Multisensory Health Monitoring Device Based on Raspberry Pi 4B Putri Madona, Jepri Simatupang, Ahmad Yani H Journal of Advanced Research in Applied Sciences and Engineering Technology, 2024 The availability of health monitoring devices that can be used independently, conveniently, and portably is increasing in line with busy lifestyles and the difficulty of scheduling medical tests. Measuring vital body signals with various devices makes measurements longer, less effective, and relatively more expensive. The proposed research can monitor vital body signals, such as heart rate, body temperature, respiratory rate, oxygen saturation, GSR, blood pressure, and snoring, which are integrated into a Raspberry Pi 4B-based device, with results displayed on an LCD screen. Data acquisition results show reasonably good accuracy in almost all parameters but require improvement in respiratory rate measurements. In the subsequent work, these seven-acquisition data will be used to predict several possible diseases.
Electrocardiogram signals classification using random forest method for web-based smart healthcare Juni Nurma Sari, Putri Madona, Hari Kusryanto, Muhammad Mahrus Zain, May Valzon International Journal of Advances in Applied Sciences, 2023 <span>Coronary heart is the highest cause of death in Indonesia reaching 26%. Therefore, to prevent the high mortality rate of coronary heart disease (CHD), early detection of CHD can be carried out. One way is to examine the electrocardiogram/electrocardiograph (ECG) recording. ECG hardware has been made in previous studies to record ECG signals. ECG research is an important study because it can detect cardiovascular disease. Cardiovascular diseases can be classified as arrhythmic diseases. Arrhythmia is a disorder that occurs in the heart rhythm. The method used to recognize and classify ECG signal patterns is the R-R interval (RRI) method. In this study, the ECG signal is classified as normal and abnormal. Abnormal means that a person has a heart rhythm disorder. The classification method used is random forest. The advantage of the random forest classifier is that it can handle noise and missing values and can handle large amounts of data. The accuracy of the ECG signal classification using the Random forest method is 96%. The contribution of this research is that early detection of heart rhythm disorders using an ECG can be monitored through the smart healthcare web.</span>
PQRST wave detection on ECG signals Putri Madona, Rahmat Ilias Basti, Muhammad Mahrus Zain Gaceta Sanitaria, 2021 OBJECTIVE: One way of detecting the heart disease is to determine the presence of abnormalities in PQRST interval on ECG signals. Therefore, it is expected to be used as a preliminary diagnosis of heart health and to prevent or decrease the mortality rate due to heart attack. METHODS: This paper uses three main processes: data acquisition, signal preprocessing, and feature extraction. The experiment was done to eighteen subjects recorded for 2min in a relaxed condition to obtain P wave points, QRS complexes, and T waves. RESULT: Based on the data obtained from the 18 subjects, the average accuracy of point P detection is 98.31%, point Q=98.7%, point R=99.12%, point S=86.27%, and point T=97.99%. CONCLUSION: The extraction of used features proved capable of detecting P waves, QRS complexes, T waves, as well as the amount of heart rate on all subjects.
Classification of ECG Signals Using the Naïve Bayes Classification Method and Its Implementation in Android-Based Smart Health Care Putri Madona, Yogi Zafitrah, Juni Nurma Sari, Muhammad Mahrus Zain, May Valzon Proceedings 2nd International Conference on Computer Science and Engineering the Effects of the Digital World After Pandemic Edwap Ic2se 2021, 2021 Based on the data from Basic Health Research (Riskesdas), the incidence of heart and blood vessel disease is increasing from year to year. At least 15 out of 1000 people in Indonesia suffer from heart disease. The lack of early detection of heart disease makes sufferers of this disease increase. Also, general practitioners as the first health facility visited by patients do not have the ability like a cardiologist does in examining the heart. Therefore, an application of an android-based heart rhythm abnormality classification is made for general practitioners in an effort to overcome this problem as early detection of heart abnormalities. This application utilizes a portable ECG recording device (Electrocardiogram) to record the patient's ECG signal. The recorded ECG signal is then extracted by taking the values of PT interval, Bpm, RR interval, and local RR to be classified using machine learning with the Naïve bayes algorithm. The accuracy obtained by using naive bayes is about 75%. The results of this application can assist general practitioners in early detection of heart abnormalities and as a reference in the development of research on early detection of ECG signal abnormalities.
