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All Journal International Journal of Power Electronics and Drive Systems (IJPEDS) Bulletin of Electrical Engineering and Informatics JITTER (Jurnal Ilmiah Teknologi Informasi Terapan) Jurnal ULTIMA Computing Jurnal Teknologi Terpadu Abdi Dosen : Jurnal Pengabdian Pada Masyarakat Indonesian Journal of Electrical Engineering and Computer Science Jurnal Dharma Bhakti Ekuitas Journal La Multiapp Jurnal Nasional Teknik Elektro dan Teknologi Informasi EPSILON: Journal of Electrical Engineering and Information Technology JURNAL TEKNIK INDUSTRI Eduvest - Journal of Universal Studies Jurnal Elektronika dan Telekomunikasi
Silmi Ath Thahirah Al Azhima
Universitas Pendidikan Indonesia
Religion Aerospace Engineering Humanities Automotive Engineering Chemical Engineering, Chemistry & Bioengineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Health Professions Industrial & Manufacturing Engineering Mechanical Engineering Neuroscience Public Health Social Sciences Other

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Hybrid Machine Learning Model untuk memprediksi Penyakit Jantung dengan Metode Logistic Regression dan Random Forest Silmi Ath Thahirah Al Azhima; Dwicky Darmawan; Nurul Fahmi Arief Hakim; Iwan Kustiawan; Mariya Al Qibtiya; Nendi Suhendi Syafei
Jurnal Teknologi Terpadu Vol. 8 No. 1: July, 2022
Publisher : LPPM STT Terpadu Nurul Fikri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.54914/jtt.v8i1.539

Abstract

The heart is the main organ that must work properly and regularly. If there is interference, it will be fatal, namely the onset of a heart attack. Heart attack is included in the 10 diseases with a high risk of death. This is caused by stress factors, blood pressure, excessive work, blood sugar, and others. The purpose of this study is to predict heart disease using Machine Learning (ML) algorithms as an early preventive measure on desktop-based information systems. With Machine Learning models, the hybrid model can increase the accuracy value of an ML method that is added to other ML methods. The accuracy value obtained from the Hybrid Model Machine Learning using the Random Forest and Logistic Regression methods is 84.48%, which is an increase of 1.32%.  
Microsoft Office Spesialist, Met PELATIHAN MICROSOFT OFFICE SPECIALIST (MOS) POWER POINT DALAM UPAYA PENINGKATAN KOMPETENSI GURU SMK PADA ERA DISRUPSI INDUSTRI 4.0 Resa Pramudita; Roer Eka Pawinanto; Muhammad Adli Rizqulloh; Nurul Fahmi Arief Hakiem; Mariya Al Qibtiya; Silmi Ath Thahirah Al Azhima
Jurnal Ilmiah Teknologi Infomasi Terapan Vol. 8 No. 3 (2022)
Publisher : Universitas Widyatama

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (516.088 KB) | DOI: 10.33197/jitter.vol8.iss3.2022.910

Abstract

Era disrupsi Industri membawa banyak pengaruh dalam kehidupan manusia, hal ini juga yang mempengaruhi dunia pendidikan, Salah satu cara meningkatkan kompetensi guru di era revolusi industry 4.0 ini adalah membekali guru dengan kemampuan skala global yaitu Microsoft Office Specialist atau MOS. Dari permasalahan tersebut kami berinisiatif untuk mengadakan Kegiatan pelatihan Microsoft Office Specialist (MOS) Power Point bagi guru SMK/SMA di kota Bandung. Pelatihan ini mengadaptasi model pelatihan Goad, yaitu Model ini terdiri atas beberapa siklus diantaranya adalah: (1) analisis kebutuhan pelatihan; (2)desain pendekatan pelatihan; (3) pengembangan materi pelatihan; (4) pelaksanaan pelatihan; (5) evaluasi dan pemutakhiran pelatihan.
Sistem Informasi Rekam Medis Berbasis Aplikasi Desktop untuk Daerah Pedesaan Dwicky Darmawan; Silmi Ath Thahirah Al Azhima; Nurul Fahmi Arief Hakim
EPSILON: Journal of Electrical Engineering and Information Technology Vol 20 No 2 (2022): EPSILON: Journal of Electrical Engineering and Information Technology
Publisher : Department of Electrical Engineering, UNJANI

