DOI: https://doi.org/10.14710/jtsiskom.2023.14466

Implementasi Algoritma Pengolahan Citra dan Algoritma Jaringan Syaraf Tiruan pada Prototipe Mobil Otonom Berbasis Raspberry Pi

1Department of Computer Engineering, Universitas Wiralodra Indramayu, Indonesia

2Prodi Teknik Komputer Universitas Wiralodra, Jl. Ir. Juanda KM.03, Karanganyar Indramayu, 45213, Indonesia., Indonesia

Received: 30 Apr 2022; Published: 24 Sep 2024.
Open Access Copyright (c) 2024 Muhamad Dandi, Muh Pauzan
Creative Commons License This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.

Citation Format:
Abstract
Telah tercipta prototipe mobil otonom berbasis raspberry pi dengan sistem autopilot hasil dari penerapan dua algoritma berbeda yaitu pengolahan cita dan jaringan syaraf tiruan. Penelitian ini bertujuan untuk implementasi kedua algoritma tersebut. Implementasi pengolahan citra diawali dengan capture video yang kemudian dilatih dengan algoritma pengolahan citra sehingga diperolah nilai kurva saat deteksi jalur. Sedangkan Implementasi jaringan syaraf tiruan diawali dengan mencari data collection dari capture citra yang kemudian dilatih dengan algoritma jaringan syaraf tiruan sehingga menghasilkan model data untuk ditanamkan pada mobil otonom. Berdasarkan pengujian yang dilakukan pada jalur lintasan yang sama didapatkan persentase keberhasilan pada pengolahan citra 70% sukses. sedangkan jaringan syaraf tiruan 97% sukses. Beberapa kegagalan pengolahan citra disebabkan oleh kondisi pencahayaan saat pengujian yang berbeda dari data awal. Sedangkan jaringan syaraf tiruan dipengaruhi dari jumlah data yang dilatih, apabila semakin banyak maka semakin besar persentase keberhasilannya, hal tersebut menjadikan jaringan syaraf tiruan lebih unggul dari pengolahan citra

Note: This article has supplementary file(s).

Fulltext |  File Word Submited Journal
Implementation of image processing algorithms and neural network algorithms on an autonomous car prototype based on raspberry pi
Subject autonomous car, Raspberry Pi; image processing; neural network; training.
Type File Word Submited Journal
  Download (2MB)    Indexing metadata
Email colleagues
Keywords: mobil otonom; raspberry pi; pengolahan citra; jaringan syaraf tiruan; latihan.

Article Metrics:

