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All Journal International Journal of Electrical and Computer Engineering Jurnal Pengabdian Magister Pendidikan IPA Indonesian Journal of Electrical Engineering and Computer Science Indonesian Applied Physics Letters ABSYARA: Jurnal Pengabdian Pada Masyarakat
Osmalina Nur Rahma
Proram Studi S1 Teknik Biomedis, Departemen Fisika, Fakultas Sains Dan Teknologi, Universitas Airlangga, Surabaya, Indonesia
Agriculture, Biological Sciences & Forestry Chemical Engineering, Chemistry & Bioengineering Computer Science & IT Control & Systems Engineering Earth & Planetary Sciences Education Electrical & Electronics Engineering Engineering Environmental Science Library & Information Science Materials Science & Nanotechnology Mathematics Physics Other

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Pelatihan Rancang Bangun Alat Deteksi Kelelahan Berbasis Sinyal Plethysmograph untuk Meningkatkan Kualitas Kerja Dan Kesehatan di SMK 3 Pancasila Kecamatan Ambulu Kabupaten Jember Osmalina Nur Rahma; Endah Purwanti; Khusnul Ain
Jurnal Pengabdian Magister Pendidikan IPA Vol 5 No 1 (2022): Januari - Maret
Publisher : Universitas Mataram

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (473.189 KB) | DOI: 10.29303/jpmpi.v5i1.1018

Abstract

Kualitas kerja tanpa disadari juga dipengaruhi oleh kesehatan fisik dan mental dari pekerja. Demi meningkatkan produktivitas, terkadang Kesehatan fisik dan mental pegawai tidak diperhatikan sehingga dapat menimbulkan kelelahan yang berdampak pada kecelakaan kerja. Salah satu cara untuk mencegah kelelahan adalah dengan mengukur kelelahan yang dialami pekerja, diantaranya dengan menggunakan sensor pletyhsmograph (PPG). Sensor PPG mengukur kadar oksigen dalam darah dan dapat digunakan mengukur detak jantung seseorang. Kadar oksigen dalam darah dan detak jantung memiliki korelasi dengan kondisi mengantuk. Saat kondisi mengantuk, detak jantung mulai melambat akibat tubuh mulai dalam keadaan rileks. Dengan demikian, sensor PPG dapat digunakan untuk memonitoring kondisi mengantuk akibat kelelahan. Selain itu, bentuk sensor PPG yang lebih kecil dibandingkan elektrokariogram (EKG) membuat sensor PPG dapat dimanfaatkan menjadi alat yang portable. Hal ini bermanfaat untuk para siswa SMK sehinga mereka dapat meningkatkan kemampuan di bidang teknologi baru dan tepat guna. Peserta pelatihan sangat antusias terhadap pelaksanaan kegiatan karena mendapatkan pengetahuan baru terkait mikrokontroler dan kecerdasan buatan. Selain itu, Siswa SMK dapat memiliki tambahan kemampuan dan pengetahuan yang berguna untuk bersaing di dunia kerja, khususnya pada era revolusi industri 4.0.
Speech Synthesis Based on EEG Signal for Speech Impaired Patients by Using bLSTM Recurrent Neural Network Abdufattah Yurianta; Anaqi Syaddad Ihsan; Arijal Ibnu Jati; Osmalina Nur Rahma; Aji Sapta Pramulen
Indonesian Applied Physics Letters Vol. 3 No. 1 (2022): June
Publisher : Universitas Airlangga

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20473/iapl.v3i1.40257

Abstract

The disability rate in Indonesia is still relatively high and is one of the main health problems which reaches 30.38 million people or 14.2% of the Indonesian population. One of these types of disabilities is speech impairment. There are several possible causes for speech impairment, including the focal disturbance. This situation occurs because of disturbances in the vocal cords caused by injuries due to accidents and other conditions, such as throat cancer, which of course will reduce the productivity of the sufferer. Sign language can be used to communicate, but it still has limitations for normal individuals. In addition, speech synthesis using brain computer interface (BCI) based on electrocorticography (ECoG) has been developed. However, this method still has a weakness, namely invasive and allows the emergence of large enough scar tissue, so that it can reduce the quality of brain biopotential to be recorded. Therefore, a non-invasive EEG-based speech synthesis method was initiated. This method uses bLSTM as one of the components of the RNN model, so that it can construct syllables into words. This system consists of datasets, data filter programs, data segmentation programs, feature extraction programs, ANN and RNN deep learning model training programs, and text-to-speech programs. ANN and RNN form a 2-level deep learning. The testing accuracy and accuracy of the ANN are 26.04% and 20.83%, while the accuracy of the RNN is 81.25%. To improve these results, in the future, researchers can improve the data collection process and increase the number of the data, use the correct extraction feature, and compare several machine learning architectures, to produce optimal accuracy.
Pelatihan pembuatan sensor medis berbasi IoT sebagai pengenalan smart medical devices Riries Rulaningtyas; Alfian Pramudita Putra; Osmalina Nur Rahma; Katherine Katherine; I Made Mas Dwiyana Prasetya Wibawa; Kezia Sarahsophia Immanuel Ryadi
ABSYARA: Jurnal Pengabdian Pada Masayarakat Vol 4 No 1 (2023): ABSYARA: Jurnal Pengabdian Pada Masyarakat
Publisher : Universitas Hamzanwadi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29408/ab.v4i1.6989

