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All Journal ComEngApp : Computer Engineering and Applications Journal Teknika Jurnal Teliska Jurnal Ampere PROtek : Jurnal Ilmiah Teknik Elektro Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control Indonesian Journal of Artificial Intelligence and Data Mining Jurnal RESISTOR (Rekayasa Sistem Komputer) Jurnal Teknik Informatika (JUTIF) International Journal of Advanced Health Science and Technology Jurnal Pengabdian Masyarakat Bangsa Journal of Environment and Sustainability Education
Nyayu Latifah Husni, Nyayu Latifah
State Polytechnic of Sriwijaya
Agriculture, Biological Sciences & Forestry Arts Humanities Automotive Engineering Chemical Engineering, Chemistry & Bioengineering Chemistry Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Environmental Science Health Professions Industrial & Manufacturing Engineering Law, Crime, Criminology & Criminal Justice Mathematics Mechanical Engineering Medicine & Pharmacology Nursing Physics Public Health Social Sciences Other

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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.
Performance Evaluation on Applied Low-Cost Multi-Sensor Technology in Air Pollution Monitoring Handayani, Ade Silvia; Husni, Nyayu Latifah; Permatasari, Rosmalinda
Computer Engineering and Applications Journal (ComEngApp) Vol. 11 No. 3 (2022)
Publisher : Universitas Sriwijaya

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Abstract

This research aims to discuss the application of multi-sensor network technology for the monitoring of indoor air pollution. Indoor air pollution has become a severe problem that affects public health, especially indoor parking. The indoor air pollution monitoring system will provide information about vehicle exhaust emission levels. We have improved the system to identify six parameters of the vehicles' gas emissions within a different location at once. This research aimed to measure the parameter of Carbon Monoxide (CO), Carbon Dioxide (CO2), Hydro Carbon (HC), temperature and humidity, and levels of particulates in the air (PM10). The performance of this system shows good ability to compare the results of measurements of air quality measuring professionals. In this study, we investigated the performance of a custombuilt prototype developed under the android-based application to detect air pollution levels in the parking area. Our objective was to evaluate the suitability of a low-cost multi-sensor network for monitoring air pollution in parking and the other area. The benefit of our approach is that its time and space complexity make it valuable and efficient for real-time monitoring of air pollution.
Littering Activities Monitoring using Image Processing Husni, Nyayu Latifah; Handayani, Ade Silvia; Passarella, Rossi; Abdurrahman; Rahman, A.; Felia, Okta
Computer Engineering and Applications Journal (ComEngApp) Vol. 12 No. 3 (2023)
Publisher : Universitas Sriwijaya

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Abstract

Littering is a human behavior that become a habit since childhood. Even though there are rules that prohibit this behavior, the community still continues to do so. In order to limit this bad behavior, a device that can monitor and provide notifications is needed. In this research, proposed device can identify human activities by utilizing webcam-based image processing. It is processed by machine learning using the Recurrent Neural Network (RNN). The monitoring device produced in this research works by comparing the captured image data with dataset. The captured image data are extracted into figures and form several coordinate points on the human body. Then, the system classifies the human activities into two categories, i.e., normal or littering. This device will provide an output in the form of a ewarning every time the activity of littering is detected.
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 A. Rahman Abdurahman Abdurrahman Ade Silvia Handayani Aditya, M Rizky Vira Ahmad Taqwa Al Rasyid, Johansyah Alfarizal, Niksen Amperawan Amperawan, Amperawan Andika, Destra Angguna, Welan Mauli Asriyadi Asriyadi Caesandra, Wahyu DWI RAMADHANI Evelina Evelina Faisal Damsi, Faisal Faris, Fakhri Al Felia, Okta Ganza Handayani, Ade S Silvia Hetty Meileni Irawan Hadi Irdayanti, Yeni Irsyadi Yani Kinasih, Ayu Antika Sekar Kusumanto, Kusumanto Leni Novianti Markinashella, Deva Marniati, Yessi Meranda, Arganda Moch Hikmal Abrar1 Muslimin, Selamat Niksen Alfarizal Permatasari, Rosmalinda Plowerita, Sanyyah Pola Risma Pratama, Destra Andika Prihatini, Ekawati Putra, Yogie Dwi Rahman, M. Arief Ramadhan, M. Daffa Rasyad, Sabilal Riansyah, Rici Rizkiyanti, Shally Rossi Passarella Sepulau, Ifroh Intan Siti Chodijah Siti Nurmaini Sitompul, Carlos R Sobri, M. Ulandari, Monica Vira, Rizky Wahyu Caesarendra