Concerns of Ethical and Privacy in the Rapid Advancement of Artificial Intelligence: Directions, Challenges, and Solutions

Furizal Furizal, Agus Ramelan, Feri Adriyanto, Hari Maghfiroh, Alfian Ma'arif, Kariyamin Kariyamin, Alya Masitha, Aldi Bastiatul Fawait

Abstract


AI is a transformative technology that emulates human cognitive abilities and processes large volumes of data to offer efficient solutions across various sectors of life. Although AI significantly enhances efficiency in many areas, it also presents substantial challenges, particularly regarding ethics and user privacy. These challenges are exacerbated by the inadequacy of global regulations, which may lead to potential abuse and privacy violations. This study provides an in-depth review of current AI applications, identifies future needs, and addresses emerging ethical and privacy issues. The research explores the important roles of AI technologies, including multimodal AI, natural language processing, generative AI, and deepfakes. While these technologies have the potential to revolutionize industries such as content creation and digital interactions, they also face significant privacy and ethical challenges, including the risks of deepfake abuse and the need for improved data protection through platforms like PrivAI. The study emphasizes the necessity for stricter regulations and global efforts to ensure ethical AI use and effective privacy protection. By conducting a comprehensive literature review, this research aims to provide a clear perspective on the future direction of AI and propose strategies to overcome barriers in ethical and privacy practices.

Keywords


Artificial Intelligence; Ethical; Privacy; Concern; Rapid Advancement.

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References


M. H. Jarrahi, “Artificial intelligence and the future of work: Human-AI symbiosis in organizational decision making,” Bus Horiz, vol. 61, no. 4, pp. 577–586, Jul. 2018, doi: 10.1016/j.bushor.2018.03.007.

A. Trunk, H. Birkel, and E. Hartmann, “On the current state of combining human and artificial intelligence for strategic organizational decision making,” Business Research, vol. 13, no. 3, pp. 875–919, Nov. 2020, doi: 10.1007/s40685-020-00133-x.

D. Saba, Y. Sahli, and A. Hadidi, “The Role of Artificial Intelligence in Company’s Decision Making,” Enabling AI Applications in Data Science, pp. 287–314, 2021, doi: 10.1007/978-3-030-52067-0_13.

N. Wijaya, “Capital Letter Pattern Recognition in Text to Speech by Way of Perceptron Algorithm,” Knowledge Engineering and Data Science, vol. 1, no. 1, p. 26, Dec. 2017, doi: 10.17977/um018v1i12018p26-32.

K. C. Kirana and S. A. K. Abdulrahman, “Random Multi-Augmentation to Improve TensorFlow-Based Vehicle Plate Detection,” Buletin Ilmiah Sarjana Teknik Elektro, vol. 6, no. 2, pp. 113–125, 2024.

A. Z. Dhunny, R. H. Seebocus, Z. Allam, M. Y. Chuttur, M. Eltahan, and H. Mehta, “Flood Prediction using Artificial Neural Networks: Empirical Evidence from Mauritius as a Case Study,” Knowledge Engineering and Data Science, vol. 3, no. 1, pp. 1–10, Aug. 2020, doi: 10.17977/um018v3i12020p1-10.

H. Alaskar and T. Saba, “Machine Learning and Deep Learning: A Comparative Review,” Proceedings of Integrated Intelligence Enable Networks and Computing: IIENC 2020, pp. 143–150, 2021, doi: 10.1007/978-981-33-6307-6_15.

M. M. Taye, “Understanding of Machine Learning with Deep Learning: Architectures, Workflow, Applications and Future Directions,” Computers, vol. 12, no. 5, p. 91, Apr. 2023, doi: 10.3390/computers12050091.

A. Kurniawati, E. M. Yuniarno, and Y. K. Suprapto, “Deep Learning for Multi-Structured Javanese Gamelan Note Generator,” Knowledge Engineering and Data Science, vol. 6, no. 1, p. 41, Jul. 2023, doi: 10.17977/um018v6i12023p41-56.

A. Pamungkas and A. Fadlil, “Optimizing Banana Type Identification: An Support Vector Machine Classification-Based Approach for Cavendish, Mas, and Tanduk Varieties,” Buletin Ilmiah Sarjana Teknik Elektro, vol. 5, no. 4, pp. 539–551, 2023.

G. Airlangga, “Performance Evaluation of Deep Learning Techniques in Gesture Recognition Systems,” Buletin Ilmiah Sarjana Teknik Elektro, vol. 6, no. 1, pp. 83–90, 2024.

