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All Journal ELKOMIKA: Jurnal Teknik Energi Elektrik, Teknik Telekomunikasi, & Teknik Elektronika Jurnal Penelitian Pendidikan IPA (JPPIPA) Journal of Energy and Electrical Engineering (JEEE)
Nugraha, Nur Wisma
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Agriculture, Biological Sciences & Forestry Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Chemistry Control & Systems Engineering Education Electrical & Electronics Engineering Energy Engineering Materials Science & Nanotechnology Physics

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IMPLEMENTASI MACHINE LEARNING UNTUK IDENTIFIKASI ORANG BATUK / BERSIN Mulia, Sandy Bhawana; Nugraha, Nur Wisma; Robbani, Muhammad Hanif
Journal of Energy and Electrical Engineering Vol 4, No 2: 13 April 2023
Publisher : Teknik Elektro Universitas Siliwangi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37058/jeee.v4i2.6836

Abstract

Hal terpenting pada machine learning adalah fitur ekstraksi dari sebuah data mentah. Pada jurnal ini akan dijelaskan mengenai Implementasi Machine Learning untuk identifikasi orang batuk / bersin. Tujuan utama penelitian ini adalah bagaimana mengklasifikasi suara bersin dan batuk pada situasi yang sulit seperti noise yang terdapat pada data mentah. Pekembangan kecerdasan buatan yang cukup pesat terutama pada klasifikasi suara menggunakan machine learning dapat membantu untuk membedakan batuk dan bersin berdasarkan suara. Dalam tugas akhir ini, algoritma Long Short Term Memory (LSTM) digunakan karena algoritma ini mampu mengklasifikasikan pola suara dari suatu data. Untuk menghasilkan program pendeteksi batuk penulis membuat 3 program utama yaitu Pre-Processing, Training, dan Prediction. Melalui metode yang digunakan, Implementasi Machine Learning untuk identifikasi orang batuk / bersin mampu mencapai nilai rata-rata akurasi 68,52%, presisi 88,10% dan recall 62,03%.
Penerapan Human Machine Interface Berbasis Augmented Reality pada Sistem SCADA Modular Production System ABADI, SAROSA CASTRENA; NUGRAHA, NUR WISMA; DHIMYATI, IRFAN AHMAD; SUMARSO, ADE HASAN
ELKOMIKA: Jurnal Teknik Energi Elektrik, Teknik Telekomunikasi, & Teknik Elektronika Vol 11, No 2: Published April 2023
Publisher : Institut Teknologi Nasional, Bandung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26760/elkomika.v11i2.285

Abstract

ABSTRAKPenerapan teknologi digital pada sistem SCADA dapat dilakukan dengan menampilkan user interface dengan metode Augmented Reality yaitu suatu teknologi yang menggabungkan suatu objek nyata dan objek virtual secara realtime. Penelitian ini bertujuan untuk mengimplementasikan HMI NodeRED dengan metode pengamatan Augmented Reality pada Sistem SCADA Modular Production Systems (MPS) bagian Testing dan Handling Station. Hasil pengujian menunjukkan sistem berhasil melakukan kontrol dan monitoring plant Testing dan Handling Station melalui Webview Augmented Reality. Nilai rata-rata waktu pengiriman data PLC ke PC pada Testing Station yaitu 109 ms dan rata-rata waktu yang pengiriman data PC ke PLC yaitu 106 ms. Sedangkan untuk Handling Station rata-rata waktu pengiriman data PLC ke PC yaitu 109 ms dan rata-rata waktu pengiriman data PC ke PLC yaitu 105 ms.Kata kunci: Augmented Reality, SCADA, NodeRED, PLC, MPS ABSTRACTThe Implementation of digital technology in the SCADA system can be done by displaying a user interface with the Augmented Reality method, which is a technology that combines a real object and a virtual object in real-time. This study aims to implement HMI NodeRED with the Augmented Reality observation method on the SCADA Modular Production Systems (MPS) for testing and handling stations. The test results showed that the system successfully controlled and monitored the Testing and Handling Station plants through the Augmented Reality Webview.The average value of the time of sending PLC data to PC at the Testing Station is 109 ms and the average time that PC data is sent to the PLC is 106 ms. While for the Handling Station, the average time for sending PLC data to PC is 109 ms and the average time for sending PC data to PLC is 105 ms.Keywords: Augmented Reality, SCADA, NodeRED, PLC, MPS
Implementasi Wireless Sensor Network pada Sistem Manajemen Gedung Menggunakan Protokol Komunikasi Modbus TCP ABADI, SAROSA CASTRENA; NUGRAHA, NUR WISMA; AMINAH, SITI
ELKOMIKA: Jurnal Teknik Energi Elektrik, Teknik Telekomunikasi, & Teknik Elektronika Vol 10, No 3: Published July 2022
Publisher : Institut Teknologi Nasional, Bandung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26760/elkomika.v10i3.514

