DEEP LEARNING : REVIEW DAN IMPLEMENTASI

Authors

  • Aditya Cahyadi Universitas Negeri Malang, Jl. Semarang No.5, Sumbersari, Kota Malang, Jawa Timur 65145
  • Artiko Fajar Universitas Negeri Malang, Jl. Semarang No.5, Sumbersari, Kota Malang, Jawa Timur 65145
  • Benaya Sastro Universitas Negeri Malang, Jl. Semarang No.5, Sumbersari, Kota Malang, Jawa Timur 65145
  • Gamma Fitrian Universitas Negeri Malang, Jl. Semarang No.5, Sumbersari, Kota Malang, Jawa Timur 65145
  • Hanif Adhilaga Universitas Negeri Malang, Jl. Semarang No.5, Sumbersari, Kota Malang, Jawa Timur 65145

Keywords:

Big data, Analisa big data, Algoritma big data, Business intelligent, Deep learning

Abstract

The abstract should be written in both English and Indonesian in one paragraph consists of maximum 250 words. The abstract should explain the purpose, method, and the result of the research concisely. An abstract should stand alone, means that no citation in the abstract. The abstract should be written in both English and Indonesian in one paragraph consists of maximum 250 words. The abstract should explain the purpose, method, and the result of the research concisely. An abstract should stand alone, means that no citation in the abstract. The abstract should be written in both English and Indonesian in one paragraph consists of maximum 250 words. The abstract should explain the purpose, method, and the result of the research concisely. An abstract should stand alone, means that no citation in the abstract. The abstract should be written in both English and Indonesian in one paragraph consists of maximum 250 words. The abstract should explain the purpose, method, and the result of the research concisely. An abstract should stand alone, means that no citation in the abstract

References

Abdillah, M., … J. N., & 2016, undefined. (2016). Using Deep Learning To Predict Customer Churn In A Mobile Telecomunication Network. … .Telkomuniversity.Ac.Id, 3(2), 3882–3888.

Ahmad, A. (2017). Mengenal Artificial Intelligence , Machine Learning , Neural Network , dan Deep Learning. June.

Ahmed, E., Jones, M., & Marks, T. K. (2015). An improved deep learning architecture for person re-identification. 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 3908–3916. https://doi.org/10.1109/CVPR.2015.7299016

Akhtar, N., & Mian, A. (2018). Threat of Adversarial Attacks on Deep Learning in Computer Vision: A Survey. 4(c). https://doi.org/10.1109/ACCESS.2018.2807385

Araque, O., Corcuera-Platas, I., Sánchez-Rada, J. F., & Iglesias, C. A. (2017). Enhancing deep learning sentiment analysis with ensemble techniques in social applications. Expert Systems with Applications, 77, 236–246. https://doi.org/10.1016/j.eswa.2017.02.002

Bar, Y., Diamant, I., Wolf, L., Lieberman, S., Konen, E., & Greenspan, H. (2015). Chest pathology detection using deep learning with non-medical training. Proceedings - International Symposium on Biomedical Imaging, 2015–July, 294–297. https://doi.org/10.1109/ISBI.2015.7163871

Bengio, Y., Courville, A., & Vincent, P. (2012). Representation Learning: A Review and New Perspectives. 1–30. https://doi.org/10.1145/1756006.1756025

Cao, L., & Fan, J. (2014). Deep Learning in Computer Vision and NLP. 1–14.

Carrio, A., Sampedro, C., Rodriguez-Ramos, A., & Campoy, P. (2017). A review of deep learning methods and applications for unmanned aerial vehicles. Journal of Sensors, 2017. https://doi.org/10.1155/2017/3296874

Chen, H., Engkvist, O., Wang, Y., Olivecrona, M., & Blaschke, T. (2018). The rise of deep learning in drug discovery. Drug Discovery Today, 00(00), 1–10. https://doi.org/10.1016/j.drudis.2018.01.039

Chotitham, S., Wongwanich, S., & Wiratchai, N. (2014). Deep Learning and its Effects on Achievement. Procedia - Social and Behavioral Sciences, 116(1), 3313–3316. https://doi.org/10.1016/j.sbspro.2014.01.754