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55893/epsilon.v20i2.89

Abstract

Rural areas have several challenges that must be resolved. The availability of internet connections in the rural area is also one of the facilities that until now has not been evenly distributed. The clinical administration system is still conventional which has many weaknesses such as data processing, time management, and requires document storage space. So that the conventional system reduces the quality of clinical health services in the area. Therefore, this research was made with the method used is the waterfall model which aims to solve the challenges in making information systems. The advantage of using this method is that it is easy to use, directed, structured, and good for optimization. In this research, the information system created is in the form of a desktop application and is made using the Python programming language and MySQL database as a data storage area. This system is able to store personal data and patient history data according to the required information. In addition, this clinical information system is able to process data quickly and accurately, minimize lost data, search for the required data quickly, make patient data reports quickly and neatly, the system can be accessed by users even though there is no internet signal, and can reduce use of paper and reduce the use of space for patient data document storage.
Cumulative error correction of inertial navigation systems using LIDAR sensors and extended Kalman filter Silmi Ath Thahirah Al Azhima; Dadang Lukman Hakim; Robby Ikhfa Nulfatwa; Nurul Fahmi Arief Hakim; Mariya Al Qibtiya
Indonesian Journal of Electrical Engineering and Computer Science Vol 34, No 2: May 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v34.i2.pp878-887

Abstract

Autonomous robots have gained significant attention in research due to their ability to facilitate human work. Navigation systems, particularly localization, present a challenge in autonomous robots. The inertial navigation system is a localization system that uses inertial sensors and a wheel odometer to estimate the robot’s relative position to the initial position. However, the system is susceptible to continuous error accumulation over time due to factors like sensor noise and wheel slip. To address these issues, external sensors are required to measure the robot’s position in the environment. The extended Kalman filter (EKF) method is utilized to estimate the robot’s position based on wheel odometer and light detection and ranging (LIDAR) sensor measurements. In the prediction stage, the input to the EKF is the position measurement from the wheel odometer, while the LIDAR sensor’s position measurement is used in the update stage to improve the prediction stage results. The test results reveal that the EKF’s estimated position has a lower average error compared to the position measurement using the wheel odometer. Therefore, it can be concluded that the EKF technique is effectively applied to the robot and can correct the wheel odometer's cumulative error with the assistance of the LIDAR sensor.
Modeling and Simulation of 4-DOF RRPR Manipulator Robot Using MATLAB Permadi, Tesya; Ath Thahirah Al Azhima, Silmi; Al Qibtya, Mariya; Arief Hakim, Nurul Fahmi
Ultima Computing : Jurnal Sistem Komputer Vol 16 No 1 (2024): Ultima Computing : Jurnal Sistem Komputer
Publisher : Faculty of Engineering and Informatics, Universitas Multimedia Nusantara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31937/sk.v16i1.3628

Abstract

In recent decades, the rapid development of robotic technology has increased the demand for efficient and affordable robots, especially in the manufacturing industry. One type of robot that is commonly used is the robotic arm, which is capable of performing complex tasks with high precision and speed. This research focuses on the modeling and simulation of a 4-DOF RRPR robot manipulator using MATLAB software, including the SimScape Multibody Toolbox and Robotic System Toolbox. This study investigates various aspects of robot performance, such as joint angles, end-effector coordinates, and robot dynamics. With an emphasis on simulation, this research aims to accelerate the development of robotic technology and minimize the risks associated with physical implementation in the field. The simulation results provide valuable insights for improving the efficiency, precision, and reliability of robot manipulators in various applications. Furthermore, this research suggests future research directions, such as the exploration of advanced control systems to dynamically compensate for disturbances and the investigation of robots with higher degrees of freedom for more adaptive technology in challenging operational conditions.
One-Phase Smart Switch using OpenCV Hand Gesture Recognition Fauzan, Mochamad Rizal; Khairi, Saiqa Fatur; Kaniarudi, Neneng Puspita; Ath Thahirah Al Azhima, Silmi; Arief Hakim, Nurul Fahmi; Kustiawan, Iwan; Al Qibtya, Mariya; Elvyanti, Siscka
Ultima Computing : Jurnal Sistem Komputer Vol 16 No 1 (2024): Ultima Computing : Jurnal Sistem Komputer
Publisher : Faculty of Engineering and Informatics, Universitas Multimedia Nusantara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31937/sk.v16i1.3633