J. Bajwa, U. Munir, A. Nori, and B. Williams, “Artificial intelligence in healthcare: transforming the practice of medicine,” Future Healthc J, vol. 8, no. 2, pp. e188–e194, Jul. 2021, doi: 10.7861/fhj.2021-0095.

K. L.-M. Ang, J. K. P. Seng, E. Ngharamike, and G. K. Ijemaru, “Emerging Technologies for Smart Cities’ Transportation: Geo-Information, Data Analytics and Machine Learning Approaches,” ISPRS Int J Geoinf, vol. 11, no. 2, p. 85, Jan. 2022, doi: 10.3390/ijgi11020085.

A. Boukerche, Y. Tao, and P. Sun, “Artificial intelligence-based vehicular traffic flow prediction methods for supporting intelligent transportation systems,” Computer Networks, vol. 182, p. 107484, Dec. 2020, doi: 10.1016/j.comnet.2020.107484.

A. Nikitas, K. Michalakopoulou, E. T. Njoya, and D. Karampatzakis, “Artificial Intelligence, Transport and the Smart City: Definitions and Dimensions of a New Mobility Era,” Sustainability, vol. 12, no. 7, p. 2789, Apr. 2020, doi: 10.3390/su12072789.

F. Ouyang and P. Jiao, “Artificial intelligence in education: The three paradigms,” Computers and Education: Artificial Intelligence, vol. 2, p. 100020, 2021, doi: 10.1016/j.caeai.2021.100020.

A. Alam, “Should Robots Replace Teachers? Mobilisation of AI and Learning Analytics in Education,” in 2021 International Conference on Advances in Computing, Communication, and Control (ICAC3), pp. 1–12, 2021, doi: 10.1109/ICAC353642.2021.9697300.

W. Leal Filho et al., “Using artificial intelligence to implement the UN sustainable development goals at higher education institutions,” International Journal of Sustainable Development & World Ecology, vol. 31, no. 6, pp. 726–745, Aug. 2024, doi: 10.1080/13504509.2024.2327584.

S. Z. Kamoonpuri and A. Sengar, “Hi, May AI help you? An analysis of the barriers impeding the implementation and use of artificial intelligence-enabled virtual assistants in retail,” Journal of Retailing and Consumer Services, vol. 72, p. 103258, May 2023.

S. G. Thandekkattu and M. Kalaiarasi, “Customer-Centric E-commerce Implementing Artificial Intelligence for Better Sales and Service,” Proceedings of Second International Conference on Advances in Computer Engineering and Communication Systems: ICACECS 2021, pp. 141–152, 2022, doi: 10.1007/978-981-16-7389-4_14.

M. Javaid, A. Haleem, I. H. Khan, and R. Suman, “Understanding the potential applications of Artificial Intelligence in Agriculture Sector,” Advanced Agrochem, vol. 2, no. 1, pp. 15–30, Mar. 2023, doi: 10.1016/j.aac.2022.10.001.

A. Sharma, M. Georgi, M. Tregubenko, A. Tselykh, and A. Tselykh, “Enabling smart agriculture by implementing artificial intelligence and embedded sensing,” Comput Ind Eng, vol. 165, p. 107936, Mar. 2022, doi: 10.1016/j.cie.2022.107936.

T. Talaviya, D. Shah, N. Patel, H. Yagnik, and M. Shah, “Implementation of artificial intelligence in agriculture for optimisation of irrigation and application of pesticides and herbicides,” Artificial Intelligence in Agriculture, vol. 4, pp. 58–73, 2020, doi: 10.1016/j.aiia.2020.04.002.

A. Setiawan, U. L. Wibowo, A. Mubarok, K. Larasati, and J. A. H. Hammad, “Random Forest Algorithm to Measure the Air Pollution Standard Index,” Knowledge Engineering and Data Science, vol. 7, no. 1, pp. 86–100, 2024.

S. Gupta, S. Modgil, S. Bhattacharyya, and I. Bose, “Artificial intelligence for decision support systems in the field of operations research: review and future scope of research,” Ann Oper Res, vol. 308, no. 1–2, pp. 215–274, Jan. 2022, doi: 10.1007/s10479-020-03856-6.

M. Schmitt, “Automated machine learning: AI-driven decision making in business analytics,” Intelligent Systems with Applications, vol. 18, p. 200188, May 2023, doi: 10.1016/j.iswa.2023.200188.