Collobert, R., & Weston, J. (2009). Deep Learning for Natural Language Processing. Slides, 1–113. https://doi.org/10.1.1.678.8129

Conneau, A., Schwenk, H., Barrault, L., & Lecun, Y. (2016). Very Deep Convolutional Networks for Text Classification. Künstliche Intelligenz, 26(4), 357–363. https://doi.org/10.1007/s13218-012-0198-z

Costilla-Reyes, O., Scully, P., & Ozanyan, K. B. (2017). Deep Neural Networks for Learning Spatio-Temporal Features from Tomography Sensors. IEEE Transactions on Industrial Electronics, 65(1), 1–1. https://doi.org/10.1109/TIE.2017.2716907

Denil, M., Shakibi, B., Dinh, L., Ranzato, M., & de Freitas, N. (2013). Predicting Parameters in Deep Learning. 1–9.

Dimitrovski, I., Kocev, D., Kitanovski, I., Loskovska, S., & Džeroski, S. (2015). Improved medical image modality classification using a combination of visual and textual features. Computerized Medical Imaging and Graphics, 39, 14–26. https://doi.org/10.1016/j.compmedimag.2014.06.005

Elmisery, A. M., Sertovic, M., & Gupta, B. B. (2018). Cognitive Privacy Middleware for Deep Learning Mashup in Environmental IoT. IEEE Access, 6(0518798), 8029–8041. https://doi.org/10.1109/ACCESS.2017.2787422

Erdmann, M., Glombitza, J., & Walz, D. (2018). A deep learning-based reconstruction of cosmic ray-induced air showers. Astroparticle Physics, 97, 46–53. https://doi.org/10.1016/j.astropartphys.2017.10.006

Erickson, B. J., Korfiatis, P., Kline, T. L., Akkus, Z., Philbrick, K., & Weston, A. D. (2018). Deep Learning in Radiology: Does One Size Fit All? Journal of the American College of Radiology, 1–6. https://doi.org/10.1016/j.jacr.2017.12.027

Ersti, R., & Wisesty, U. N. (2016). Klasifikasi Sinyal EEG Menggunakan Deep Neural Network EEG Signal Classification using Deep Neural Network. 3(3), 5213–5220.

George, D., & Huerta, E. A. (2017). Deep Learning for Real-time Gravitational Wave Detection and Parameter Estimation: Results with Advanced LIGO Data. Physics Letters B, 778, 64–70. https://doi.org/10.1016/j.physletb.2017.12.053

Gibson, E., Li, W., Sudre, C., Fidon, L., Shakir, D. I., Wang, G., Eaton-Rosen, Z., Gray, R., Doel, T., Hu, Y., Whyntie, T., Nachev, P., Modat, M., Barratt, D. C., Ourselin, S., Cardoso, M. J., & Vercauteren, T. (2018). NiftyNet: a deep-learning platform for medical imaging. Computer Methods and Programs in Biomedicine, 158, 113–122. https://doi.org/10.1016/j.cmpb.2018.01.025

Glorot, X., Bordes, A., & Bengio, Y. (2011). Domain Adaptation for Large-Scale Sentiment Classification: A Deep Learning Approach. Proceedings of the 28th International Conference on Machine Learning, 1, 513–520.

Guimaraes, R. G., Rosa, R. L., De Gaetano, D., Rodriguez, D. Z., & Bressan, G. (2017). Age Groups Classification in Social Network Using Deep Learning. IEEE Access, 5, 10805–10816. https://doi.org/10.1109/ACCESS.2017.2706674

Guo, Y., Liu, Y., Oerlemans, A., Lao, S., Wu, S., & Lew, M. S. (2016). Deep learning for visual understanding: A review. Neurocomputing, 187, 27–48. https://doi.org/10.1016/j.neucom.2015.09.116

Hanif tnn-94-gradient.pdf. (n.d.).

