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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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Aplikasi Fuzzy Logic Controller Untuk Pengukuran Suhu Tubuh Dan Detak Jantung Pada Anak Autism Spectrum Disorder Menggunakan Socially Assistive Robot Prihatini, Ekawati; Irdayanti, Yeni; Rafly, Muhammad
Jurnal Teknologi Informasi dan Pendidikan Vol. 17 No. 1 (2024): Jurnal Teknologi Informasi dan Pendidikan
Publisher : Universitas Negeri Padang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24036/jtip.v17i1.733

Abstract

In the development of electronic technology, especially robots, they have played an important role in the medical field, including for overcoming the anxiety of autistic children and their health. This study used a SAR (Socially Assistive Robot) robot equipped with a heart rate and body temperature sensor to help reduce and indicate the anxiety and also health of autistic children. The MAX30100 sensor was used to detect heart rate, while the GY906 sensor was used to detect body temperature. The robot's response resembled a hand movement, which was a sign that a child's body temperature and heart rate were out of the ordinary (abnormal). The aim was to provide assistance to autistic children in dealing with anxiety and their health, making it easier for teachers to supervise autistic children, which could affect their emotional and social development. By using a fuzzy logic controller to analyze the response of MAX30100 sensors and GY906 sensors working optimally or not, with servo motor output.
Multi-task Cascaded Convolutional Neural Network Face Recognition in Robot SAR (Socially Assistive Robot) Prihatini, Ekawati; Muslimin, Selamat; Thoriq, Noval Al
Jurnal Teknologi Informasi dan Pendidikan Vol. 17 No. 1 (2024): Jurnal Teknologi Informasi dan Pendidikan
Publisher : Universitas Negeri Padang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24036/jtip.v17i1.734

Abstract

This study intends to create a Face Recognition system for a Socially Assistive Robot (SAR) created especially for autistic youngsters. Autism is a developmental disease that has varied degrees of impact on social interaction, speech, and behavior. In order to address the developmental deficits in autistic children, early intervention is essential. Children with autism require the right kind of therapy to help them manage their anxiety, develop their social skills, and sharpen their concentration. In this study, Multi-task Cascaded Multi-task Cascaded Convolutional Neural Network(MTCNN) facial recognition technology is used to classify and identify the emotions of autistic children. The technology has the ability to record and recognize children's faces, gauge a child's level of autism, categorize their emotions, and offer the proper support. Previous studies have indicated that it is possible to identify children with autism through their facial expressions. It is anticipated that by using Face Recognition technology on a SAR, autistic youngsters will make progress in their treatment and will feel better emotionally and be more motivated. This research serves as a foundational step in the creation of technologies that can improve the quality of life for kids with autism.
The Speech Recognition Approaches for Emotion Regulation in Socially Assistive Robot Prihatini, Ekawati; Irdayanti, Yeni; Susanto, Naziatul Husna
Jurnal Teknologi Informasi dan Pendidikan Vol. 17 No. 1 (2024): Jurnal Teknologi Informasi dan Pendidikan
Publisher : Universitas Negeri Padang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24036/jtip.v17i1.736

Abstract

Socially Assistive Robot (SAR) can be created and used to assist children with special needs specifically those with autism, to manage their emotions through the therapy they need, which is then programmed into the robot. The therapy used on the robot is Applied Behavioral Analysis (ABA) therapy in the form of a guessing game with pictures. This therapy utilizes one of the methods in the robot's program, which is Speech Recognition, to provide feedback from the child using the robot. Speech recognition plays a role in facilitating the interaction between the child and the robot. When the picture-guessing game starts, the robot displays an image on the LCD screen and asks the child to guess the name of the object shown in the picture. At that moment, Speech Recognition works by recording the child's voice and converting it into text, then comparing the child's answer with the correct answer. When the child answers correctly, the robot provides praise, while if the child answers incorrectly, the robot encourages the child to try answering correctly again. This system allows the Socially Assistive Robot to support children with autism in managing their emotions by combining the ABA therapy method with the program's interactive feature enabled by Speech Recognition. Speech Recognition enhances communication and interaction between the child and the robot, creating a supportive and engaging therapeutic experience.
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.
The Application of the Adaptive Neuro Fuzzy Inference System (ANFIS) Method in Estimating State of Charge (SOC) and State of Health (SOH) of Lithium-Ion Batteries Muslimin, Selamat; Prihatini, Ekawati; Husni, Nyayu Latifah; Caesandra, Wahyu
Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control Vol. 10, No. 4, November 2025
Publisher : Universitas Muhammadiyah Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22219/kinetik.v10i4.2357

