Identifikasi Kecacatan Pada Jalur Rel Kereta Api Berbasis Pengolahan Citra Menggunakan Convolutional Neural Network
DOI:
https://doi.org/10.34818/eoe.v8i6.17064Abstrak
Abstrak Keselamatan dalam menggunakan transportasi adalah hal yang sangat penting, begitu juga dengan menggunakan kereta api sebagai moda transportasi massal. Salah satu bagian penting dalam keselamatan perjalanan kereta api adalah kondisi jalur rel kereta api yang baik. Namun, kecelakaan kereta api akibat anjlokan masih saja terjadi di Indonesia, maka dari itu diperlukan pengawasan prasarana jalur rel kereta api. Hanya saja, pengawasan prasarana jalur rel kereta api masih menggunakan sistem manual. Penelitian Tugas Akhir ini bertujuan untuk membuat suatu sistem yang dapat digunakan untuk mendeteksi dan mengklasifikasikan kerusakan pada jalur rel kereta api dengan menggunakan metode Convolutional Neural Network (CNN). Klasifikasi dibagi menjadi 2 kelas, yaitu defective dan non-defective. Skenario pengujian pada Tugas Akhir ini menggunakan sejumlah 384 citra. Performansi sistem akan diukur dengan menganalisis nilai parameter resize, learning rate, dan batch size, dengan hasil output berupa akurasi, presisi, recall, f1-score. Didapatkan hasil terbaik dengan nilai akurasi 80%, untuk defective didapatkan nilai presisi 81%, recall 74%, dan f1-score 77%, untuk Non-Defective didapatkan nilai presisi 80%, recall 85%, f1-score 83%. Kata kunci: Rel Kereta Api, Convolutional Neural Network (CNN), Defective, Non-Defective Abstract Safety in using transportation is very important, as is using trains as a mode of transportation. One of the important parts in the safety of train travel is the condition of a good railroad track. However, train accidents due to derailment still happen in Indonesia, therefore it is necessary to monitor the railway infrastructure. However, the supervision of railroad infrastructure still uses a manual system. This Final Task aims to create a system that can be used to detect and classify damage to railroads using the Convolutional Neural Network (CNN) method. Classification are divided into 2 classes, namely defective and non-defective. The test scenario in this final project uses a number of 384 images. System performance will be measured by analyzing the parameter values of resize, learning rate, and batch size, with the output results of accuracy, precision, recall, f1-score. The best results are obtained with an accuracy value of 80%, for defective the precision value is 81%, recall is 74%, and F1-score is 77%, for Non-Defective the precision value is 80%, recall is 85%, F1-score is 83%. Keywords: Railroad, Convolutional Neural Network (CNN), Defective, Non-DefectiveReferensi
[1] Buku Statistik Bidang Perkeretaapian, 2019.
[2] Ashish James, Wang Jie, Yang Xulei, Ye Chenghao, Nguyen Bao Ngan, Lou Yuxin, Su
Yi, Vijay Chandrasekhar, and Zeng Zeng,” TrackNet – A Deep Learning Based Fault
Detection for Railway Track Inspection”, IEEE. 2018.
[3] Peraturan Menteri Perhubungan Republik Indonesia Nomor PM 60 Tahun 2020 Tentang
Pengawasan Prasarana dan Sarana Perkeretaapian Berbasis Teknologi Informasi.
[4] Admin. (2013, Februari 2014). Komponen Penyusun Rel Kereta Api. Retrieved From
https://www.ilmutekniksipil.com/teknik-lalu-lintas/komponen-penyusun-rel-kereta-api.
[5] PJKA. 1986. Perencaan Konstruksi Jalan Rel (Peraturan Dinas No.10).
[6] U. O. Tartu, "Digital Image Processing" University of Tartu [Online].
Available: https://sisu.ut.ee/imageprocessing/book/1. [Diakses 25 Februari 2021].
[7] P. N. Andono, T.Sutojo and Muljono, "Representasi Citra Digital", dalam Pengolahan
Citra Digital, Semarang, Andi, 2017, p. 2.
[8] N. Nafi'iyah and S. Mujilahwati, "Format Citra", dalam Buku Ajar Citra Binarisasi Dan
Enhancment, Yogyakarta, Deepublish, Deepublish, p. 13.
[9] A. Kadir and A. Susanto, Pengolahan Citra., Yogyakarta: ANDI, 2012.
[10] Y. Heryadi and E. Irwansyah, Deep learning : Aplikasinya di Bidang Geospasial, Depok:
AWI Technology Press, 2020.
[11] M. Miftahul Huda, Algoritma Data Mining: Analisis Data Dengan Komputer, bisakimia,
[12] P. Kim, MATLAB Deep Learning With Machine Learning Neural Networks and Artificial
Intelligence, apress, 2017.
[13] Y. N. Fu'adah and I. Wijiyanto, "Automated Classification of Alzheimer's Disease Based
on MRI Image Processing using Convolutional Neural Network (CNN) with AlexNet
Architecture", 2020.
[14] S. Sakib and N. Ahmed, "An Overview of Convolutional Neural Network: Its Architecture
and Applications", IEEE,2018.
[15] R. Yamashita and M. Nishio, "Convolutional Neural Network: An Overview and
Application in Radiology", Springer, 2018.
[16] R. D. Nurfita and G. Ariyanto, "Implementasi Deep Learning Berbasis Tensorflow untuk
pengenalan sidik jari", J. Emit vol 18, no.1, pp 22-27, 2018.
[17] R. B. Arif and A. B. Siddique, "Study and Observation of the Variations of Accuracies for
Handwritten Digits Recognition with Various Hidden Layers and Epochs using
Convolutional Neural Network", IEEE, pp. 112-117, 2018.
[18] Y. N. Fu'adah and S. Sa'idah, "Computer Aided Diagnosis for Early Detection of
Glaucoma using Convolutional Neural Network (CNN)", 2020.
[19] Santra, A. K. and Christy, C. J. (2012). Genetic Algorithm and Confussion Matrix for
Document Clustering, International Journal of Computer Science Issues, 9(1), 322-328
[20] T. de Bruin, K, Verbert, and R. Babuska, "Railway Track Circuit Fault Diagnosis Using
Recurrent Neural Network", IEEE. 2016.
[21] Welankiwar, A., Sherekar, S., Bhagat, A. P., & Khodke, P. A. (2018). Fault Detection in
Railway Tracks Using Artificial Neural Networks. 2018 International Conference on
Research in Intelligent and Computing in Engineering (RICE).