Hao, Y., Khoo, H. M., von Ellenrieder, N., Zazubovits, N., & Gotman, J. (2018). DeepIED: An epileptic discharge detector for EEG-fMRI based on deep learning. NeuroImage: Clinical, 17(June 2017), 962–975. https://doi.org/10.1016/j.nicl.2017.12.005

Heinsfeld, A. S., Franco, A. R., Craddock, R. C., Buchweitz, A., & Meneguzzi, F. (2018). Identification of autism spectrum disorder using deep learning and the ABIDE dataset. NeuroImage: Clinical, 17(June 2017), 16–23. https://doi.org/10.1016/j.nicl.2017.08.017

Holden, D., Saito, J., & Komura, T. (2016). A deep learning framework for character motion synthesis and editing. ACM Transactions on Graphics, 35(4), 1–11. https://doi.org/10.1145/2897824.2925975

Huval, B., Wang, T., Tandon, S., Kiske, J., Song, W., Pazhayampallil, J., Andriluka, M., Rajpurkar, P., Migimatsu, T., Cheng-Yue, R., Mujica, F., Coates, A., & Ng, A. Y. (2015). An Empirical Evaluation of Deep Learning on Highway Driving. 1–7.

Isin, A., & Ozdalili, S. (2017). Cardiac arrhythmia detection using deep learning. Procedia Computer Science, 120, 268–275. https://doi.org/10.1016/j.procs.2017.11.238

Kang, B., & Choo, H. (2016). A deep-learning-based emergency alert system. ICT Express, 2(2), 67–70. https://doi.org/10.1016/j.icte.2016.05.001

Kvam, J., & Kongsro, J. (2017). In vivo prediction of intramuscular fat using ultrasound and deep learning. Computers and Electronics in Agriculture, 142(September), 521–523. https://doi.org/10.1016/j.compag.2017.11.020

Le, Q. V, Coates, A., Prochnow, B., & Ng, A. Y. (2011). On Optimization Methods for Deep Learning. Proceedings of The 28th International Conference on Machine Learning (ICML), 265–272. https://doi.org/10.1.1.220.8705

Lenz, I., Lee, H., & Saxena, A. (2015). Deep learning for detecting robotic grasps. International Journal of Robotics Research, 34(4–5), 705–724. https://doi.org/10.1177/0278364914549607

Liu, H., Taniguchi, T., Tanaka, Y., Takenaka, K., & Bando, T. (2017). Visualization of Driving Behavior Based on Hidden Feature Extraction by Using Deep Learning. IEEE Transactions on Intelligent Transportation Systems, 18(9), 2477–2489. https://doi.org/10.1109/TITS.2017.2649541

Liu, J., Pan, Y., Li, M., Chen, Z., Tang, L., Lu, C., & Wang, J. (2018). Applications of deep learning to MRI images: A survey. Big Data Mining and Analytics, 1(1), 1–18. https://doi.org/10.26599/BDMA.2018.9020001

Liu, W. X., Zhang, J., Liang, Z. W., Peng, L. X., & Cai, J. (2017). Content Popularity Prediction and Caching for ICN: A Deep Learning Approach with SDN. IEEE Access, 5075–5089. https://doi.org/10.1109/ACCESS.2017.2781716

Liu, W., Zhang, M., Luo, Z., & Cai, Y. (2017). An Ensemble Deep Learning Method for Vehicle Type Classification on Visual Traffic Surveillance Sensors. IEEE Access, 5, 24417–24425. https://doi.org/10.1109/ACCESS.2017.2766203

Liu, Z., Luo, P., Wang, X., & Tang, X. (2015). Deep learning face attributes in the wild. Proceedings of the IEEE International Conference on Computer Vision, 2015 Inter, 3730–3738. https://doi.org/10.1109/ICCV.2015.425

Loukas, G., Vuong, T., Heartfield, R., Sakellari, G., Yoon, Y., & Gan, D. (2017). Cloud-based cyber-physical intrusion detection for vehicles using Deep Learning. IEEE Access, PP(99), 1. https://doi.org/10.1109/ACCESS.2017.2782159

Lustberg, T., van Soest, J., Gooding, M., Peressutti, D., Aljabar, P., van der Stoep, J., van Elmpt, W., & Dekker, A. (2017). Clinical evaluation of atlas and deep learning based automatic contouring for lung cancer. Radiotherapy and Oncology. https://doi.org/10.1016/j.radonc.2017.11.012

