Penerapan Computer Vision Menggunakan Metode Deep Learning pada Perspektif Generasi Ulul Albab

Penulis

  • Imamul Arifin Politeknik Elektronika Negeri Surabaya
  • Reydiko Fakhran Haidi Politeknik Elektronika Negeri Surabaya
  • Muhammad Dzalhaqi Politeknik Elektronika Negeri Surabaya

DOI:

https://doi.org/10.54914/jtt.v7i2.436

Kata Kunci:

Sistem Kecerdasan Buatan, Machine Learning, Computer Vision, Deep Learning

Abstrak

Machine learning merupakan salah satu penerapan kecerdasan buatan. Penggunaan machine learning pada computer vision erat berkaitan dengan deep learning yang mana para ilmuwan komputer mendapatkan inspirasi mengenai teknologi deep learning dari alam sekitar. Tujuan penelitian pada naskah ini adalah Mengetahui dan memahami teknologi deep learning beserta contoh sederhana dalam pemrosesan objek gambar dan Mengetahui dan memahami teknologi kecerdasan buatan dalam perspektif generasi ulul albab sehingga bisa memberikan manfaat secara menyeluruh bagi dunia. Penelitian yang dilakukan pada karya tulis ini merupakan jenis penelitian kualitatif dengan metode studi pustaka (library research) menggunakan berbagai buku dan literatur bacaan lainnya seperti jurnal dan website khusus sehingga menghasilkan informasi dari topik yang diteliti. Teknologi kecerdasan buatan akan selalu berkembang dan menuju arah yang semakin canggih, tetapi teknologi juga mempunyai dampak negatif. Generasi Ulul Albab harus bisa berjuang untuk memberikan dampak positif bagi masyarakat karena sejatinya generasi ulul albab adalah harapan kemajuan peradaban islam di berbagai sektor ilmu pengetahuan dan teknologi.

Unduhan

Data unduhan belum tersedia.

Referensi

S. Bharati, T. Khan, P. Podder, and N. Hung, “A Comparative Analysis of Image Denoising Problem: Noise Models, Denoising Filters and Applications,” in Studies in Systems, Decision and Control, 2020, pp. 49–66. doi: 10.1007/978-3-030-55833-8_3.

I. N. Junejo and N. Ahmed, “A multi-branch separable convolution neural network for pedestrian attribute recognition,” Heliyon, vol. 6, no. 3, p. 2, Mar. 2020, doi: 10.1016/j.heliyon.2020.e03563.

L. Datta, “A Survey on Activation Functions and their relation with Xavier and He Normal Initialization,” arXiv:2004.06632 [cs], Mar. 2020, Accessed: Nov. 18, 2021. [Online]. Available: http://arxiv.org/abs/2004.0632

M. R. R. Budianto, S. F. Kurnia, and T. R. S. W. Galih, “Perspektif Islam Terhadap Ilmu Pengetahuan dan Teknologi,” Islamika, vol. 21, no. 01, pp. 55–61, Aug. 2021, doi: 10.32939/islamika.v21i01.776.

S. SHARMA, “Activation Functions in Neural Networks,” Medium, Jul. 04, 2021. https://towardsdatascience.com/activation-functions-neural-networks-1cbd9f8d91d6 (accessed Nov. 18, 2021).

F. Sultana, A. Sufian, and P. Dutta, “Advancements in Image Classification using Convolutional Neural Network,” 2018 Fourth International Conference on Research in Computational Intelligence and Communication Networks (ICRCICN), pp. 122–129, Nov. 2018, doi: 10.1109/ICRCICN.2018.8718718.

Al-Qur’an dan Terjemahannya, vol. 4. Al-Akhyar, 2015.

P. S, “Artificial Neural Networks - Better Understanding !,” Analytics Vidhya, Jun. 14, 2021. https://www.analyticsvidhya.com/blog/2021/06/artificial-neural-networks-better-understanding/ (accessed Nov. 18, 2021).

M. Z. Asghar, M. Abbas, K. Zeeshan, P. Kotilainen, and T. Hämäläinen, “Assessment of Deep learning Methodology for Self-Organizing 5G Networks,” Applied Sciences, vol. 9, no. 15, Art. no. 15, Jan. 2019, doi: 10.3390/app9152975.

S. Savalia and V. Emamian, “Cardiac Arrhythmia Classification by Multi-Layer Perceptron and Convolution Neural Networks,” Bioengineering (Basel), vol. 5, no. 2, p. 35, May 2018, doi: 10.3390/bioengineering5020035.

M. Pirhooshyaran and M. Yetkin, “Convolutional Neural Network (CNN): Basics and Recent Advancements,” OptML Meetings, pp. 5–31, Sep. 2019.

Y. Chen, L. Xu, K. Liu, D. Zeng, and J. Zhao, “Event Extraction via Dynamic Multi-Pooling Convolutional Neural Networks,” in Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing (Volume 1: Long Papers), Beijing, China, 2015, pp. 167–176. doi: 10.3115/v1/P15-1017.

V. Verdhan, “Image Classification Using LeNet,” in Computer vision Using Deep learning: Neural Network Architectures with Python and Keras, V. Verdhan, Ed. Berkeley, CA: Apress, 2021, pp. 67–101. doi: 10.1007/978-1-4842-6616-8_3.

W. S. Eka Putra, “Klasifikasi Citra Menggunakan Convolutional Neural Network (CNN) pada Caltech 101,” JTITS, vol. 5, no. 1, Mar. 2017, doi: 10.12962/j23373539.v5i1.15696.

