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All Journal Elektron Jurnal Ilmiah JOIV : International Journal on Informatics Visualization Journal of Information Technology and Computer Engineering JITSI : Jurnal Ilmiah Teknologi Sistem Informasi Jurnal Sistim Informasi dan Teknologi Proceeding International Applied Business and Engineering Conference JECCOM
- Hendrick
Politeknik Negeri Padang
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A Portable Device of Air Pollution Measurement Due to Highway Exhaust Emissions Using LabVIEW Programming - Andrizal; - Lifwarda; Anna Yudanur; Rivanol Chadry; - Hendrick
JOIV : International Journal on Informatics Visualization Vol 5, No 4 (2021)
Publisher : Politeknik Negeri Padang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30630/joiv.5.4.697

Abstract

A multisensory gas device integrated with myRIO module to measure air pollution has been established. This device is programmed using the LabVIEW programming language and can measure CO2, CO, NOX, and HC pollution on roads due to motor vehicle exhaust emissions. The device and the display system are made separately using wireless network communication to make this tool portable. Exhaust Gas Analyzer (EGA) was chosen for device calibration, obtaining 3.62% on the average error after performing 30 tests. The tests for measuring CO, CO2, NOX, and HC gas levels were conducted in several locations in Padang City and performed in the morning, afternoon, and evening. The result showed that the system properly measured CO2, CO, NOX and HC pollution in parks and highways in real-time in parts per million (ppm). It also displayed varied gas measurement results in terms of time and test location with a range of CO gas values at 0.034 – 0.15 ppm, CO2 151.3 – 815.2 ppm, NOX 0.0001 – 0.004 ppm, and HC 0.04 – 0.65 ppm. In addition, the system could perform well in providing warnings by automatically activating the air indicator alert at several measurement places when the gas content on one of the gas elements and compounds at a particular location has exceeded the threshold for the clean air category. Thus, this device can be used as initial research to build a real-time air pollution measurement system using the Internet of Things (IoT).
Alat Pengukur Ketinggian Air Pada Landasan Pacu Pesawat Dengan Metode Image Processing ivan Finiel Hotmartua Bagariang; Hendrick Hendrick
Elektron : Jurnal Ilmiah Volume 13 Nomor 2 Tahun 2021
Publisher : Teknik Elektro Politeknik Negeri Padang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30630/eji.13.2.255

Abstract

An aircraft accident are caused by several factors, one of which is the weather. High rainfall causes the runway to become slippery and wet. This will cause hydroplaning events on the plane. Hyproplaning is a skidding event caused by puddles of water on the runway. The maximum height of puddles on the runway is 3 mm. Currently, to measure the water level is still done in a conventional way and there is no sensor capable of measuring the water level in mm. To prevent accidents due to hydroplaning, a measuring device is needed that can monitor the water level in millimeters and has a long size range and is calibrated on the runway. This water level meter is made using a raspberry Pi 3 B+ miniPC. The stages in making this tool include hardware design and software design. Measurements were carried out using the image processing method with a Raspberry Pi camera and an optical liquid water sensor. The results of the measurements are displayed on the monitor screen with the VNC Viewer software. The measurement results using an optical liquid water level sensor are accurate with a relative error of 0% in each measurement, namely at 1 mm, 2 mm and 3 mm. For measurements using the image processing method, the average error generated is 2.3% at a height of 1 mm, 6.9% at a height of 2 mm, 3.3% at a height of 3 mm, 0.9% at a height of 4 mm and 0 .24% at 5 mm height.
Rancang Bangun Instrumentasi Elektrokardiograf (EKG) dan Klasifikasi Kenormalan Jantung Pada Pola Sinyal EKG Menggunakan Learning Vector Quantization (LVQ) Maulana Maulana; Hendrick Hendrick; Ratna Aisuwarya
JITCE (Journal of Information Technology and Computer Engineering) Vol 2 No 01 (2018): Journal of Information Technology and Computer Engineering
Publisher : Universitas Andalas