Maimo, L. F., Gomez, A. L. P., Clemente, F. J. G., Perez, M. G., & Perez, G. M. (2018). A Self-Adaptive Deep Learning-Based System for Anomaly Detection in 5G Networks. IEEE Access, 3536(c), 1–12. https://doi.org/10.1109/ACCESS.2018.2803446

Maymin, P. Z. (2017). Wage against the machine: A generalized deep-learning market test of dataset value. International Journal of Forecasting. https://doi.org/10.1016/j.ijforecast.2017.09.008

Mehmood, R. M., Du, R., & Lee, H. Y. O. J. (2017). SPECIAL SECTION ON ADVANCES OF MULTISENSORY SERVICES AND Optimal Feature Selection and Deep Learning Ensembles Method for Emotion Recognition From Human Brain EEG Sensors. 5, 14797–14806.

Melucci, M. (2016). Relevance Feedback Algorithms Inspired by Quantum Detection. IEEE Transactions on Knowledge and Data Engineering, 28(4), 1022–1034. https://doi.org/10.1109/TKDE.2015.2507132

Min, W., Ha, E. Y., Rowe, J., Mott, B., & Lester, J. (2014). Deep Learning-Based Goal Recognition in Open-Ended Digital Games. Proceedings of the 10th AAAI Conference on Artificial Intelligence and Interactive Digital Entertainment, 37–43.

Mohsen, H., El-Dahshan, E.-S. A., El-Horbaty, E.-S. M., & Salem, A.-B. M. (2017). Classification using Deep Learning Neural Networks for Brain Tumors. Future Computing and Informatics Journal. https://doi.org/10.1016/j.fcij.2017.12.001

Muhammad, I., & Novia Wisesty, U. (2017). Klasifikasi Sinyal Ecg Menggunakan Deep Learning Dengan Stacked Denoising Autoencoders Ecg Signal Classification Using Deep Learning With Stacked Denoising Autoencoders. 4(3), 4719–4724.

Nguyen, K., Fookes, C., Ross, A., & Sridharan, S. (2017). Iris Recognition with Off-the-Shelf CNN Features: A Deep Learning Perspective. IEEE Access, 1–9. https://doi.org/10.1109/ACCESS.2017.2784352

Nguyen, N. D., Nguyen, T., & Nahavandi, S. (2017). System Design Perspective for Human-Level Agents Using Deep Reinforcement Learning: A Survey. IEEE Access, PP(99), 1. https://doi.org/10.1109/ACCESS.2017.2777827

Ouyang, W., Chu, X., & Wang, X. (2014). Multi-source Deep Learning for Human Pose Estimation. 2014 IEEE Conference on Computer Vision and Pattern Recognition, 2337–2344. https://doi.org/10.1109/CVPR.2014.299

Ouyang, W., & Wang, X. (2013). Joint deep learning for pedestrian detection. Proceedings of the IEEE International Conference on Computer Vision, 2056–2063. https://doi.org/10.1109/ICCV.2013.257

Ozbolat, I. T. (2017). Introduction. 3D Bioprinting, 18(1), 1–12. https://doi.org/10.1016/B978-0-12-803010-3.00001-9

Particke, F., Kolbenschlag, R., Hiller, M., Patiño-Studencki, L., & Thielecke, J. (2017). Deep Learning for Real-Time Capable Object Detection and Localization on Mobile Platforms. IOP Conference Series: Materials Science and Engineering, 261, 012005. https://doi.org/10.1088/1757-899X/261/1/012005

Pei, K., Cao, Y., Yang, J., & Jana, S. (2017). DeepXplore: Automated Whitebox Testing of Deep Learning Systems. https://doi.org/10.1145/3132747.3132785

Putra, I. W. S. E. (2016). Klasifikasi Citra Menggunakan Convolutional Neural Network (Cnn) Pada Caltech 101. Jurnal Teknik ITS, 5(1), 65–69.

