASD), a condition often accompanied by difficulties in recognising and expressing emotions, including anxiety. Early recognition of anxiety in children with Autism Spectrum Disorder (ASD) is crucial as it can affect their behaviour and social interactions. This SAR monitors vital signs namely blood pressure, heart rate and body temperature. This study involved children with Autism Spectrum Disorder (ASD) with two conditions, namely Asperger Syndrome and Classical Autism who interacted with a Socially Assistive Robot (SAR) equipped with a tensimeter (MPS20N0040D sensor) for blood pressure, MAX30100 sensor for heart rate, and MLX90614 sensor to measure body temperature. Results show that the Socially Assistive Robot (SAR) is able to measure vital signs with high accuracy and provide an indication of anxiety levels effectively, as vital signs correlate with anxiety levels. These findings demonstrate the potential of the Socially Assistive Robot (SAR) as a reliable tool in anxiety monitoring in children with ASD, with important implications for the development of future therapeutic interventions.
Development of a Littering Behavior Detection Using 3D Convolutional Neural Networks (3D CNN) Husni, Nyayu Latifah; Prihatini, Ekawati; Ulandari, Monica; Handayani, Ade Silvia
Computer Engineering and Applications Journal (ComEngApp) Vol. 14 No. 1 (2025)
Publisher : Universitas Sriwijaya

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

Abstract

Littering has become a significant problem that negatively impacts public health and environmental cleanliness. This research introduces an innovative solution using 3D Convolutional Neural Networks (3D CNN) technology to automatically detect littering behavior through real-time CCTV recordings. Two models were developed and tested. Model 1, which employs Conv3D, Batch Normalization, and Dropout, showed high training accuracy but exhibited fluctuations in validation accuracy, indicating potential overfitting. In contrast, Model 2, designed with a simpler structure without Batch Normalization and Dropout, achieved higher classification accuracy and efficiency. Both models significantly contribute to addressing littering in public areas, increasing awareness, and supporting environmental law enforcement. The integration of 3D CNN technology in detecting littering behavior demonstrates its potential to reduce pollution and promote environmentally responsible behavior.
Co-Authors Ade Silvia Handayani Caesandra, Wahyu Darmawan, Muhammad Rizki Faisal Damsi, Faisal Hadi, Kurnia Irdayanti, Yeni Marniati, Yessi Martin, Tri Muslimin, Selamat Nyayu Latifah Husni, Nyayu Latifah Rafly, Muhammad Ramadhan, M. Daffa Susanto, Naziatul Husna Thoriq, Noval Al Ulandari, Monica