Abstract

Cardiovascular disease (CVD) is a leading cause of death globally, resulting in approximately 17.9 million deaths each year (WHO, 2017), with estimates projecting a rise to 23.3 million deaths by 2030 (Pusdatin Kemenkes RI, 2014). Early detection of heart disease plays a crucial role in CVD prevention, with heart rate (bpm) being a key indicator to assess heart function, ranging from 60 to 100 beats per minute. To address the need for early detection, a practical heart rate monitoring device utilizing the Internet of Things (IoT) and Smart Medical Devices (SMDs) was developed. This research aimed to provide training on IoT-based heart rate detection to high school students in Trenggalek. The training encompassed lectures and hands-on practice, successfully enhancing participants' knowledge of IoT, as demonstrated by improved test scores. Moreover, the training resulted in a prototype of an IoT-based heart rate monitoring system that utilizes Arduino and a heart rate sensor. Post-training evaluations showed the majority of participants were satisfied with the quality of materials and organization, indicating the positive impact of this engagement on the partners. The results support the potential of this IoT training to equip high school students with essential skills, fostering self-reliance in medical device production and reducing dependence on imports in the face of ASEAN Economic Community challenges. Ultimately, this initiative contributes to building a competent healthcare workforce in Indonesia.
Application of ANFIS-based Non-Linear Regression Modelling to Predict Concentration Level in Concentration Grid Test as Early Detection of ADHD in Children Sayyidul Istighfar Ittaqillah; Delfina Amarissa Sumanang; Quinolina Thifal; Akila Firdausi Harahap; Akif Rahmatillah; Alfian Pramudita Putra; Riries Rulaningtyas; Osmalina Nur Rahma, S.T., M.Si.
Indonesian Applied Physics Letters Vol. 4 No. 1 (2023): June
Publisher : Universitas Airlangga

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20473/iapl.v4i1.48153

Abstract

Concentration is the main asset for students and serves as an indicator of successful learning implementation. One of the abnormal disturbances that can occur in a child's concentration development is attention deficit hyperactivity disorder (ADHD). The prevalence of ADHD in Indonesia in 2014 reached 12.81 million people due to delayed management in addressing ADHD. Therefore, early detection of ADHD is necessary for prevention. ADHD detection can be done by testing the level of concentration using a concentration grid. However, a method is needed that can be applied to uncooperative young children who are not familiar with numbers. Therefore, research was conducted with an innovative approach using a combination of EEG-ECG to classify concentration levels. The data used in this study were primary data from 4 participants with 5 repetitions. The data were processed in the preprocessing stage, which involved noise filtering and Butterworth filtering. The features used in this study were BPM (beats per minute), alpha, theta, and beta EEG signals, which would later become inputs for the Adaptive Neuro-Fuzzy Inference System (ANFIS). The output shows that the combination of EEG-ECG has the potential to predict concentration test results. Using BPM, alpha, theta, and beta signals can serve as parameters for predicting the concentration grid test values using ANFIS effectively. In the ANFIS model with 4 features, an accuracy of 99.997% was obtained for the training data and 80.2142% for the testing data. This result could be developed for early detection of ADHD based on concentration levels so the learning implementation could be more effective.
Brain-computer interface-based hand exoskeleton with bidirectional long short-term memory methods Osmalina Nur Rahma; Khusnul Ain; Alfian Pramudita Putra; Riries Rulaningtyas; Khouliya Zalda; Nita Lutfiyah; Nafisa Rahmatul Laili Alami; Rifai Chai
Indonesian Journal of Electrical Engineering and Computer Science Vol 34, No 1: April 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v34.i1.pp173-185