Pyakillya, B., Kazachenko, N., & Mikhailovsky, N. (2017). Deep Learning for ECG Classification. Journal of Physics: Conference Series, 913(1). https://doi.org/10.1088/1742-6596/913/1/012004

Ravi, D., Wong, C., Deligianni, F., Berthelot, M., Andreu-Perez, J., Lo, B., & Yang, G. Z. (2017). Deep Learning for Health Informatics. IEEE Journal of Biomedical and Health Informatics, 21(1), 4–21. https://doi.org/10.1109/JBHI.2016.2636665

Ravi, D., Wong, C., Lo, B., & Yang, G.-Z. (2017). A Deep Learning Approach to on-Node Sensor Data Analytics for Mobile or Wearable Devices. IEEE Journal of Biomedical and Health Informatics, 21(1), 56–64. https://doi.org/10.1109/JBHI.2016.2633287

Rere, L. M. R., Fanany, M. I., & Arymurthy, A. M. (2015). Simulated Annealing Algorithm for Deep Learning. Procedia Computer Science, 72, 137–144. https://doi.org/10.1016/j.procs.2015.12.114

Sallab, A. A. Al, Baly, R., & Hajj, H. (2015). Deep Learning Models for Sentiment Analysis in Arabic. ANLP Workshop …, November, 9–17. https://doi.org/10.13140/RG.2.1.4537.4167

Schuurmans, D., & Zinkevich, M. (2016). Deep Learning Games. Nips, 1–9.

Sharma, M., Anuradha, J., Manne, H. K., & Kashyap, G. S. C. (2017). Facial detection using deep learning. IOP Conference Series: Materials Science and Engineering, 263, 042092. https://doi.org/10.1088/1757-899X/263/4/042092

Shi, H., Xu, M., Ma, Q., Zhang, C., Li, R., & Li, F. (2017). A Whole System Assessment of Novel Deep Learning Approach on Short-Term Load Forecasting. Energy Procedia, 142, 2791–2796. https://doi.org/10.1016/j.egypro.2017.12.423

Smith, L. N. (2017). Best Practices for Applying Deep Learning to Novel Applications. ArXiv, 1–10.

Socher, R., & Huval, B. (2012). Convolutional-recursive deep learning for 3D object classification. Advances in Neural …, i, 1–9.

Stevenson, M., Winter, R., & Widrow, B. (1990). Sensitivity of Feedforward Neural Networks to Weight Errors. IEEE Transactions on Neural Networks, 1(1), 71–80. https://doi.org/10.1109/72.80206

Sun, Y., Wang, X., & Tang, X. (2014). Deep learning face representation from predicting 10,000 classes. Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 1891–1898. https://doi.org/10.1109/CVPR.2014.244

Sundsøy, P. R., Bjelland, J., Iqbal, A. M., & Jahani, E. (2016). Deep Learning Applied to Mobile Phone Data for Individual Income Classification. Icaita, 96–99.

Syulistyo, A. R., Jati Purnomo, D. M., Rachmadi, M. F., & Wibowo, A. (2016). Particle Swarm Optimization (Pso) for Training Optimization on Convolutional Neural Network (Cnn). Jurnal Ilmu Komputer Dan Informasi, 9(1), 52. https://doi.org/10.21609/jiki.v9i1.366

Tang, Y. (2013). Deep Learning using Linear Support Vector Machines. Deeplearning.Net.

van der Burgh, H. K., Schmidt, R., Westeneng, H. J., de Reus, M. A., van den Berg, L. H., & van den Heuvel, M. P. (2017). Deep learning predictions of survival based on MRI in amyotrophic lateral sclerosis. NeuroImage: Clinical, 13, 361–369. https://doi.org/10.1016/j.nicl.2016.10.008

Vieira, S., Pinaya, W. H. L., & Mechelli, A. (2017). Using deep learning to investigate the neuroimaging correlates of psychiatric and neurological disorders: Methods and applications. Neuroscience and Biobehavioral Reviews, 74, 58–75. https://doi.org/10.1016/j.neubiorev.2017.01.002

Wan, J., Wang, D., Hoi, S. C. H., & Wu, P. (2014). Deep Learning for Content-Based Image Retrieval: A Comprehensive Study. Proceedings of the ACM International Conference on Multimedia (MM), 157–166.

