Pemanfaatan Machine Learning dalam Berbagai Bidang: Review paper

Ahmad Roihan, Po Abas Sunarya, Ageng Setiani Rafika

Sari


Abstrak - Pembelajaran mesin merupakan bagian dari kecerdasan buatan yang banyak digunakan untuk memecahkan berbagai masalah. Artikel ini menyajikan ulasan pemecahan masalah dari penelitian-penelitian terkini dengan mengklasifikasikan machine learning menjadi tiga kategori: pembelajaran terarah, pembelajaran tidak terarah, dan pembelajaran reinforcement. Hasil ulasan menunjukkan ketiga kategori masih berpeluang digunakan dalam beberapa kasus terkini dan dapat ditingkatkan untuk mengurangi beban komputasi dan mempercepat kinerja untuk mendapatkan tingkat akurasi dan presisi yang tinggi. Tujuan ulasan artikel ini diharapkan dapat menemukan celah dan dijadikan pedoman untuk penelitian pada masa yang akan datang.

Katakunci: pembelajaran mesin, pembelajaran reinforcement, pembelajaran terarah, pembelajaran tidak terarah

Abstract - Machine learning is part of artificial intelligence that is widely used to solve various problems. This article reviews problem solving from the latest studies by classifying machine learning into three categories: supervised learning, unsupervised learning, and reinforcement learning. The results of the review show that the three categories are still likely to be used in some of the latest cases and can be improved to reduce computational costs and accelerate performance to get a high level of accuracy and precision. The purpose of this article review is expected to be able to find a gap and it is used as a guideline for future research.

Keywords: machine learning, reinforcement learning, supervised learning, unsupervised learning


Teks Lengkap:

PDF

Referensi


Aji, B. P., & Wibisono, M. A. (2018). Strategi Pengambilan Keputusan Penjualan Dalam Rangka Optimasi Profit Industri Ritel Berbasis Unsupervised Machine Learning Algorithm (Studi Kasus Modern Minimarket-X).

Amei, W., Huailin, D., Qingfeng, W., & Ling, L. (2011). A survey of application-level protocol identification based on machine learning. 2011 International Conference on Information Management, Innovation Management and Industrial Engineering, 3, 201–204.

Athmaja, S., Hanumanthappa, M., & Kavitha, V. (2017). A survey of machine learning algorithms for big data analytics. 2017 International Conference on Innovations in Information, Embedded and Communication Systems (ICIIECS), 1–4.

Board, F. S. (2017). Artificial intelligence and machine learning in financial services. November, Available at: Http://Www. Fsb. Org/2017/11/Artificialintelligence-and-Machine-Learning-in-Financialservice/(Accessed 30th January, 2018).

Brownlee, J. (2016). Master Machine Learning Algorithms: discover how they work and implement them from scratch. Jason Brownlee.

Darujati, C., & Gumelar, A. B. (2012). Pemanfaatan Teknik Supervised Untuk Klasifikasi Teks Bahasa Indonesia. Jurnal Bandung Text Mining, 16(1), 1–5.

Das, S., & Nene, M. J. (2017). A survey on types of machine learning techniques in intrusion prevention systems. 2017 International Conference on Wireless Communications, Signal Processing and Networking (WiSPNET), 2296–2299. https://doi.org/10.1109/WiSPNET.2017.8300169

Dharmawan, D. A., Li, D., Ng, B. P., & Rahardja, S. (2019). A New Hybrid Algorithm for Retinal Vessels Segmentation on Fundus Images. IEEE Access, 7, 41885–41896.

Dharmawan, D. A., Ng, B. P., & Rahardja, S. (2018). A Modified Dolph-Chebyshev Type II Function Matched Filter for Retinal Vessels Segmentation. Symmetry, 10(7). https://doi.org/10.3390/sym10070257

Ester, M., Kriegel, H.-P., Sander, J., Xu, X., & others. (1996). A density-based algorithm for discovering clusters in large spatial databases with noise.

Goldberg, D. E., & Holland, J. H. (1988). Genetic algorithms and machine learning. Machine Learning, 3(2), 95–99.