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (694.839 KB) | DOI: 10.25077/jitce.2.01.19-26.2018

Abstract

Electrocardiograph (ECG) is a recorder of human heart signals with signal output on a monitor or graph paper. The ECG records the measurement of the electrical activity of the heart from the surface of the body by a set of electrodes that are installed in such a way that reflects the tapping point activity. The pattern of ECG output signals in one heartbeat produces a pattern with a peak point P, Q, R, S and T or QRS complex. ECG signal waveform results were analyzed using Learning Vector Quantization (LVQ) Artificial Neural Networks, and grouped into two classes, namely normal and abnormal heart patterns. The normal heart condition that is trained is a medically normal heart categorized as healthy as 10 data, while an abnormal heart (Heart, Coronary Heart, and Aortic Regurgutation) is 20 data. The LVQ method recognizes the input pattern based on the proximity of the two vectors, namely the vector of the input unit or neuron with the weight vector produced by each class. Online LVQ identification (using ECG) recorded from 25 direct trials resulted in 80% accuracy.
Alat Keamanan Pintu Menggunakan E-KTP, Modul RFID dan AWS EC2 berbasis NODEMCU ESP8266 Verdian Ramdhani; Rahmat Hidayat; Hendrick
JITSI : Jurnal Ilmiah Teknologi Sistem Informasi Vol 3 No 1 (2022)
Publisher : SOTVI - Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62527/jitsi.3.1.60

Abstract

The current door locking sistem still uses conventional keys, making it less efficient for homes with many doors because there are too many keys to carry. In addition, conventional locks are easily opened by thieves and also locks can be duplicated by irresponsible people. So that a more practical and efficient lock is needed, from this problem the author has the idea to produce a safe and practical RFID-based door security device. By utilizing E-KTP as an RFID tag as a security for the door of the house. Design and build a house door security using the NodeMCU ESP8266 microcontroller as a circuit controller. This study uses the Research and Development method, which is a method that aims to produce or develop certain products. Based on the test results, it can be concluded that the simulation of the door safety device can operate properly, according to the design made. The RFID reader used has a frequency of 13.56 Mhz which is placed on the door to read the E-KTP ID and sent to the AWS server with AWS EC2 as the website infrastructure to add other information and be stored in the MySQL database. If the ID is not in the database, Led The red light will light up and the buzzer will sound. If the ID is in the database, the servo motor will pull the door latch The yellow LED will light up, and will close the door again within 10 seconds.
Klasifikasi Kualitas Mutu Daun Gambir Ladang Rakyat Menggunakan Metode Convolutional Neural Network Teddy Winanda; Y Yuhandri; H Hendrick
Jurnal Sistim Informasi dan Teknologi 2021, Vol. 3, No. 3
Publisher : Rektorat Universitas Putra Indonesia YPTK Padang

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (814.536 KB) | DOI: 10.37034/jsisfotek.v3i3.51

Abstract

Indonesia is one of the countries which have the best Gambier quality in the world. Those are a few areas in Indonesia which have best gambier quality such as Aceh, Riau, North Sumatera, Bengkulu, South Sumatera and West Sumatra. Kabupaten 50 Kota is one of the regencies in west Sumatra that supplies gambier in Indonesia. The gambier leaf selection is mostly done by manual inspection or conventional method. The leaf color, thickness and structure are the important parameters in selecting gambier leaf quality. Farmers usually classify the quality of gambier leaves into good and bad. Computer Vision can help farmers to classify gambier leaves automatically. To realize this proposed method, gambier leaves are collected to create a dataset for training and testing processes. The gambier image leaves is captured by using DLSR camera at Kabupaten 50 Koto manually. 60 images were collected in this research which separated into 30 images with good and 30 images with bad quality. Furthermore, the gambier leaves image is processed by using digital image processing and coded by using python programming language. Both TensorFlow and Keras were implemented as frameworks in this research. To get a faster processing time, Ubuntu 18.04 Linux is selected as an operating system. Convolutional Neural Network (CNN) is the basis of image classification and object detection. In this research, the miniVGGNet architecture was used to perform the model creation. A quantity of dataset images was increased by applying data augmentation methods. The result of image augmentation for good quality gambier produced 3000 images. The same method was applied to poor quality images, the same results were obtained as many as 3000 images, with a total of 6000 images. The classification of gambier leaves produced by the Convolutional Neural Network method using miniVGGNet architecture obtained an accuracy rate of 0.979 or 98%. This method can be used to classify the quality of Gambier leaves very well.
Pembuatan Alat Inspeksi Visual Jalur PCB Menggunakan Pengolahan Citra Untuk Kegiatan Praktikum Pengawatan Dan Teknologi PCB Rangga Ade Julianto; Efrizon Efrizon; Hendrick Hendrick; Laxsmy Devy; Suryadi Suryadi; Yul Antonisfia
Elektron : Jurnal Ilmiah Volume 14 Nomor 2 Tahun 2022
Publisher : Teknik Elektro Politeknik Negeri Padang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30630/eji.14.2.295