Wang, G., Li, W., Zuluaga, M. A., Pratt, R., Patel, P. A., Aertsen, M., Doel, T., David, A. L., Deprest, J., Ourselin, S., & Vercauteren, T. (2018). Interactive Medical Image Segmentation using Deep Learning with Image-specific Fine-tuning. IEEE Transactions on Medical Imaging, 1–12. https://doi.org/10.1109/TMI.2018.2791721

Wang, W., Zhang, M., Chen, G., Jagadish, H. V, Ooi, B. C., & Tan, K.-L. (2016). Database Meets Deep Learning. ACM SIGMOD Record, 45(2), 17–22. https://doi.org/10.1145/3003665.3003669

Wang, X., Gao, L., & Mao, S. (2017). BiLoc: Bi-Modal Deep Learning for Indoor Localization with Commodity 5GHz WiFi. IEEE Access, 5, 4209–4220. https://doi.org/10.1109/ACCESS.2017.2688362

Wang, Y. (2016). Application of Deep Learning to Biomedical Informatics. International Journal of Applied Science - Research and Review, 03(05), 3–5. https://doi.org/10.21767/2349-7238.100048

Wang, Z. (2015). The Applications of Deep Learning on Traffic Identification. Black Hat USA.

Wu, B.-F., & Lin, C.-H. (2018). Adaptive Feature Mapping for Customizing Deep Learning Based Facial Expression Recognition Model. IEEE Access, XX(c), 1–1. https://doi.org/10.1109/ACCESS.2018.2805861

Wu, Z., Jiang, Y.-G., Wang, J., Pu, J., & Xue, X. (2014). Exploring Inter-feature and Inter-class Relationships with Deep Neural Networks for Video Classification. Proceedings of the ACM International Conference on Multimedia - MM ’14, 167–176. https://doi.org/10.1145/2647868.2654931

Wu, Z. Y., & Rahman, A. (2017). Optimized Deep Learning Framework for Water Distribution Data-Driven Modeling. Procedia Engineering, 186, 261–268. https://doi.org/10.1016/j.proeng.2017.03.240

Xue, Y., & Ray, N. (2017). Cell Detection with Deep Convolutional Neural Network and Compressed Sensing. 1–29.

Yan, H., Wan, J., Zhang, C., Tang, S., Hua, Q., & Wang, Z. (2018). Industrial Big Data Analytics for Prediction of Remaining Useful Life Based on Deep Learning. IEEE Access, 3536(c), 1–1. https://doi.org/10.1109/ACCESS.2018.2809681

Yang, P.-C., Sasaki, K., Suzuki, K., Kase, K., Sugano, S., & Ogata, T. (2017). Repeatable Folding Task by Humanoid Robot Worker Using Deep Learning. IEEE Robotics and Automation Letters, 2(2), 397–403. https://doi.org/10.1109/LRA.2016.2633383

Yoo, Y., Tang, L. Y. W., Brosch, T., Li, D. K. B., Kolind, S., Vavasour, I., Rauscher, A., MacKay, A. L., Traboulsee, A., & Tam, R. C. (2018). Deep learning of joint myelin and T1w MRI features in normal-appearing brain tissue to distinguish between multiple sclerosis patients and healthy controls. NeuroImage: Clinical, 17(October 2017), 169–178. https://doi.org/10.1016/j.nicl.2017.10.015

Yu, L., Hermann, K. M., Blunsom, P., & Pulman, S. (2014). Deep Learning for Answer Sentence Selection. 1–9.

Yuan, Z., Lu, Y., & Xue, Y. (2016). Droiddetector: Android malware characterization and detection using deep learning. Tsinghua Science and Technology, 21(1), 114–123. https://doi.org/10.1109/TST.2016.7399288

Zhao, L., Sun, Q., & Zhang, Z. (2017). Single Image Super-Resolution Based on Deep Learning Features and Dictionary Model. IEEE Access, 5(Cccv), 17126–17135. https://doi.org/10.1109/ACCESS.2017.2736058

Zhu, Z., Luo, P., Wang, X., & Tang, X. (2013). Deep learning identity-preserving face space. Proceedings of the IEEE International Conference on Computer Vision, 113–120. https://doi.org/10.1109/ICCV.2013.21

Zulfa, I., & Winarko, E. (2017). Sentimen Analisis Tweet Berbahasa Indonesia Dengan Deep Belief Network. IJCCS (Indonesian Journal of Computing and Cybernetics Systems), 11(2), 187. https://doi.org/10.22146/ijccs.24716

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