Good, Z., Borges, L., Gonzalez, N. V., Sahaf, B., Samusik, N., Tibshirani, R., … Bendall, S. C. (2019). Proliferation tracing with single-cell mass cytometry optimizes generation of stem cell memory-like T cells. Nature Biotechnology, 37(3), 259.

Hinton, G. (2018). Deep Learning: A Technology With the Potential to Transform Health CareThe Potential of Deep Learning Technology to Transform Health CareThe Potential of Deep Learning Technology to Transform Health Care. JAMA, 320(11), 1101–1102. https://doi.org/10.1001/jama.2018.11100

Holder, C., Pin, T., & Kalva, H. (2009). Improved machine learning techniques for low complexity MPEG-2 to H. 264 transcoding using optimized codecs. 2009 Digest of Technical Papers International Conference on Consumer Electronics, 1–2.

Huang, G.-B., Zhu, Q.-Y., & Siew, C.-K. (2006). Extreme learning machine: theory and applications. Neurocomputing, 70(1–3), 489–501.

Khan, A., Doucette, J. A., Cohen, R., & Lizotte, D. J. (2012). Integrating machine learning into a medical decision support system to address the problem of missing patient data. 2012 11th International Conference on Machine Learning and Applications, 1, 454–457.

Kosala, G., Harjoko, A., & Hartati, S. (2017). License Plate Detection Based on Convolutional Neural Network: Support Vector Machine (CNN-SVM). Proceedings of the International Conference on Video and Image Processing, 1–5. https://doi.org/10.1145/3177404.3177436

Krisandi, N., Helmi, B. P., & others. (2013). Algoritma k-Nearest Neighbor dalam Klasifikasi Data Hasil Produksi Kelapa Sawit pada PT. Minamas Kecamatan Parindu. BIMASTER, 2(1).

Lakshmi, J. V. N., & Sheshasaayee, A. (2015). Machine learning approaches on map reduce for Big Data analytics. 2015 International Conference on Green Computing and Internet of Things (ICGCIoT), 480–484.

Latif, J., Xiao, C., Imran, A., & Tu, S. (2019). Medical Imaging using Machine Learning and Deep Learning Algorithms: A Review. 2019 2nd International Conference on Computing, Mathematics and Engineering Technologies (ICoMET), 1–5.

Lee, K.-T., Yoon, H., & Lee, Y.-S. (2018). Implementation of smartwatch user interface using machine learning based motion recognition. 2018 International Conference on Information Networking (ICOIN), 807–809.

LeMoyne, R., Kerr, W., Mastroianni, T., & Hessel, A. (2014). Implementation of machine learning for classifying hemiplegic gait disparity through use of a force plate. 2014 13th International Conference on Machine Learning and Applications, 379–382.

Mahmud, M., Kaiser, M. S., Hussain, A., & Vassanelli, S. (2018). Applications of deep learning and reinforcement learning to biological data. IEEE Transactions on Neural Networks and Learning Systems, 29(6), 2063–2079.

Mitchell, T. M. (1997). Machine learning. In McGraw Hill Series in Computer Science. Retrieved from http://www.worldcat.org/oclc/61321007

Mohammadi, M., Al-Fuqaha, A., Guizani, M., & Oh, J.-S. (2018). Semisupervised deep reinforcement learning in support of IoT and smart city services. IEEE Internet of Things Journal, 5(2), 624–635.

Nayak, A., & Dutta, K. (2017). Impacts of machine learning and artificial intelligence on mankind. 2017 International Conference on Intelligent Computing and Control (I2C2), 1–3. https://doi.org/10.1109/I2C2.2017.8321908

Negnevitsky, M. (2005). Artificial intelligence: a guide to intelligent systems. Pearson education.

Qiang, W., & Zhongli, Z. (2011). Reinforcement learning model, algorithms and its application. 2011 International Conference on Mechatronic Science, Electric Engineering and Computer (MEC), 1143–1146.

Rahardja, U., Roihan, A., & others. (2017). Design of Business Intelligence in Learning Systems Using iLearning Media. Universal Journal of Management, 5(5), 227–235.