I. H. Sarker, “Data Science and Analytics: An Overview from Data-Driven Smart Computing, Decision-Making and Applications Perspective,” SN Comput Sci, vol. 2, no. 5, p. 377, Sep. 2021, doi: 10.1007/s42979-021-00765-8.

N. H. Parmenas and R. S. Samosir, “Industrial Relations Dispute Simulation System Prototype with Artificial Intelligence Approach,” Buletin Ilmiah Sarjana Teknik Elektro, vol. 5, no. 2, pp. 291–302, 2023, doi: 10.12928/biste.v5i2.7607.

M. Eigenstetter, “Ensuring Trust in and Acceptance of Digitalization and Automation: Contributions of Human Factors and Ethics,” Digital Human Modeling and Applications in Health, Safety, Ergonomics and Risk Management. Human Communication, Organization and Work: 11th International Conference, DHM 2020, Held as Part of the 22nd HCI International Conference, HCII 2020, Copenhagen, Denmark, July 19–24, 2020, Proceedings, Part II 22, pp. 254–266, 2020, doi: 10.1007/978-3-030-49907-5_18.

J. Baker-Brunnbauer, “Management perspective of ethics in artificial intelligence,” AI and Ethics, vol. 1, no. 2, pp. 173–181, May 2021, doi: 10.1007/s43681-020-00022-3.

C. D. Raab, “Information privacy, impact assessment, and the place of ethics,” Computer Law & Security Review, vol. 37, p. 105404, Jul. 2020, doi: 10.1016/j.clsr.2020.105404.

S. S. Gill et al., “AI for next generation computing: Emerging trends and future directions,” Internet of Things, vol. 19, p. 100514, Aug. 2022, doi: 10.1016/j.iot.2022.100514.

C. R. Taylor, N. Monga, C. Johnson, J. R. Hawley, and M. Patel, “Artificial Intelligence Applications in Breast Imaging: Current Status and Future Directions,” Diagnostics, vol. 13, no. 12, p. 2041, Jun. 2023, doi: 10.3390/diagnostics13122041.

J. C. De Gagne, “The State of Artificial Intelligence in Nursing Education: Past, Present, and Future Directions,” Int J Environ Res Public Health, vol. 20, no. 6, p. 4884, Mar. 2023, doi: 10.3390/ijerph20064884.

K. Zhang and A. B. Aslan, “AI technologies for education: Recent research & future directions,” Computers and Education: Artificial Intelligence, vol. 2, p. 100025, 2021, doi: 10.1016/j.caeai.2021.100025.

M. M. Merlec, Y. K. Lee, S.-P. Hong, and H. P. In, “A Smart Contract-Based Dynamic Consent Management System for Personal Data Usage under GDPR,” Sensors, vol. 21, no. 23, p. 7994, Nov. 2021, doi: 10.3390/s21237994.

M. Macenaite and E. Kosta, “Consent for processing children’s personal data in the EU: following in US footsteps?,” Information & Communications Technology Law, vol. 26, no. 2, pp. 146–197, May 2017, doi: 10.1080/13600834.2017.1321096.

J. Ruohonen and K. Hjerppe, “The GDPR enforcement fines at glance,” Inf Syst, vol. 106, p. 101876, May 2022, doi: 10.1016/j.is.2021.101876.

F. Flack, C. Adams, and J. Allen, “Authorising the Release of Data without Consent for Health Research: The Role of Data Custodians and HRECs in Australia.,” J Law Med, vol. 26, no. 3, pp. 655–680, Apr. 2019.

M. H. Arnold, “Teasing out Artificial Intelligence in Medicine: An Ethical Critique of Artificial Intelligence and Machine Learning in Medicine,” J Bioeth Inq, vol. 18, no. 1, pp. 121–139, Mar. 2021, doi: 10.1007/s11673-020-10080-1.

A. J. Andreotta, N. Kirkham, and M. Rizzi, “AI, big data, and the future of consent,” AI Soc, vol. 37, no. 4, pp. 1715–1728, Dec. 2022, doi: 10.1007/s00146-021-01262-5.

M. A. Levin, J. P. Wanderer, and J. M. Ehrenfeld, “Data, Big Data, and Metadata in Anesthesiology,” Anesth Analg, vol. 121, no. 6, pp. 1661–1667, Dec. 2015, doi: 10.1213/ANE.0000000000000716.

J. Hallamaa and T. Kalliokoski, “AI Ethics as Applied Ethics,” Front Comput Sci, vol. 4, Apr. 2022, doi: 10.3389/fcomp.2022.776837.