A. Ng, “Machine learning - Non-linear SVM classification with kernels,” Open Classroom Standford Edu, 2017. http://openclassroom.stanford.edu/MainFolder/DocumentPage.php?course=MachineLearning&doc=exercises/ex8/ex8.html (accessed Nov. 18, 2021).

M. P. Kuchera et al., “Machine learning methods for track classification in the AT-TPC,” Nuclear Instruments and Methods in Physics Research Section A: Accelerators, Spectrometers, Detectors and Associated Equipment, vol. 940, pp. 156–167, Oct. 2019, doi: 10.1016/j.nima.2019.05.097.

L. Leong, “Machine learning Pipelines: Nonlinear Model Stacking,” Medium, Jul. 29, 2019. https://towardsdatascience.com/machine-learning-pipelines-nonlinear-model-stacking-668f2b720344 (accessed Nov. 18, 2021).

J. Nagi et al., “Max-pooling convolutional neural networks for vision-based hand gesture recognition,” Nov. 2011, pp. 342–347. doi: 10.1109/ICSIPA.2011.6144164.

N. Kang, “Multi-Layer Neural Networks with Sigmoid Function— Deep learning for Rookies (2) | by Nahua Kang | Towards Data Science,” Towards Data Science, Jun. 27, 2017. https://towardsdatascience.com/multi-layer-neural-networks-with-sigmoid-function-deep-learning-for-rookies-2-bf464f09eb7f (accessed Nov. 18, 2021).

Z. Liao and G. Carneiro, “On the Importance of Normalisation Layers in Deep learning with Piecewise Linear Activation Units,” arXiv:1508.00330 [cs], Nov. 2015, Accessed: Nov. 18, 2021. [Online]. Available: http://arxiv.org/abs/1508.00330

M. Munir, “Membingkai Kepribadian Ulul Albab Generasi Milenial,” TA’LIMUNA, vol. 7, no. 1, p. 45, Aug. 2018, doi: 10.32478/ta.v7i1.147.

C. Anam and M. Y. A. Bakar, “Pemikiran Imam Suprayogo Dalam Integrasi Ilmu Keislaman Dan Sains Berbasis Ulul Albab,” Madinah: Jurnal Studi Islam, vol. 8, no. 1, Art. no. 1, Jun. 2021.

D. Fermansah, “Penggunaan Metode Traditional Transformations Data Augmentation Untuk Peningkatan Hasil Akurasi Pada Model Algoritma Convolutional Neural Network (Cnn) Diklasifikasi Gambar,” sarjana, Universitas Siliwangi, 2019. Accessed: Nov. 18, 2021. [Online]. Available: http://repositori.unsil.ac.id/233/

K. Rana, “Pooling Layer — Beginner To Intermediate,” Medium, Apr. 20, 2020. Https://Ai.Plainenglish.Io/Pooling-Layer-Beginner-To-Intermediate-Fa0dbdce80eb (Accessed Nov. 18, 2021).

S. Ganesh, “Weights and Bias in a Neural Network | Towards Data Science,” Towards Data Science, Jul. 25, 2020. https://towardsdatascience.com/whats-the-role-of-weights-and-bias-in-a-neural-network-4cf7e9888a0f (accessed Nov. 18, 2021).

Z. HY, “Introduction to Deep learning,” Mobile Monitoring Solutions. https://mobilemonitoringsolutions.com/introduction-to-deep-learning/ (accessed Dec. 10, 2021).

R. P. Yaniawati, “Penelitian Studi Kepustakaan,” p. 31, Apr. 2020.

A. Yanuar R, “Artificial Neural Network (ANN) – Universitas Gadjah Mada Menara Ilmu Machine learning.” https://machinelearning.mipa.ugm.ac.id/2018/05/24/artificial-neural-network-ann/ (accessed Dec. 10, 2021).

“Hidden Layer,” DeepAI, May 17, 2019. https://deepai.org/machine-learning-glossary-and-terms/hidden-layer-machine-learning (accessed Dec. 10, 2021).

“Kejahatan siber, 2020,” https://lokadata.beritagar.id/, May 04, 2020. https://lokadata.beritagar.id/chart/preview/kejahatan-siber-2020-1588564923 (accessed Dec. 11, 2021).

R. Ayunda, “Perlindungan Data Nasabah Terkait Pemanfaatan Artificial Intelligence dalam Aktivitas Perbankan di Indonesia,” vol. 7, p. 15, 2021.

A. Geron, Hands-On Machine learning with Scikit-Learn, Keras, and TensorFlow, 2nd Edition, 2nd ed. O’Reilly, 2019.

R. Atienza, Advanced Deep learning with TensorFlow 2 and Keras, 2nd ed. Packt Publishing, 2020.

A. F. Gad Menoufia, Practical Computer vision Applications Using Deep learning with CNNs, 1st ed. 2018. [Online]. Available: https://doi.org/10.1007/978-1-4842-4167-7

Diterbitkan

2021-12-30

Cara Mengutip

[1]
I. . Arifin, R. F. . Haidi, dan M. . Dzalhaqi, “Penerapan Computer Vision Menggunakan Metode Deep Learning pada Perspektif Generasi Ulul Albab”, j. teknologi terpadu, vol. 7, no. 2, hlm. 98-107, Des 2021.

Terbitan

Bagian

Artikel

Artikel paling banyak dibaca berdasarkan penulis yang sama