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All Journal JOIV : International Journal on Informatics Visualization Journal of Aero Technology
Zikri, Arizal Akbar
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Computer Science & IT

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Pengembangan Sistem Streaming Sensor Data Temperatur dan Kelembapan di Ruang Laboratorium BBTA3 Melalui Teknologi Open Source Zikri, Arizal Akbar
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Publisher : BPPT

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (486.245 KB) | DOI: 10.29122/joat.v2i1.3820

Abstract

AbstractDevelopment of temperature and humidity data streaming Application from sensors in the server room is necessary to enable the operator to monitor the real-time air condition in the server room, without having to enter the room. This application development utilizes open source technology to make developers more independent and able to interact easily within the community without license hassle.Sensor data reading is done by Raspberry Pi, assigned as a producer in sending data to Kafka Cluster. Kafka is an open source technology used as tools for streaming data distributedly. One node in a cluster is set to receive sensor data, known as consumer, executes python service to handle requests from users through Server Sent Event (SSE) in form of REST API.This application is called TempHum and can be executed on Desktop (Windows, Linux, Mac OS), web browser, and smartphone (Android and iOS). Hence, the application can serve many clients in monitoring air condition in realtime.Keywords: open source, cluster, raspberry pi, kafka, python.AbstraksAplikasi streaming data sensor berupa temperatur dan kelembapan di ruang server perlu dikembangkan, sehingga memudahkan bagi operator untuk memantau kondisi udara terkini secara dinamis di ruang server tanpa harus masuk kedalam ruang tersebut. Pengembangan aplikasi dilakukan menggunakan teknologi open source agar memudahkan pengembang untuk mandiri dan berinteraksi dalam komunitas tanpa terikat dengan permasalahan lisensi.Pembacaan data sensor dilakukan oleh Raspberry Pi dan dijadikan sebagai producer untuk mengirimkan data tersebut ke Kafka Cluster. Kafka merupakan teknologi open source yang digunakan sebagai alat untuk streaming data terdistribusi. Satu node dalam cluster digunakan untuk menerima kiriman data atau dikenal sebagai consumer sekaligus menjalankan python servis untuk menangani permintaan dari pengguna aplikasi melalui Server Sent Event (SSE) dalam bentuk REST API.Aplikasi ini diberi nama TempHum dan dapat dijalankan di Desktop (Windows, Linux, Mac OS), web browser, dan smartphone (Android dan iOS), sehingga aplikasi ini dapat melayani banyak pengguna dalam memantau kondisi ruang server secara dinamis.Kata Kunci : open source, cluster, raspberry pi, kafka, python. 
PENGEMBANGAN SISTEM STREAMING SENSOR DATA TEMPERATUR DAN KELEMBAPAN DI RUANG LABORATORIUM BBTA3 MELALUI TEKNOLOGI OPEN SOURCE Zikri, Arizal Akbar
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Publisher : BPPT

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (486.245 KB) | DOI: 10.29122/joat.v2i1.3820

Abstract

AbstractDevelopment of temperature and humidity data streaming Application from sensors in the server room is necessary to enable the operator to monitor the real-time air condition in the server room, without having to enter the room. This application development utilizes open source technology to make developers more independent and able to interact easily within the community without license hassle.Sensor data reading is done by Raspberry Pi, assigned as a producer in sending data to Kafka Cluster. Kafka is an open source technology used as tools for streaming data distributedly. One node in a cluster is set to receive sensor data, known as consumer, executes python service to handle requests from users through Server Sent Event (SSE) in form of REST API.This application is called TempHum and can be executed on Desktop (Windows, Linux, Mac OS), web browser, and smartphone (Android and iOS). Hence, the application can serve many clients in monitoring air condition in realtime.Keywords: open source, cluster, raspberry pi, kafka, python.AbstraksAplikasi streaming data sensor berupa temperatur dan kelembapan di ruang server perlu dikembangkan, sehingga memudahkan bagi operator untuk memantau kondisi udara terkini secara dinamis di ruang server tanpa harus masuk kedalam ruang tersebut. Pengembangan aplikasi dilakukan menggunakan teknologi open source agar memudahkan pengembang untuk mandiri dan berinteraksi dalam komunitas tanpa terikat dengan permasalahan lisensi.Pembacaan data sensor dilakukan oleh Raspberry Pi dan dijadikan sebagai producer untuk mengirimkan data tersebut ke Kafka Cluster. Kafka merupakan teknologi open source yang digunakan sebagai alat untuk streaming data terdistribusi. Satu node dalam cluster digunakan untuk menerima kiriman data atau dikenal sebagai consumer sekaligus menjalankan python servis untuk menangani permintaan dari pengguna aplikasi melalui Server Sent Event (SSE) dalam bentuk REST API.Aplikasi ini diberi nama TempHum dan dapat dijalankan di Desktop (Windows, Linux, Mac OS), web browser, dan smartphone (Android dan iOS), sehingga aplikasi ini dapat melayani banyak pengguna dalam memantau kondisi ruang server secara dinamis.Kata Kunci : open source, cluster, raspberry pi, kafka, python. 
PREDIKSI PARAMETER REDAMAN SINYAL RESPON DINAMIK MENGGUNAKAN METODA LSCE DENGAN BAHASA PYTHON Zikri, Arizal Akbar; Saputra, Angga Dwi
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Publisher : BPPT