Abstract

ABSTRAKWireless Sensor Network merupakan suatu solusi potensial dalam sistem manajemen gedung agar pengelolaan sumber daya gedung menjadi lebih efektif. Penelitian ini membahas mengenai implementasi wsn pada sistem manajemen gedung dengan menerapkan metode komunikasi Modbus TCP. Sistem terdiri dari monitoring volume sampah, pengendalian kunci pintu, tombol emergency dan sistem pengawasan video dengan memanfaatkan dashboard Node-RED dan Platform IoT Ubidots. Hasil pengujian menunjukkan bahwa sistem berfungsi sesuai program yang dibuat dengan nilai rata-rata error pembacaan sensor 2,52%, maksimal jarak antara Node sensor dengan Node sink yaitu 20 meter. Tingkat keberhasilan sistem kontrol kunci pintu melalui dashboard Node-RED dan Ubidots sebesar 100 % dengan masing-masing rata-rata delay sebesar 225ms dan 489 ms kemudian nilai rata-rata delay pengiriman pesan notifikasi whatsapp sebesar 2504 ms.Kata kunci: WSN, Building, Modbus TCP, Raspberry Pi, Node-RED ABSTRACTWireless Sensor Network is a potential solution in building management systems so that building resource management becomes more effective. This study discusses the implementation of wsn on building management systems by applying the MODBUS TCP communication method. The system consists of garbage volume monitoring, door lock control, emergency buttons and a video surveillance system utilizing the Node-RED dashboard and Ubidots IoT Platform. The test results showed that the system functioned according to the program created with an average sensor reading error value of 2.52%, the maximum distance between the sensor Node and node sink is 20 meters. The success rate of the door lock control system through the Node-RED and Ubidots dashboards is 100% with an average delay of 225ms and 489 ms respectively and the average delay of whatsapp notification message delivery is 2504 ms.Keywords: WSN, Building, Modbus TCP, Raspberry Pi, Node-RED
Kendali Aliran dan Tekanan Adaptif dengan Metode Artificial Neural Network pada Alat Terapi Oksigen SALAM, ABYANUDDIN; NUGRAHA, NUR WISMA; ALFARIDHANI, WILDAN
ELKOMIKA: Jurnal Teknik Energi Elektrik, Teknik Telekomunikasi, & Teknik Elektronika Vol 12, No 1: Published January 2024
Publisher : Institut Teknologi Nasional, Bandung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26760/elkomika.v12i1.133

Abstract

ABSTRAKPenelitian ini bertujuan untuk merancang prototype pengendalian aliran dan tekanan adaptif pada alat terapi oksigen. Sensor yang digunakan yaitu sensor MAX30100 untuk membaca saturasi oksigen dan sensor MLX90614 sebagi sensor yang dapat menghitung Respiration Rate atau laju napas. Metode yang digunakan yaitu Artificial Neural Network yang diimplentasikan pada Raspberry Pi. Sistem akan bekerja dengan memprediksi nilai laju aliran dan tekanan oksigen yang diperlukan pasien berdasarkan nilai Respiration Rate (RR). Artificial Neural Network (ANN) dapat diimplmentasikan pada rancangan alat terapi oksigen, dengan persentase akurasi Output ANN terhadap perhitungan yaitu 99,39%, sedangkan persentase akurasi ANN terhadap pembacaan aliran oksigen yang terbaca pada sensor flow sebesar yaitu 94,73% dan persentase akurasi ANN terhadap pembacaan tekanan oksigen pada sensor pressure sebesar 89,03%.Kata kunci: Terapi Oksigen, Respiration Rate, Artificial Neural Network ABSTRACTThis research aims to design a prototype of flow and pressure control in an adaptive oxygen therapy device. The sensors used are MAX30100 sensors to read oxygen saturation and MLX90614 sensors as sensors that can calculate Respiration Rate or breath rate. The method used is Artificial Neural Network which is implemented on Raspberry Pi. The system will work by predicting the value of the flow rate and oxygen pressure required by the patient based on the Respiration Rate (RR) value. Artificial Neural Network (ANN) can be implemented in the design of oxygen therapy devices, with the percentage of ANN Output accuracy to the calculation of 99.39%, while the percentage of ANN accuracy on oxygen flow readings on the flow sensor is 94.73% and the percentage of ANN accuracy on oxygen pressure readings on the pressure sensor is 89.03%.Keywords: Oxygen Therapy, Respiration Rate, Artificial Neural Network
Implementation of Industrial IoT Integration Using Node-RED and PLC on Cascade Control Level and Flow Plant Nugraha, Nur Wisma; Suryatini, Fitria; Lilansa, Noval; Farhan, Farhan Ali Madani
Jurnal Penelitian Pendidikan IPA Vol 11 No 6 (2025): June
Publisher : Postgraduate, University of Mataram

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29303/jppipa.v11i6.11623

Abstract

Accurate control of liquid level and flow is essential in industrial processes to ensure efficiency and product quality. Conventional single PID control methods often suffer from steady-state errors and slow response times due to the dynamic interaction between level and flow. This research develops a cascade control system using a PLC integrated with a Node-RED based Industrial Internet of Things (IIoT) platform to overcome these limitations. The cascade control system applies two control loops, the outer loop for level control and the inner loop for flow control, with PID parameters optimized through trial-and-error method. IIoT integration enables real-time monitoring, remote control, and data logging through an interactive dashboard. Experimental results show the system is able to achieve stability with steady-state error reduced to ± 0.5 cm, faster settling time and rise time, and better disturbance resistance. Very low communication delays support real-time operation. The system offers a practical and effective solution for precise liquid level and flow control, aligned with the demands of Industry 4.0 and can be a model for educational and industrial applications.
Co-Authors Abadi, Sarosa Castrena Abyanuddin Salam ALFARIDHANI, WILDAN DHIMYATI, IRFAN AHMAD Farhan, Farhan Ali Madani Fitria Suryatini Robbani, Muhammad Hanif Sandy Bhawana Mulia, Sandy Bhawana Siti Aminah SUMARSO, ADE HASAN