Abstract

The need for simplicity in various activities encourages further technological development. One of them is a system to turn lights on and off with just a hand gesture. This hand gesture-based One-phase smart switch uses OpenCV, Arduino Nano, relays, and webcam cameras to recognize hand gestures. Static finger movements are used as buttons to turn on the lights. The results show that the algorithm used has high reliability with a precision score of 0.90, sensitivity of 0.90, accuracy of 0.96, and F1-score of 0.90. The accuracy of the system is affected by light intensity, distance, and hand tilt angle. At a light intensity of 70 LUX, the accuracy reaches 100%, while at 40 LUX the accuracy ranges from 98-99%. A distance of 30-60 cm gave the best accuracy of 100%, but decreased at longer distances. A hand tilt of 0° gives 100% accuracy, while at an angle of 60° the accuracy drops significantly, especially for the fifth finger with 64% accuracy. The average response time of the light to finger movement is 0.133 seconds. This device can recognize a variety of finger patterns well, thus meeting the desired needs.
Micropower design of energy harvesting based on piezoelectric transducer array circuit Juanda, Enjang Akmad; Hakim, Nurul Fahmi Arief; Sarosa, Moechammad; Saputra, Dede Irawan; Azhima, Silmi Ath Thahirah Al; Qibtiya, Mariya Al
International Journal of Power Electronics and Drive Systems (IJPEDS) Vol 15, No 3: September 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijpeds.v15.i3.pp1767-1776

Abstract

This article reveals the conceptualization and implementation of energy harvesting system that utilize piezoelectric arrays within environments marked by elevated ambient noise levels. The selected methodology involves conducting an empirical study where the system is introduced into a room with pronounced ambient noise. A series configuration is adopted for assembling the piezoelectric sensors. For this particular experiment, 36 piezoelectric sensor units were arranged, each equipped with a voltage doubler circuit, aiming to harness a specific micropower energy threshold. The experimental results validate the successful development of an energy harvesting mechanism employing a piezoelectric array within a noisy setting. Notably, the device functions optimally at a frequency of 250 Hz. Additionally, a series of controlled experimental tests were executed at a sound level of 95.8 dBA to assess the efficacy of the piezoelectric array. Measurements taken at the voltage doubler output reveal that the device achieves its peak output signal at 3.32 Vpp and 50.42 Hz. The maximum attainable direct current (DC) voltage stands at 1 Volt, complemented by a current of 0.45 mA.
Real-Time Monitoring System for Peatland Fire Potential Based on Internet of Things Nawawi, Imam Khushthon; Al Qibtiya, Mariya; Al Azhima, Silmi Ath Thahirah; Arief Hakim, Nurul Fahmi
Journal La Multiapp Vol. 5 No. 4 (2024): Journal La Multiapp
Publisher : Newinera Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37899/journallamultiapp.v5i4.1449

Abstract

This research aims to develop a real-time monitoring system for peatland fire potential based on the Internet of Things (IoT) with a focus on early detection of potential peatland fires. The main problem to be solved is the lack of an effective system in the early detection of potential peatland fires, which can cause serious environmental impacts. The method used involves the use of air temperature, air humidity, soil moisture, and fire detection sensors integrated with alarm-based alerts. Data collection is done in real-time to provide a deeper understanding of peatland conditions and potential fire risks. The research results show that the developed system is capable of providing accurate and fast information related to peatland conditions, thus helping to prevent and reduce the impact of peatland fires. With this system, it is expected to increase efficiency in early fire detection and minimize the losses caused by peatland fires.
Air Filtration System Utilizing Biomimetic Technology and IoT for Air Quality Improvement Fauzan, Mochamad Rizal; Al Azhima, Silmi Ath Thahirah; Pramudita, Resa; Hakim, Dadang Lukman; Rahmawati, Hanifah Indah; Azmi, Mutiara Nabila; Fauzi, Rafi Rahman; Somantri, Maman; Rahayu, Sri
Ultima Computing : Jurnal Sistem Komputer Vol 16 No 2 (2024): Ultima Computing : Jurnal Sistem Komputer
Publisher : Faculty of Engineering and Informatics, Universitas Multimedia Nusantara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31937/sk.v16i2.3871