Effect of Methadone on the Brain Activity in Close Eyes Condition Arjon Turnip, Dwi Esti Kusumandari, Siti Aminah Sobana, Arifah Nur Istiqomah, Teddy Hidayat, Shelly Iskandar, Yumna Nabila, Ririn Amrina, Putri Madona Advances in Intelligent Systems and Computing, 2021
The Design of Wheelchair Systems with Raspberry Pi 3-Based Joystick Analog and Voice Control Putri Madona, Husna Khairun Nisa, Yusmar Palapa Wijaya, Amnur Akhyan Iop Conference Series Materials Science and Engineering, 2020 In this study, an electric wheelchair that combines two controls: joystick analog and voice control is designed. IC MCP3008 is used to navigate wheelchairs by using Josytick, where joystick analog data will be converted into digital data. The movements resulted from the joystick analog on the xAxis axis (horizontally) are the right turn and left turn, and on the yAxis axis (vertically) are forward and backward. The movements on the yAxis and xAxis axes set by the user affects the speed of the wheelchair. Meanwhile, the AMR-Voice application on Android is used to navigate wheelchairs by using sound. There are five commands in this voice control: “Forward”, “backward”, “left”, “right”, “stop”. The order will be sent to Raspberry Pi 3 via the HC-06 module to then be recognized for the command. If the voice commands are received accordingly, Raspberry Pi 3 will provide an activation signal to the motor driver to move the wheelchair in the direction corresponding to the command given by the user. Voice control testing on wheelchairs is tested in quiet rooms and noisy rooms. The results of the wheelchair control testing with sound indicate that the accuracy and speed of the wheelchair response rely heavily on Internet connection and room conditions. The average response when the condition of the room is quiet is 0.16 s and when the condition of the room is noisy is 5.18 s. Wheelchairs with joystick control and the voice made can be used for the disabled, whether for those who can move their fingers or not, at a low cost so that they can be an alternative in developing countries.
Controlling the Direction of Wheelchair Movement Using Raspberry-Pi Based Brain Signals Putri Madona, Renndy Raldy Mujiono, Yusmar Palapa Wijaya Proceedings of the 2019 2nd International Conference on Applied Engineering Icae 2019, 2019 This study discusses the processing of EEG and EOG signals for the classification of wheelchairs movement. Brain signals are obtained with NeuroSky mind wave sensor; this sensor emits attention, meditation, and RAW data values. Attention value will be used for forward movement, meditation is used for backward movement, while RAW data will be used for left, right, and stop movements. The test results of forward orders have a success rate of 92%, turn right 96%, turn left 100%, stop 96%, and backward 76%.
RECENT SCHOLAR PUBLICATIONS
RH Laksmana's Yam Flour Market Expansion with Technology and Digital Marketing: Ekspansi Pasar Tepung Ubi RH Laksmana dengan Implementasi Teknologi dan Pemasaran Digital P Madona, JY Zaira, A Wijianto, SR Henim, A Trisnadoli, I Chandra Dinamisia: Jurnal Pengabdian Kepada Masyarakat 9 (2), 428-436 , 2025 2025
Pelatihan Dasar Internet Of Things (Iot) Sebagai Usaha Peningkatan Kompetensi Teknologi Bagi Siswa Pesantren Darussalam P Madona, J Jaenudin JITER-PM (Jurnal Inovasi Terapan-Pengabdian Masyarakat) 3 (1), 9-18 , 2025 2025
Pelatihan Dan Pembimbingan Robot Line Follower Bagi Santri Pesantren Darussalam J Jaenudin, P Madona JITER-PM (Jurnal Inovasi Terapan-Pengabdian Masyarakat) 2 (4), 27-34 , 2024 2024 Citations: 1