Abstract

PCBs are very influential on the manufacture of electronic devices, for example when there is even a small number of PCB paths that are cut off or damaged, the electronic device cannot be operated properly. Therefore, in this study, the author tried to create and analyze a defect checking tool on PCBs to replace human vision to make it easier and can save costs. This tool is equipped with the help of a Logitech c920 Webcam and a Raspberry Pi 3b+ microprocessor which is used to store and run programs that have been created on Python programming software, so this tool can be used portablely. With these two technologies, Image Processing can be used to detect objects with the OpenCv library and Google Colab. PCB defect detection tool with the help of Image Processing uses yolo convolutional neural network method to help determine path damage on the PCB. You Only Look Once (YOLO) algorithm with five detection classifications, namely short, open circuit, missing hole, mouse bite, and spur. From the results of the study, the results were obtained that the YOLO algorithm was able to detect these five classifications with a value of mAP@0.5 short 90.67%, open circuit 97.86%, Mouse Bite 94.43%, Missing Hole 96.09%, and spur 97.56%.
Electric vehicle power monitoring system based on IoT sensing architecture Jia-Syuan Lin; Zi-Fang Tsai; Zhi-Hao Wang; Hendrick
International ABEC Vol. 2 (2022): Proceeding International Applied Business and Engineering Conference 2022
Publisher : International ABEC

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (705.455 KB)

Abstract

At present, most of the vehicles in life are mainly based on gasoline and diesel, and with the gradual advancement of vehicle technology, but also accelerating global warming, countries have launched a variety of green energy to reduce the harm to the earth. Many vehicles have been developed into hybrid vehicles, and a large number of pure electric vehicles have been launched in recent years. On the other hand, many countries have begun to plan carbon footprint and carbon allowance management, and the emission of greenhouse gases or the use of green energy will be more regulated in the future. Therefore, this research will take the electric stacker as the test target, measure the values of current and voltage, input the measured values into the program to calculate the carbon emissions produced during operation, and obtain the carbon emissions emitted by driving electric vehicles that are significantly smaller than the carbon emissions emitted by driving gasoline and diesel vehicles.
Driving Physiological State Monitoring Based on IoT Sensing Architecture Yi-Ching Kuo; Yu-lian Yu; Zhi-Hao Wang; Hendrick
International ABEC Vol. 2 (2022): Proceeding International Applied Business and Engineering Conference 2022
Publisher : International ABEC

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (1033.055 KB)