Abstract

The "Hepix" smart air filtration system, developed with biomimetic and Internet of Things (IoT) technology, aims to address the urgent issue of poor indoor air quality, particularly in high-mobility urban areas. This system integrates advanced sensors (MQ135 and BME680) and biomimetic filtration inspired by leaf stomata to monitor and filter air pollutants. Tested across three locations—Cilame, Jatinangor, and Cibiru—the system achieved an approximate 24.4% reduction in pollutant levels, as well as stable control of humidity and air pressure. Real-time data is continuously monitored through a mobile and web interface, supported by Google Assistant integration for voice commands. The results demonstrate that "Hepix" effectively improves air quality, offering a practical solution for healthier indoor environments in urban areas.
One-Phase Smart Switch using OpenCV Hand Gesture Recognition Fauzan, Mochamad Rizal; Khairi, Saiqa Fatur; Kaniarudi, Neneng Puspita; Ath Thahirah Al Azhima, Silmi; Arief Hakim, Nurul Fahmi; Kustiawan, Iwan; Al Qibtya, Mariya; Elvyanti, Siscka
ULTIMA Computing Vol 16 No 1 (2024): Ultima Computing : Jurnal Sistem Komputer
Publisher : Faculty of Engineering and Informatics, Universitas Multimedia Nusantara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31937/sk.v16i1.3633

Abstract

The need for simplicity in various activities encourages further technological development. One of them is a system to turn lights on and off with just a hand gesture. This hand gesture-based One-phase smart switch uses OpenCV, Arduino Nano, relays, and webcam cameras to recognize hand gestures. Static finger movements are used as buttons to turn on the lights. The results show that the algorithm used has high reliability with a precision score of 0.90, sensitivity of 0.90, accuracy of 0.96, and F1-score of 0.90. The accuracy of the system is affected by light intensity, distance, and hand tilt angle. At a light intensity of 70 LUX, the accuracy reaches 100%, while at 40 LUX the accuracy ranges from 98-99%. A distance of 30-60 cm gave the best accuracy of 100%, but decreased at longer distances. A hand tilt of 0° gives 100% accuracy, while at an angle of 60° the accuracy drops significantly, especially for the fifth finger with 64% accuracy. The average response time of the light to finger movement is 0.133 seconds. This device can recognize a variety of finger patterns well, thus meeting the desired needs.
Co-Authors Aan Eko Setiawan Al Qibtya, Mariya Aldy Padmanegara Putra Andre Reyvaldo Iglesias Arief Suryadi Satyawan Arief Suryadi Satyawan Azmi, Mutiara Nabila Dadang Lukman Hakim Darmawati, Irma Dwicky Darmawan Erik Haritman Erik Haritman Erik Haritman Fathin, Muhammad Alif Fathin Fauzan, Mochamad Rizal Fauzi, Rafi Rahman Haritman, Erik Haritman Ibnu Hartopo Iwan Kustiawan Juanda, Enjang Akmad Kaniarudi, Neneng Puspita Khairi, Saiqa Fatur Luthfi Maulana, Luthfi M Sadam Al Jabbar Maman Somantri Mariya Al Qibtiya Mariya Al Qibtiya Moechammad Sarosa Muhammad Adli Rizqulloh Nawawi, Imam Khushthon Nendi Suhendi Syafei Nurhakim, Raihan Nurul Fahmi Arief Hakim Permadi, Tesya Pramudita, Resa Rahmawati, Hanifah Indah Resa Pramudita Resa Pramudita Ridwan , Azwar Mudzakkir Robby Ikhfa Nulfatwa Roer Eka Pawinanto, Roer Eka Saepul Rohman Saputra, Dede Irawan Siscka Elvyanti, Siscka SRI RAHAYU