M. Borghi, F. Ferretti, and S. Karapapa, “Online data processing consent under EU law: a theoretical framework and empirical evidence from the UK,” International Journal of Law and Information Technology, vol. 21, no. 2, pp. 109–153, Jun. 2013, doi: 10.1093/ijlit/eat001.

B. Gonçalves, “The Turing Test is a Thought Experiment,” Minds Mach (Dordr), vol. 33, no. 1, pp. 1–31, Mar. 2023, doi: 10.1007/s11023-022-09616-8.

A. M. Turing, “Computing Machinery and Intelligence,” in Parsing the Turing Test, pp. 23–65, 2009, doi: 10.1007/978-1-4020-6710-5_3.

V. Rajaraman, “JohnMcCarthy — Father of artificial intelligence,” Resonance, vol. 19, no. 3, pp. 198–207, Mar. 2014, doi: 10.1007/s12045-014-0027-9.

M. van Assen, E. Muscogiuri, G. Tessarin, and C. N. De Cecco, “Artificial Intelligence: A Century-Old Story,” Artificial Intelligence in Cardiothoracic Imaging, pp. 3–13, 2022, doi: 10.1007/978-3-030-92087-6_1.

J. Fleck, “Development and Establishment in Artificial Intelligence,” in The Question of Artificial Intelligence, pp. 106–164, 2018, doi: 10.4324/9780429505331-3.

S. Sabanovic, S. Milojevic, and J. Kaur, “John McCarthy [History],” IEEE Robot Autom Mag, vol. 19, no. 4, pp. 99–106, Dec. 2012, doi: 10.1109/MRA.2012.2221259.

H. I. K. Fathurrahman and C. Li-Yi, “Character Translation on Plate Recognition with Intelligence Approaches,” Buletin Ilmiah Sarjana Teknik Elektro, vol. 4, no. 3, pp. 105–110, 2023.

M. M. Islam, Mst. T. Akter, H. M. Tahrim, N. S. Elme, and Md. Y. A. Khan, “A Review on Employing Weather Forecasts for Microgrids to Predict Solar Energy Generation with IoT and Artificial Neural Networks,” Control Systems and Optimization Letters, vol. 2, no. 2, pp. 184–190, 2024, doi: 10.59247/csol.v2i2.108.

B. P. Ganthia et al., “Artificial Neural Network Optimized Load Forecasting of Smartgrid using MATLAB,” Control Systems and Optimization Letters, vol. 1, no. 1, pp. 46–51, May 2023.

U. Athiyah, A. W. Muhammad, and A. Azhari, “Human Intestinal Condition Identification based-on Blended Spatial and Morphological Feature using Artificial Neural Network Classifier,” Knowledge Engineering and Data Science, vol. 3, no. 1, pp. 19–27, Aug. 2020.

Md. A. Habib et al., “Exploring Progress in Text-to-Image Synthesis: An In-Depth Survey on the Evolution of Generative Adversarial Networks,” IEEE Access, 2024, doi: 10.1109/ACCESS.2024.3435541.

X. Wang, Z. He, and X. Peng, “Artificial-Intelligence-Generated Content with Diffusion Models: A Literature Review,” Mathematics, vol. 12, no. 7, p. 977, Mar. 2024, doi: 10.3390/math12070977.

M.-F. Wong, S. Guo, C.-N. Hang, S.-W. Ho, and C.-W. Tan, “Natural Language Generation and Understanding of Big Code for AI-Assisted Programming: A Review,” Entropy, vol. 25, no. 6, p. 888, Jun. 2023.

W. Godoy, P. Valero-Lara, K. Teranishi, P. Balaprakash, and J. Vetter, “Evaluation of OpenAI Codex for HPC Parallel Programming Models Kernel Generation,” in Proceedings of the 52nd International Conference on Parallel Processing Workshops, pp. 136–144, 2023.

J.-P. Briot, “From artificial neural networks to deep learning for music generation: history, concepts and trends,” Neural Comput Appl, vol. 33, no. 1, pp. 39–65, Jan. 2021, doi: 10.1007/s00521-020-05399-0.

E. Frid, C. Gomes, and Z. Jin, “Music Creation by Example,” in Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems, pp. 1–13, 2020, doi: 10.1145/3313831.3376514.