Abstract

It takes at least 3 months to restore hand and arm function to 70% of its original value. This condition certainly reduces the quality of life for stroke survivors. The effectiveness in restoring the motor function of stroke survivors can be improved through rehabilitation. Currently, rehabilitation methods for post-stroke patients focus on repetitive movements of the affected hand, but it is often stalled due to the lack of professional rehabilitation personnel. This research aims to design a brain-computer interface (BCI)-based exoskeleton hand motion control for rehabilitation devices. The Bidirectional long short-term memory (Bi-LSTM) method performs motion classification for the ESP32 microcontroller to control the movement of the DC motor on the exoskeleton hand in real-time. The statistical features, such as mean and standard deviation from the sliding windows process of electroencephalograph (EEG) signals, are used as the input for Bi-LSTM. The highest accuracy at the validation stage was obtained in the combination of mean and standard deviation features, with the highest accuracy of 91% at the offline testing stage and reaching an average of 90% in real-time (80%-100%). Overall, the control system design that has been made runs well to perform movements on the hand exoskeleton based on the classification of opening and grasping movements.
Classification of endometrial adenocarcinoma using histopathology images with extreme learning machine method Rulaningtyas, Riries; Rahaju, Anny Setijo; Dewi, Rosa Amalia; Hanifah, Ummi; Purwanti, Endah; Rahma, Osmalina Nur; Katherine, Katherine
Indonesian Journal of Electrical Engineering and Computer Science Vol 36, No 2: November 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v36.i2.pp961-971

Abstract

As many as 70-80% of endometrial cancer cases are endometrial adenocarcinoma. Histopathological assessment is based on the degree of differentiation, into well-differentiated, moderate-differentiated, and poorly-differentiated. Management and prognosis differ between grades, so differential diagnosis in determining the degree of tumor differentiation is crucial for appropriate treatment decisions. Histopathological image analysis offers detailed diagnostic results, but manual analysis by a pathologist is very complicated, error-prone, quite tedious, and time-consuming. Therefore, an automatic diagnostic system is needed to assist pathologists in grading the tumor. This research aims to determine the degree of differentiation of endometrial adenocarcinoma based on histopathological images. The extreme learning machine (ELM) method performs image classification with gray level run long matrix (GLRLM) features and a combination of local binary pattern (LBP)-GLRLM features as input. Experimental results show that the ELM model can achieve satisfactory performance. Training accuracy, testing accuracy, and model precision with GLRLM features were 97.13%, 91.33%, and 80% and combined LBPGLRLM features were 91.03%, 71.33%, and 100%. Overall, the model created can determine the degree of tumor differentiation and is useful in providing a second opinion for pathologists.
Measuring anxiety level on phobia using electrodermal activity, electrocardiogram and respiratory signals Ain, Khusnul; Rahma, Osmalina Nur; Purwanti, Endah; Varyan, Richa; Ittaqilah, Sayyidul Istighfar; Arfensia, Danny Sanjaya; Sosialita, Tiara Dyah; Qulub, Fitriyatul; Chai, Rifai
International Journal of Electrical and Computer Engineering (IJECE) Vol 15, No 1: February 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v15i1.pp337-348

Abstract

People with spider phobia experience excessive anxiety reactions when exposed to spiders that will interfere with daily life. Diagnosing and measuring anxiety levels in patients with spider phobia is a complex challenge. Conventional diagnosis requires psychological evaluations and clinical interviews that take time and often result in a high degree of subjectivity. Therefore, there is a need for a more objective and efficient approach to measuring anxiety levels in patients. This study performs anxiety level classification based on electrodermal activity, electrocardiogram (ECG) and respiratory signals using the dataset of Arachnophobia subjects. Each raw data is preprocessed using 24 types of features. Feature performance is processed using the recursive feature elimination method. Data processing was performed in 3 anxiety levels (high, medium, low) and two anxiety levels (high, low) with the support vector machine method and hold-out validation method (7:3). The performance of the model is evaluated by showing the accuracy, precision, recall and F1 score values. The polynomial kernel can perform optimal classification and obtain 100% accuracy in 2 classes and three classes with 100% precision, recall, and F1 score values. This result shows excellent potential in measuring anxiety levels that correlate with mental health issues.
Co-Authors Abdufattah Yurianta Akila Firdausi Harahap Alfian Pramudita Putra Anaqi Syaddad Ihsan Anny Setijo Rahaju, Anny Setijo Arfensia, Danny Sanjaya Arijal Ibnu Jati Chai, Rifai Delfina Amarissa Sumanang Dewi, Rosa Amalia Fitriyatul Qulub I Made Mas Dwiyana Prasetya Wibawa Ittaqilah, Sayyidul Istighfar Katherine Katherine Katherine Kho Kezia Sarahsophia Immanuel Ryadi Khouliya Zalda Khusnul Ain Khusnul Ain Nafisa Rahmatul Laili Alami Nita Lutfiyah Pramulen, Aji Sapta Quinolina Thifal Rahmatillah, Akif Rifai Chai Riries Rulaningtyas Sayyidul Istighfar Ittaqillah Solly Aryza Sosialita, Tiara Dyah Ummi Hanifah Varyan, Richa