Abstract

The increasing reliance on lithium-ion batteries (LIBs) for electric vehicles and portable electronics demands accurate monitoring of battery performance, particularly the State of Charge (SOC) and State of Health (SOH). Conventional estimation methods—such as Coulomb counting, Kalman filtering, and equivalent circuit modeling—face challenges under dynamic conditions due to drift and limited adaptability. Recent studies have explored machine learning and neuro-fuzzy approaches to enhance prediction accuracy, yet many lack integration of real-time hybrid learning or struggle with high estimation error in noisy data environments. This research aims to apply the Adaptive Neuro-Fuzzy Inference System (ANFIS) to estimate SOC and SOH using experimental data from a 48V lithium-ion battery. The novelty lies in combining voltage, current, and capacity data within a MATLAB-based ANFIS framework that employs a hybrid learning algorithm integrating backpropagation and Recursive Least Squares Estimation (RLSE). Training data for SOC estimation used charging voltage and current, while SOH estimation incorporated discharging data and capacity. Results show that ANFIS achieved high accuracy with RMSE of 0.1466 and MAE of 0.021 for SOC, and RMSE of 0.012 and MAE of 0.0017 for SOH. The estimated SOH was 33.61%, closely aligned with actual values. These findings confirm ANFIS as a robust and adaptive method for real-time battery diagnostics. Future work will explore multi-input hybrid models, the integration of IoT-based BMS telemetry, and testing across diverse battery chemistries to generalize the model's performance and extend its application in smart energy systems.
Development of a Hand Gesture Detection-Based Robot System with MediaPipe Muslimin, Selamat; Prihatini, Ekawati; Martin, Tri
Indonesian Journal of Artificial Intelligence and Data Mining Vol 8, No 3 (2025): November 2025
Publisher : Universitas Islam Negeri Sultan Syarif Kasim Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24014/ijaidm.v8i3.37678

Abstract

This research presents the development of an intelligent robot that can be summoned simply by waving a hand, without the need for physical buttons or voice commands. The system utilizes MediaPipe technology to detect and recognize hand gestures in real time through a camera. When a user waves their hand toward the camera, the system processes the motion and identifies it as a signal to call the robot. Image processing is handled by a Raspberry Pi, while movement control is managed by an Arduino, which regulates the direction and speed of the motors. The robot automatically moves toward the user and stops at a certain point to wait for further confirmation. Test results show that the robot can accurately detect gestures under various lighting conditions and distances. This approach enables more natural and efficient human–robot interaction, making it well-suited for modern contactless service systems
Real-Time Detection of Autistic Children's Activities Using YOLOv8 on Social Monitoring Robots Prihatini, Ekawati; Muslimin, Selamat; Hadi, Kurnia
Indonesian Journal of Artificial Intelligence and Data Mining Vol 8, No 2 (2025): July 2025
Publisher : Universitas Islam Negeri Sultan Syarif Kasim Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24014/ijaidm.v8i2.37380

Abstract

Children with autism spectrum disorder require special attention in both therapy and daily activity monitoring. One approach that can assist is the utilization of a Social Monitoring Robot (SMR) with the capability of automatic activity monitoring. This study aims to develop a real-time activity detection system for children with autism using the You Only Look Once version 8 (YOLOv8) algorithm on the SAR platform. The system is designed to recognize key activities such as eating, studying, and walking, through video input from a webcam processed by a Raspberry Pi. The recognition process is carried out by detecting bounding boxes and confidence scores for the child and their activities. The detection results are then visualized through a Human Machine Interface (HMI). Based on the testing, the system is capable of detecting and classifying children's activities with a fairly high level of reliability under real-world environmental conditions. These results indicate that the implementation of YOLOv8 in an SMR-based monitoring system has the potential to enhance supervision and intervention for children with autism in a more responsive and personalized manner.
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