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All Journal JOURNAL OF ELECTRICAL AND SYSTEM CONTROL ENGINEERING MIND (Multimedia Artificial Intelligent Networking Database) Journal TELKA - Telekomunikasi, Elektronika, Komputasi dan Kontrol Jurnal Teknik Informatika (JUTIF) eProceedings of Engineering Journal of Computer Engineering, Electronics and Information Technology Jurnal INFOTEL
Syamsul Rizal
Universitas Telkom
Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Engineering Industrial & Manufacturing Engineering Other

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Deteksi Pelanggaran Marka Jalan Berbasis Pengolahan Citra Menggunakan Metode Deteksi Garis Tepi Canny Dan Transformasi Hough Iqbal Muhammadin; Iwan Iwut Tritoasmoro; Syamsul Rizal
eProceedings of Engineering Vol 7, No 2 (2020): Agustus 2020
Publisher : eProceedings of Engineering

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

Pada tahun 2018, Indonesia tercatat sebagai negara peringkat ke -3 yang memiliki angka kecelakaan lalu lintas tertinggi di seluruh dunia. Salah satu faktor penyebab kecelakaan lalu lintas yaitu pelanggaran lalu lintas yang dilakukan oleh pengendara dengan tidak mengindahkan peraturan lalu lintas khususnya pada peraturan melintasi marka jalan yang terlarang. Maka dari itu, diperlukan sistem yang dapat memantau arus lalu lintas secara intensif untuk mempermudah pihak aparatur negara yang berwenang dalam membuat kebijakan berlalu lintas yang lebih baik dimasa yang akan datang. Penelitian ini menggunakan data rekaman video CCTV untuk mendeteksi pelanggaran marka jalan berbasis pengolahan citra. Pemrosesan sistem menggunakan perangkat lunak pengolah citra. Proses pengolahan citra dalam mendeteksi garis marka jalan menggunakan metode deteksi garis tepi Canny. Selain itu juga dilakukan pembandingan hasil pengujian terhadap metode deteksi garis tepi Prewitt, Roberts, dan Sobel. Metode transformasi Hough digunakan untuk membuat plotting garis lurus diatas marka jalan yang telah terdeteksi sebagai penanda pada area yang digunakan sebagai bahan pengujian. Sistem akan mendeteksi telah terjadi pelanggaran marka jalan apabila nilai piksel garis marka jalan pada citra video kurang dari nilai piksel citra background. Pengujian dilakukan dengan menggunakan 4 buah video bahan uji untuk setiap metode deteksi garis tepi. Setiap video memiliki 450 frame dengan spesifikasi durasi 15 detik dan frame rate sebesar 30 fps. Sistem mendapatkan hasil pengujian terhadap nilai akurasi dalam mendeteksi jumlah kendaraan yang melanggar marka jalan sebesar 100% untuk pengujian pada seluruh metode deteksi garis tepi yang digunakan dalam proses pengujian. Metode deteksi garis tepi yang memiliki kinerja terbaik dalam proses deteksi pelanggaran marka jalan yaitu metode deteksi garis tepi Roberts dengan nilai error rate sebesar 0 % untuk pengujian data video ke-1, 20,89 % untuk pengujian data video ke-2, 41,78 % untuk pengujian data video ke-3 dan 53,33 % untuk pengujian data video ke-4.Kata Kunci: lampu lalu lintas, suara sirene, sistem kontrol, MFCC, Euclidean Distance, Arduino.
Application of Neural Network for ECG-based Biometrics System Using QRS Features Ana Rahma Yuniarti; Syamsul Rizal; Ferdinand Aprillian Manurung
Journal of Computer Engineering, Electronics and Information Technology Vol 1, No 2 (2022): COELITE: Volume 1, Issue 2, 2022
Publisher : Universitas Pendidikan Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (780.059 KB) | DOI: 10.17509/coelite.v1i2.43823

Abstract

Applications of Biometrics technology are extremely popular today, ranging from access control to automation. Fingerprint is the oldest and the most widely used biometrics technology. However, its key features are externally exposed which make it tend to be easily forged. This study investigates the possibility of electrocardiogram (ECG) signal as an alternative modality for biometrics systems. Besides that, the study is conducted using the ECG database under arrhythmia conditions to accommodate the real-world application since arrhythmia exists in large-scale world populations. In this study, a total of 8,972 datasets from 47 subjects were modeled using a machine learning technique (i.e., one-dimensional convolution neural network or 1-D CNN). The results showed that the accuracy (F1-score) of 92% and 0.25 of loss was achieved. Furthermore, we prove that the proposed model is a good fitting based on the visualization plot of the train-test. These findings show that the proposed model is reasonable enough for an ECG-based biometrics system though it's not the best in the literature.
Construction of cardiac arrhythmia prediction model using deep learning and gradient boosting Dhanar Bintang Pratama; Favian Dewanta; Syamsul Rizal
JURNAL INFOTEL Vol 13 No 3 (2021): August 2021
Publisher : LPPM INSTITUT TEKNOLOGI TELKOM PURWOKERTO

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20895/infotel.v13i3.683

Abstract

Arrhythmia is a condition in which the rhythm of heartbeat becomes irregular. This condition in extreme cases can lead to fatal heart attack accidents. In order to reduce heart attack risk, appropriate early treatments should be conducted right after getting results of Arrhythmia condition, which is generated by electrocardiography ECG tools. However, reading ECG results should be done by qualified medical staff in order to diagnose the existence of arrhythmia accurately. This paper proposes a deep learning algorithm method to classify and detect the existence of arrhythmia from ECG reading. Our proposed method relies on Convolutional Neural Network (CNN) to extract feature from a single lead ECG signal and also Gradient Boosting algorithm to predict the final outcome of single lead ECG reading. This method achieved the accuracy of 96.18% and minimized the number of parameters used in CNN Layer.
Co-Authors Achmad Rizal Ana Rahma Yuniarti Dhanar Bintang Pratama Dieny Rofiatul Mardiyah Fajar Kurniawan Alhamal FAUZI FRAHMA TALININGSIH Favian Dewanta Ferdinand Aprillian Manurung Gelar Budiman Hilman Fauzi, Hilman Hurianti Vidya Iqbal Muhammadin Iwan Iwut Tritoasmoro Jangkung Raharjo Ledya Novamizanti Mochamad Dandi Muhamad Rokhmat Isnaini MUHAMMAD ADNAN PRAMUDITO Nor Kumalasari Caecar Pratiwi Novelita Dwi Miranda Nugraeni Kholifaturrofiah Nur Ibrahim R. Yunendah Nur Fu’adah Rendi Bagus Oklanri Rita Magdalena SOFIA SAIDAH Utari Nur Ramadhani Yora