Riau Youth Leader Club: Empowering the Youth of Riau to Implement Community Empowerment Programs: Riau Youth Leader Club: Pemberdayaan Generasi Muda Riau dalam … P Madona, MI Zul, H Azwar, M Rahmawaty, T Arfan, M Akbar, F Ali, ... Dinamisia: Jurnal Pengabdian Kepada Masyarakat 8 (3), 748-757 , 2024 2024
Mobile-based Stress Level Detection using Tree-Based Machine Learning Algorithms YY Kartina Diah Kesuma Wardani, Tony Wijaya, Putri Madona, Juni Nurma Sari Proceedings of the 11th International Applied Business and Engineering … , 2024 2024
Smart Glove Sebagai Alat Bantu Komunikasi Pasien G Wiranda, P Madona Jurnal Komputer Terapan 9 (2), 161-172 , 2023 2023
Pelatihan Internet of Things (IoT) Bagi Siswa Ponpes Imam Ibnu Katsir P Madona Jurnal Pengabdian UntukMu NegeRI 7 (2), 6133-6133 , 2023 2023 Citations: 5
Implementasi Teknologi Informasi dan Industri sebagai Upaya Peningkatan Produktivitas Usaha Donat Bakar Abdurrahman A Trisnadoli, T Tianur, P Madona, MP Zifi COMSEP: Jurnal Pengabdian Kepada Masyarakat 4 (3), 261-266 , 2023 2023
Pelatihan Internet of Things (IoT) Untuk Guru SMK Negeri 7 Pekanbaru menggunakan NodeMCU M Rahmawaty, N Khamdi, P Madona JITER-PM (Jurnal Inovasi Terapan-Pengabdian Masyarakat) 1 (2), 47-52 , 2023 2023 Citations: 4
Electrocardiogram signals classification using random forest method for web-based smart healthcare JN Sari, P Madona, H Kusryanto, MM Zain, M Valzon International Journal of Advances in Applied Sciences 12 (2), 133-43 , 2023 2023 Citations: 8
Mobile-based stress level detection using tree-based machine learning algorithms KDK Wardani, T Wijaya, P Madona, JN Sari, Y Yuliska EAI International Conference on Advanced Embedded Systems , 2023 2023
Rancang Bangun Robot Pemain Musik Bellyra 2 Oktaf E Susianti, P Madona, PS Maria Jurnal Elektro dan Mesin Terapan 8 (2), 214-224 , 2022 2022
Alat Akuisisi Data 5 Parameter Sinyal Fisiologis Sebagai Penciri Stress Pada Manusia Berbasis Arduino MEGA P Madona, S Wulandari Jurnal Elektro dan Mesin Terapan 8 (2), 123-131 , 2022 2022
Classification of ECG signals using the Naïve Bayes classification method and its implementation in android-based smart health care P Madona, Y Zafitrah, JN Sari, MM Zain, M Valzon 2021 International conference on computer science and engineering (IC2SE) 1, 1-7 , 2021 2021 Citations: 10
Akuisisi Sinyal Electrocardiography (ECG) Berbasis Arduino P Madona, R Fadilla Jurnal Elektro dan Mesin Terapan 7 (1), 35-46 , 2021 2021 Citations: 3
Effect of Methadone on the Brain Activity in Close Eyes Condition A Turnip, DE Kusumandari, SA Sobana, AN Istiqomah, T Hidayat, ... Cyber Physical, Computer and Automation System: A Study of New Technologies … , 2021 2021 Citations: 2
PQRST wave detection on ECG signals P Madona, RI Basti, MM Zain Gaceta sanitaria 35, S364-S369 , 2021 2021 Citations: 75
THE DESIGN OF WHEELCHAIR SYSTEMS WITH RASPBERRY PI 3-BASED JOYSTICK ANALOG AND VOICE CONTROL P Madona, HK Nisa, Y Palapa W, A Akhyan ICo ASNItech , 2020 2020 Citations: 13
Controlling The Direction Of Wheelchair Movement Using Raspberry-Pi Based Brain Signals P Madona, RR Mujiono, YP Wijaya 2019 2nd International Conference on Applied Engineering (ICAE), 1-4 , 2019 2019 Citations: 5
Akuisisi dan Klasifikasi Sinyal EEG Untuk Lima Arah Pergerakan Berbasis Labview P Madona, M Hidayat, E Susianti Jurnal Elementer (Jurnal Elektro dan Mesin Terapan) 4 (November 2018), 46-52 , 2018 2018 Citations: 3
MOST CITED SCHOLAR PUBLICATIONS
PQRST wave detection on ECG signals P Madona, RI Basti, MM Zain Gaceta sanitaria 35, S364-S369 , 2021 2021 Citations: 75
Akuisisi data sinyal photoplethysmograph (ppg) menggunakan photodioda P Madona, CA Pratiwi Jurnal Elektro Dan Mesin Terapan 2 (2), 32-41 , 2016 2016 Citations: 15