Article Info
Section: Original Research Articles
Language : ID
Most viewed articles
Expert System for Diagnosis of Plant Pest and Disease Horticulture with Forward and Backward Chaining Inference Implementasi Algoritma Kriptografi RSA untuk Enkripsi dan Dekripsi Email Pembuatan Aplikasi Antar-Jemput Laundry Berbasis Web Service pada Platform Android Data Mining using Apriori Algorithm for Product Recommendation for Customers Pembuatan Sistem Informasi Rental Mobil dengan Menggunakan Java dan Mysql More articles
Most cited articles
Speed Bump Enforcement System Based on Vehicle Speed Classified by Haar Cascade Classifier Performance comparison of RSA and AES to SMS messages compression using Huffman algorithm Perancangan dan Pengembangan Permainan “Super Sigi” Menggunakan Stencyl Sebagai Media Pengenalan Menyikat Gigi Mobile Application of Expert System for Meningitis Early Diagnosis Prediction of Call Drops in GSM Network using Artificial Neural Network More cited articles
  1. N. K. Enggarsasi, Umi. Sa’diyah, “Kajian Terhadap Faktor-Faktor Kecelakaan Lalu Lintas,” Perspektif, vol. 22, no. 3, pp. 228–237, 2017
  2. Q. Li, J. Zhong, S. Zhong, Z. Chen, S. Qi and H. Zhang, "An self-focusing imaging method for leukocyte recognition," 2017 10th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics (CISP-BMEI), 2017, pp. 1-5, doi: 10.1109/CISP-BMEI.2017.8301994
  3. Alfita, R., Ibadillah, A. F., & Prianto, A. (2022). Identifikasi Nilai Nominal Uang Kertas Berdasarkan Warna Berbasis Image Processing Menggunakan Metode Template Matching. Jurnal Teknik Elektro dan Komputer TRIAC, 9(1), 28-32
  4. Febrian, W. D., Rusdinar, A., & Wibowo, A. S. (2019). Sistem Pengolahan Citra Pendeteksi Jalur Pada Mobil Listrik Otonom. eProceedings of Engineering, 6(1)
  5. Shaun Fernandes, Dhruv Duseja, and R. Muthalagu, “Application of Image Processing Techniques for Autonomous Car,” Proc. Eng. Technol. Innov., vol. 17 , pp. 1–12, 2021, doi: 10.46604/peti.2021.6074
  6. I. Ahmad and K. Pothuganti, “Design implementation of real time autonomous car by using image processing IoT,” Proc. 3rd Int. Conf. Smart Syst. Inven. Technol. ICSSIT 2020, no. Icssit, pp. 107–113, 2020, doi: 10.1109/ICSSIT48917.2020.9214125
  7. J. -H. Kim, B. -G. Kim, P. P. Roy and D. -M. Jeong, "Efficient Facial Expression Recognition Algorithm Based on Hierarchical Deep Neural Network Structure," in IEEE Access, vol. 7, pp. 41273-41285, 2019, doi: 10.1109/ACCESS.2019.2907327
  8. Wan, X., Jin, Z., Wu, H., Liu, J., Zhu, B., & Xie, H. (2020). Heartbeat classification algorithm based on one-dimensional convolution neural network. Journal of Mechanics in Medicine and Biology, 20(07), 2050046
  9. L. R. Manangka, H. Suprijono, and D. Nurcipto, “Pengenalan Pola Lintasan Berbasis Neural Network Pada Prototype Self-Driving Car,” Elektrika, vol. 12, no. 2, p. 67, 2020, doi: 10.26623/elektrika.v12i2.2732
  10. A. K. Jain, "Working model of Self-driving car using Convolutional Neural Network, Raspberry Pi and Arduino," 2018 Second International Conference on Electronics, Communication and Aerospace Technology (ICECA), 2018, pp. 1630-1635, doi: 10.1109/ICECA.2018.8474620
  11. T. Do, M. Duong, Q. Dang and M. Le, "Real-Time Self-Driving Car Navigation Using Deep Neural Network," 2018 4th International Conference on Green Technology and Sustainable Development (GTSD), 2018, pp. 7-12, doi: 10.1109/GTSD.2018.8595590
  12. K. N. V Satyanarayana, B. Tapasvi, P. Kanakaraju, and G. Rameshbabu, “Based on Machine Learning Autonomous car Using Raspberry-Pi .,” Int J. Eng. Res. Appl., vol. 7, no. 12, pp. 76–82, 2017, doi: 10.9790/9622-0712057682
  13. W. Vijitkunsawat and P. Chantngarm, "Comparison of Machine Learning Algorithm’s on Self-Driving Car Navigation using Nvidia Jetson Nano," 2020 17th International Conference on Electrical Engineering /Electronics, Computer, Telecommunications and Information Technology (ECTI-CON), 2020, pp. 201-204, doi: 10.1109/ECTI-CON49241.2020.9158311
  14. T. Okuyama, T. Gonsalves, and J. Upadhay, “Autonomous Driving System based on Deep Q Learnig,” 2018 Int. Conf. Intell. Auton. Syst. ICoIAS 2018, pp. 201–205, 2018, doi: 10.1109/ICoIAS.2018.8494053
  15. J. Liu, "Survey of the Image Recognition Based on Deep Learning Network for Autonomous Driving Car" 2020 5th International Conference on Information Science, Computer Technology and Transportation (ISCTT), 2020, pp. 1-6, doi: 10.1109/ISCTT51595.2020.00007
  16. G. M. Gandhi and Salvi, “Artificial Intelligence Integrated Blockchain for Training Autonomous Cars” 5th Int. Conf. Sci. Technol. Eng. Math. ICONSTEM 2019, vol. 1, pp. 157–161, 2019, doi: 10.1109/ICONSTEM.2019.8918795
  17. T. Bergestrom, Ossian. Wernersson, Development and Implementation of Remote Steering. Department Of Mechanics And Maritime Sciences, 2021
  18. T. C. A.-S. Zulkhaidi, E. Maria, and Y. Yulianto, “Pengenalan Pola Bentuk Wajah dengan OpenCV,” J. Rekayasa Teknol. Inf., vol. 3, no. 2, p. 181, 2020, doi: 10.30872/jurti.v3i2.4033
  19. M. T. Susitanto, S. Sakinah, and M. A. Haris, “Implementasi face detection dan recognition menggunakan python dengan numpy dan opencv menggunakan metode haar-cascade dan lbph (local binary pattern histogram),” J. Tek., vol. 14, no. 1, pp. 97–102, 2021
  20. M. Vagizov, A. Potapov, K. Konzhgoladze, S. Stepanov, and I. Martyn, “Prepare and analyze taxation data using the Python Pandas library,” IOP Conf. Ser. Earth Environ. Sci., vol. 876, no. 1, 2021, doi: 10.1088/1755-1315/876/1/012078
  21. D. J. P. Manajang, A. Jacobus, J. T. Elektro, U. Sam, and R. Manado, “Implementasi Framework Tensorflow Object Detection API Dalam Mengklasifikasi Jenis Kendaraan Bermotor,” J. Tek. Inform., vol. 15, no. 3, pp. 171–178, 2020
  22. S. G. Babić, K. Cetina. "Processing and Visuali-zation of Collected Data Based on Open-Source Tools and Principles," Convention on Information, Communication and Electronic Technology (MIPRO), pp. 1736-1739, 2020
  23. B. Shkanov, A. Zolotova, M. Alexandrov and O. Koshulko, "Express Diagnosis of COVID-19 on Cough Audiograms with Machine Learning Algorithms from Scikit-learn library and GMDH Shell tool," Conference on Computer Sciences and Information Technologies (CSIT), pp. 410-414, 2021
  24. Jung. A, "Dokumentasi Imgaug," Baca the-docs. io , Juni , 25 ,2019
  25. S. Lade, P. Shrivastav, S. Waghmare, S. Hon, S. Waghmode, and S. Teli, “Simulation of self driving car using deep learning,” Emerg. Smart Comput. Informatics, ESCI, pp. 175–180, 2021, doi: 10.1109/ESCI50559.2021.9396941

Last update:

No citation recorded.

Last update: 2024-09-26 20:57:06

No citation recorded.