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (386.672 KB) | DOI: 10.29122/joat.v1i2.3069

Abstract

In the aeroelastic test on flight test or wind tunnel, damping is a critical paramater to determine boundary flutter speed. Therefore, We need accurate and effective  methods to extract damping. Damping parameter were obtained by extraction dynamic response signals of structure under test. Least Square Complex Exponential (LSCE) are used for extracting damping value. Analyzed signals was a transient signal which is simulated by python in certain damping and frequency value. The final results for damping value are compared between python and matlab.Keywords: damping, flutter, structure, aeroelastic, extractionAbstrakPada pengujian aeroelastik pada uji terbang  atau terowongan angin, redaman merupakan parameter yang kritikal  untuk menentukan batas kecepatan flutter.  Untuk itu dibutuhkan metode ekstraksi nilai redaman yang akurat dan efektif. Nilai redaman didapatkan dari ekstraksi respon dinamik struktur yang diuji. Metode ekstraksi yang digunakan, yaitu Least Square Complex Exponential (LSCE). Data yang dianalisis merupakan sinyal transien yang disimulasikan melalui pemrograman python dengan nilai parameter redaman dan frekuensi tertentu. Hasil perhitungan redaman menggunakan python dibandingkan dengan matlab. Kata kunci : redaman, flutter, struktur, aeroelastik, ekstraksi
Convolutional Neural Networks-Based For Predicting Aerodynamic Coefficient Of Airfoils At Ultra-Low Reynolds Number Kasman, Alief Sadlie; Zikri, Arizal Akbar; Fariduzzaman, Fariduzzaman; Srigutomo, Wahyu
JOIV : International Journal on Informatics Visualization Vol 8, No 1 (2024)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62527/joiv.8.1.2197

Abstract

Many applications, including airplane design, wind turbines, and heat transmission, use symmetric or asymmetric airfoils. Engineers employ these airfoil shapes to optimize performance and efficiency. Each airfoil has a unique set of aerodynamic coefficients that must be calculated to maximize the airfoil design. Engineers utilize numerous ways to calculate coefficients, such as lift and drag. One of the methods is the prediction method, which effectively reduces time and cost. This study's training dataset is obtained from particle-based numerical computation using the Lattice Boltzmann Method (LBM). Then, Convolutional Neural Networks (CNN) are used as a prediction method to get the aerodynamic coefficients of airfoils for lift and drag based on two different Reynolds numbers. In CNN, airfoil geometry representation is essential. The Signed Distance Function (SDF) was used to convert airfoil geometry into RGB pictures. On the other hand, the SDF method cannot explain different flow conditions; in this case, it is represented by the Reynolds number (Re). Therefore, we propose a Text-based Watermarking Method (TWM) to differentiate between Re = 500 and Re = 1000. Each airfoil representation was trained and tested to generate each prediction model using a modified LeNet-5. The computation results show that using CNN with TWM on SDF to define the Reynolds numbers could predict the lift and drag coefficients with varying angles of attack. Future research can focus on generalizations to different aerodynamic aspects and practical applications in complex scenarios.
Co-Authors Angga Dwi Saputra fariduzzaman, fariduzzaman Kasman, Alief Sadlie Wahyu Srigutomo