Roihan, A., Permana, A., & Mila, D. (2016). Monitoring Kebocoran Gas Menggunakan Mikrokontroler Arduino UNO dan ESP8266 Berbasis Internet of Things. ICIT (Innovative Creative and Information Technology), 2(2), 170–183.

Roihan, A., Sunarya, P. A., & Wijaya, C. (2019). Auto Tee Prototype as Tee Golf Automation in Golf Simulator Studio. 2018 6th International Conference on Cyber and IT Service Management, CITSM 2018. https://doi.org/10.1109/CITSM.2018.8674249

Russell, S. J., & Norvig, P. (2016). Artificial intelligence: a modern approach. Malaysia; Pearson Education Limited,.

Sharma, A. R., & Kaushik, P. (2017). Literature survey of statistical, deep and reinforcement learning in natural language processing. 2017 International Conference on Computing, Communication and Automation (ICCCA), 350–354.

Somvanshi, M., & Chavan, P. (2016). A review of machine learning techniques using decision tree and support vector machine. 2016 International Conference on Computing Communication Control and Automation (ICCUBEA), 1–7. https://doi.org/10.1109/ICCUBEA.2016.7860040

Sunarya, A., Santoso, S., & Sentanu, W. (2015). Sistem Pakar Untuk Mendiagnosa Gangguan Jaringan Lan. CCIT Journal, 8(2), 1–11.

Supriyono, I. A., Sudarto, F., & Fakhri, M. K. (2016). PENGUKUR TINGGI BADAN MENGGUNAKAN SENSOR ULTRASONIK BERBASIS MIKROKONTROLER ATMEGA328 DENGAN OUTPUT SUARA. CCIT Journal, 9(2), 148–156.

Sutrisno, S., Kristiadi, D. P., & Supriyanti, D. (2017). APLIKASI SISTEM PAKAR UNTUK MENDIAGNOSA GANGGUAN JARINGAN LAN BERBASIS ANDROID DI SEKOLAH KEMURNIAN JAKARTA. SENSI Journal, 3(2), 221–239.

Thupae, R., Isong, B., Gasela, N., & Abu-Mahfouz, A. M. (2018). Machine Learning Techniques for Traffic Identification and Classifiacation in SDWSN: A Survey. IECON 2018 - 44th Annual Conference of the IEEE Industrial Electronics Society, 4645–4650. https://doi.org/10.1109/IECON.2018.8591178

Watanabe, T., & Ishimaru, T. (2016). A Least Median of Squares Method Based on Fuzzy Reinforcement Learning for Modeling of Computer Vision Applications. 2016 Joint 8th International Conference on Soft Computing and Intelligent Systems (SCIS) and 17th International Symposium on Advanced Intelligent Systems (ISIS), 65–71. https://doi.org/10.1109/SCIS-ISIS.2016.0027

Wu, G., Kim, M., Wang, Q., Gao, Y., Liao, S., & Shen, D. (2013). Unsupervised deep feature learning for deformable registration of MR brain images. International Conference on Medical Image Computing and Computer-Assisted Intervention, 649–656.

Wu, Z., Khan, N. M., Gao, L., & Guan, L. (2018). Deep Reinforcement Learning with Parameterized Action Space for Object Detection. 2018 IEEE International Symposium on Multimedia (ISM), 101–104. https://doi.org/10.1109/ISM.2018.00025

Židek, K., Pitel’, J., & Hošovsk`y, A. (2017). Machine learning algorithms implementation into embedded systems with web application user interface. 2017 IEEE 21st International Conference on Intelligent Engineering Systems (INES), 77–82.




DOI: https://doi.org/10.31294/ijcit.v5i1.7951

##submission.copyrightStatement##

##submission.license.cc.by-sa4.footer##

P-ISSN: 2527-449X E-ISSN: 2549-7421
Statistik Pengunjung Jurnal IJCIT
 

Dipublikasikan oleh LPPM Universitas Bina Sarana Informatika

Jl. Kramat Raya No.98, Kwitang, Kec. Senen, Kota Jakarta Pusat, DKI Jakarta 10450
Creative Commons License
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License