Rancang Bangun Peringatan Bahaya Longsor dan Monitoring Pergeseran Tanah Menggunakan Komunikasi Berbasis GSM J Priyanto, P Madona, H Subagiyo Jurnal Elektro dan Mesin Terapan 2 (1), 43-54 , 2016 2016 Citations: 14
THE DESIGN OF WHEELCHAIR SYSTEMS WITH RASPBERRY PI 3-BASED JOYSTICK ANALOG AND VOICE CONTROL P Madona, HK Nisa, Y Palapa W, A Akhyan ICo ASNItech , 2020 2020 Citations: 13
Pengujian Parameter Tekanan Darah dan Detak Jantung Pada Alat Pendeteksi Tingkat Stress Manusia F Deza, P Madona, Tianur Applied Business and Engineering Conference (ABEC) 2013, 309 , 2013 2013 Citations: 12
Classification of ECG signals using the Naïve Bayes classification method and its implementation in android-based smart health care P Madona, Y Zafitrah, JN Sari, MM Zain, M Valzon 2021 International conference on computer science and engineering (IC2SE) 1, 1-7 , 2021 2021 Citations: 10
Alat Bantu Terapi Pasca Stroke Untuk Tangan P Madona, SR Syareza, R Oktiasari, E Susianti, M Sahar Jurnal ELEMENTER Vol 4 (1) , 2018 2018 Citations: 9
Alat Pendeteksi Tingkat Stress Manusia Berdasarkan Suhu Tubuh, Kelembaban Kulit, Tekanan Darah Dan Detak Jantung (Human Stress Level Detection Tool Based on Body Temperature … F Deza, P Madona, N Rahmardy Jurnal Elektro Dan Mesin Terapan 3 (2), 31-42 , 2017 2017 Citations: 9
Electrocardiogram signals classification using random forest method for web-based smart healthcare JN Sari, P Madona, H Kusryanto, MM Zain, M Valzon International Journal of Advances in Applied Sciences 12 (2), 133-43 , 2023 2023 Citations: 8
Sistem Instrumentasi dan Monitoring Pergeseran Tanah Menggunakan Sensor LVDT Berbasis Mikrokontroler J Priyanto, H Subagiyo, P Madona dalam Proceeding of 3rd Applied Business and Engineering Conference (ABEC … , 2015 2015 Citations: 6
Segmentasi Suara Jantung S1 dan S2 Menggunakan Kurva Amplop P Madona, A Arifin, TA Sardjono, R Hendradi 13th Seminar on Intelligent Technology and It’s Applications , 2012 2012 Citations: 6
Pelatihan Internet of Things (IoT) Bagi Siswa Ponpes Imam Ibnu Katsir P Madona Jurnal Pengabdian UntukMu NegeRI 7 (2), 6133-6133 , 2023 2023 Citations: 5
Controlling The Direction Of Wheelchair Movement Using Raspberry-Pi Based Brain Signals P Madona, RR Mujiono, YP Wijaya 2019 2nd International Conference on Applied Engineering (ICAE), 1-4 , 2019 2019 Citations: 5
PKM KElompok Usaha KErupuk Opak dalam meningkatkan kualitas dan kuantitas hasil produksi serta perbaikan strategi pemasaran P Madona UPI-YAI , 2018 2018 Citations: 5
Pelatihan Internet of Things (IoT) Untuk Guru SMK Negeri 7 Pekanbaru menggunakan NodeMCU M Rahmawaty, N Khamdi, P Madona JITER-PM (Jurnal Inovasi Terapan-Pengabdian Masyarakat) 1 (2), 47-52 , 2023 2023 Citations: 4
Akuisisi Sinyal Electrocardiography (ECG) Berbasis Arduino P Madona, R Fadilla Jurnal Elektro dan Mesin Terapan 7 (1), 35-46 , 2021 2021 Citations: 3
Akuisisi dan Klasifikasi Sinyal EEG Untuk Lima Arah Pergerakan Berbasis Labview P Madona, M Hidayat, E Susianti Jurnal Elementer (Jurnal Elektro dan Mesin Terapan) 4 (November 2018), 46-52 , 2018 2018 Citations: 3
Effect of Methadone on the Brain Activity in Close Eyes Condition A Turnip, DE Kusumandari, SA Sobana, AN Istiqomah, T Hidayat, ... Cyber Physical, Computer and Automation System: A Study of New Technologies … , 2021 2021 Citations: 2
Pelatihan Dan Pembimbingan Robot Line Follower Bagi Santri Pesantren Darussalam J Jaenudin, P Madona JITER-PM (Jurnal Inovasi Terapan-Pengabdian Masyarakat) 2 (4), 27-34 , 2024 2024 Citations: 1
Alat Ukur Kadar Gula Darah dan Informasi Dosis Insulin Berbasis Sinyal Photopletysmograph (PPG) P Madona, E Saputra, HN Syamsir Jurnal SISKOM-KB (Sistem Komputer dan Kecerdasan Buatan) 1 (2) , 2018 2018 Citations: 1
