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All Journal Elektron Jurnal Ilmiah Journal of Information Technology and Computer Engineering JECCOM
Hendrick Hendrick
Electronic Engineering Study Program, Electrical Engineering Department, State Polytechnic of Padang
Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Engineering

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Parking Identification System with Integration of Optical Character Recognition (OCR) and Radio Frequency Identification (RFID) Raihan, Raihan Fadhil; Hendrick, Hendrick; Novi, Novi; -Jiun Horn, Gwo
JECCOM: International Journal of Electronics Engineering and Applied Science Vol. 2 No. 1 (2024)
Publisher : Politeknik Negeri Padang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30630/jeccom.2.1.10-17.2024

Abstract

The Indonesian National Police (Polri) reported a significant increase in the number of crime cases during the period January - April 2023. Based on these conditions, an innovative security system is needed that can reduce the risk of motor vehicle theft. This tool is equipped with the help of an IP Camera and runs a program that has been made on programming software using OpenCV Python. The IP Camera is used to capture video that can be accessed on the server to process whether there is a license plate number detected and read the vehicle license plate number. This system integrates Radio Frequency Identification (RFID) and Optical Character Recognition (OCR) technology in the parking system. RFID technology will provide unique identification to each user who will enter the parking area, while OCR is used to read the license plate numbers of vehicles entering the parking area. OCR reading results are also sent to the microcontroller to be displayed on the LCD when the customer enters the parking area. The OCR reading results have a truth accuracy rate of 98.7%.
Rancang Bangun Instrumentasi Elektrokardiograf (EKG) dan Klasifikasi Kenormalan Jantung Pada Pola Sinyal EKG Menggunakan Learning Vector Quantization (LVQ) Maulana, Maulana; Hendrick, Hendrick; Aisuwarya, Ratna
JITCE (Journal of Information Technology and Computer Engineering) Vol. 2 No. 01 (2018)
Publisher : Universitas Andalas

Show Abstract | Download Original | Original Source | Check in Google Scholar | 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.
Segmentasi Area Perkebunan Sawit Melalui Aerial Images Menggunakan Deep Learning Novi, Novi; Hendrick, Hendrick; Rohfadli, Muhammad; Novira, Aulia
Elektron : Jurnal Ilmiah Vol 16 No 2 (2024)
Publisher : Teknik Elektro Politeknik Negeri Padang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30630/eji.16.2.544

Abstract

Pemantauan lahan sawit secara konvensional biasanya dilakukan dengan cara manual oleh petani yang mengerahkan beberapa orang untuk menyebar di area lahan. Namun, pendekatan ini membutuhkan waktu dan tenaga yang cukup besar, serta rentan terhadap ketidakakuratan dalam pemantauan. Sebagai alternatif, perusahaan besar yang mengelola lahan sawit umumnya menggunakan teknologi drone untuk memantau lahan, diikuti dengan penggunaan perangkat lunak analisis yang kompleks. Namun, pemanfaatan teknologi ini seringkali memerlukan biaya dan peralatan yang mahal. Penelitian ini bertujuan untuk mengembangkan sistem pemantauan lahan sawit menggunakan teknologi yang lebih terjangkau dan praktis, yaitu dengan memanfaatkan drone yang tersedia di pasaran serta NVIDIA Jetson Nano sebagai perangkat pemrosesan gambar portabel. Sistem ini menggunakan metode deep learning, dengan mengimplementasikan algoritma You Only Look Once (YOLO) untuk deteksi objek dan Instance Segmentation untuk segmentasi area lahan sawit. YOLO memungkinkan pendeteksian objek secara real-time dengan akurasi tinggi, sementara Instance Segmentation memfasilitasi pemisahan area sawit secara lebih detail, yang akan membantu dalam analisis lebih mendalam. Dengan menggunakan peralatan yang lebih terjangkau dan portabel, penelitian ini bertujuan untuk mempermudah petani atau pihak terkait dalam memantau dan menganalisis kondisi lahan sawit secara efektif, efisien, dan dengan biaya yang lebih rendah dibandingkan dengan teknologi pemantauan konvensional atau yang digunakan perusahaan besar.
Co-Authors -Jiun Horn, Gwo Maulana Maulana, Maulana Novi Novi Novira, Aulia Raihan, Raihan Fadhil Ratna Aisuwarya Rohfadli, Muhammad