Abstract

In clinical practice, alcoholic beverages will have imaging effects on the autonomic nervous system. Common reactions of the human body after absorbing alcohol include unsteady walking, rapid heartbeat, and reddening of the face. In this case, humans are usually unable to fully rely on self-consciousness to manipulate the body, and consciousness tends to become blurred. In recent years, the incidents of drinking and driving have emerged in an endless stream. Although there are laws and regulations, they cannot effectively prevent and control drunk driving. Therefore, this study intends to develop an alcohol lock that can monitor the physiological state of driving. The architecture proposed in this study uses the pulse oximeter to obtain the PPG signal and then analyzes the autonomic nervous system and uses the MQ-3 alcohol sensor to detect the air alcohol content in the cockpit. The two signals are sensed by ESP32 and sent to the base station outside the car by LoRa through the IoT architecture. Finally, the driving physiological information will be sent to the server for centralized display
Monitoring Pesticide Watering On Plants Shallot Using Mosquitto Network (MQTT) Client-Server Network Muhammad Yaafi Melindra; Hendrick Hendrick; Nadia Alfitri; Rahmat Rahmat
JECCOM: International Journal of Electronics Engineering and Applied Science Vol. 1 No. 2 (2023)
Publisher : Politeknik Negeri Padang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30642/jeccom.1.2.41-46.2023

Abstract

Shallots are one of the leading commodities in several regions in Indonesia. In the cultivation of shallot plants there are pests that can reduce the quality of shallots. Along with the times, and to overcome the problem of watering pesticides for shallot plants, an automation tool is designed to make it easier for farmers to water pesticides. The design of this tool uses the concept of Internet of Thing (IoT) where the application used is Node-RED with the MQTT protocol. The optimum humidity for the growth and development of shallot plants is 50-70%, so soil moisture sensors are used to measure soil moisture. Pesticide watering also looks at weather conditions such as rain. Therefore, the raindrop sensor is used to detect rain. From several trials that have been carried out, it produces one experimental result that shows soil moisture of 35% where weather conditions are not raining and, the relay is active to perform pesticide watering. This indicates that the pesticide watering of shallot plants using the MQTT client-server network is successful carried out.
Pendeteksian Aroma Ganja Kering Menggunakan Algoritma Random Forest Ulul Azmi; Hendrick; Humaira
JITSI : Jurnal Ilmiah Teknologi Sistem Informasi Vol 4 No 1 (2023)
Publisher : SOTVI - Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62527/jitsi.4.1.104

Abstract

Ganja (Cannabis Sativa) sebagai jenis narkoba terbanyak dikonsumsi dengan persentase sebesar 56,7% dimana penyalahgunaan ganja menimbulkan banyak dampak negatif salah satunya penurunan kerja otak sehingga akan mengalami reaksi halusinasi, ilusi, gangguan berpikir, perubahan perasaan secara tiba – tiba dan menimbulkan efek kecanduan. Tingginya kasus menimbulkan beberapa kendala seperti pengadaan alat yang mahal dalam proses pencarian. Maka dirancanglah sebuah alat E-Nose untuk mendeteksi ganja yang mana sistem ini akan meniru fungsi dari indera penciuman (hidung) pada manusia. Pada penilitian ini akan melakukan pengolahan data ganja yang telah terkumpul untuk menentukan model pendeteksian ganja dengan membandingkan dua buah metode klasifikasi yaitu algoritma Random Forest dan Decision Tree untuk menentukan klasifikasi yang terbaik dalam menghasilkan akurasi pengklasifikasian model untuk mendeteksi ganja. Hasil akurasi model yang diperoleh dari algoritma Random Forest adalah sebesar 100% dimana model yang dihasilkan dapat mengklasifikasikan secara benar sampel ganja kering dan bukan ganja. Pada algoritma Decision Tree menghasilkan akurasi sebanyak 70% secara benar pada ganja kering.
Co-Authors - Andrizal - Suryadi Anna Yudanur ivan Finiel Hotmartua Bagariang Jia-Syuan Lin Laxsmy Devy Lifwarda Lifwarda Maulana Maulana Muhammad Yaafi Melindra Nadia Alfitri Rahmat Hidayat Rahmat Rahmat Rangga Ade Julianto Ratna Aisuwarya Rivanol Chadry Teddy Winanda Ulul Azmi Verdian Ramdhani Yi-Ching Kuo Yu-lian Yu Yuhandri Yuhandri Yul Antonisfia Zhi-Hao Wang Zi-Fang Tsai