G. Yenduri et al., “GPT (Generative Pre-Trained Transformer)— A Comprehensive Review on Enabling Technologies, Potential Applications, Emerging Challenges, and Future Directions,” IEEE Access, vol. 12, pp. 54608–54649, 2024, doi: 10.1109/ACCESS.2024.3389497.

H. Liao et al., “GPT-4 enhanced multimodal grounding for autonomous driving: Leveraging cross-modal attention with large language models,” Communications in Transportation Research, vol. 4, p. 100116, Dec. 2024, doi: 10.1016/j.commtr.2023.100116.

S. Shahriar et al., “Putting GPT-4o to the Sword: A Comprehensive Evaluation of Language, Vision, Speech, and Multimodal Proficiency,” Applied Sciences, vol. 14, no. 17, p. 7782, Sep. 2024, doi: 10.3390/app14177782.

L. S. Riza et al., “Comparison of Machine Learning Algorithms for Species Family Classification using DNA Barcode,” Knowledge Engineering and Data Science, vol. 6, no. 2, p. 231, Nov. 2023.

K. Karthick, “Comprehensive Overview of Optimization Techniques in Machine Learning Training,” Control Systems and Optimization Letters, vol. 2, no. 1, pp. 23–27, 2024.

A. M. Al-Ansi and A. Al-Ansi, “An Overview of Artificial Intelligence (AI) in 6G: Types, Advantages, Challenges and Recent Applications Authors,” Buletin Ilmiah Sarjana Teknik Elektro, vol. 5, no. 1, pp. 67–75, 2023.

G. Airlangga, “Fuzzy A* for optimum Path Planning in a Large Maze,” Buletin Ilmiah Sarjana Teknik Elektro, vol. 5, no. 4, pp. 455–466, 2023.

K. Trang and A. H. Nguyen, “A Comparative Study of Machine Learning-based Approach for Network Traffic Classification,” Knowledge Engineering and Data Science, vol. 4, no. 2, p. 128, Jan. 2022, doi: 10.17977/um018v4i22021p128-137.

M. Y. Chuttur and Y. Parianen, “A Comparison of Machine Learning Models to Prioritise Emails using Emotion Analysis for Customer Service Excellence,” Knowledge Engineering and Data Science, vol. 5, no. 1, p. 41, Jun. 2022, doi: 10.17977/um018v5i12022p41-52.

P. H. Suputra, A. D. Sensusiati, M. D. Artaria, G. J. Verkerke, E. M. Yuniarno, and I. K. E. Purnama, “Automatic 3D Cranial Landmark Positioning based on Surface Curvature Feature using Machine Learning,” Knowledge Engineering and Data Science, vol. 5, no. 1, p. 27, Jun. 2022, doi: 10.17977/um018v5i12022p27-40.

G. Airlangga, “Comparative Analysis of Machine Learning Models for Tree Species Classification from UAV LiDAR Data Authors,” Buletin Ilmiah Sarjana Teknik Elektro, vol. 6, no. 1, pp. 54–62, 2024.

F. Furizal, S. S. Mawarni, S. A. Akbar, A. Yudhana, and M. Kusno, “Analysis of the Influence of Number of Segments on Similarity Level in Wound Image Segmentation Using K-Means Clustering Algorithm,” Control Systems and Optimization Letters, vol. 1, no. 3, pp. 132–138, Sep. 2023, doi: 10.59247/csol.v1i3.33.

E. J. Kusuma, I. Pantiawati, and S. Handayani, “Melanoma Classification based on Simulated Annealing Optimization in Neural Network,” Knowledge Engineering and Data Science, vol. 4, no. 2, p. 97, Mar. 2022, doi: 10.17977/um018v4i22021p97-104.

D. M. N. Fajri, W. F. Mahmudy, and T. Yulianti, “Detection of Disease and Pest of Kenaf Plant Based on Image Recognition with VGGNet19,” Knowledge Engineering and Data Science, vol. 4, no. 1, p. 55, Aug. 2021, doi: 10.17977/um018v4i12021p55-68.

I. K. M. Jais, A. R. Ismail, and S. Q. Nisa, “Adam Optimization Algorithm for Wide and Deep Neural Network,” Knowledge Engineering and Data Science, vol. 2, no. 1, p. 41, Jun. 2019.

D. F. Laistulloh, A. N. Handayani, R. A. Asmara, and P. Taw, “Convolutional Neural Network in Motion Detection for Physiotherapy Exercise Movement,” Knowledge Engineering and Data Science, vol. 7, no. 1, p. 27, May 2024.

L. A. Latumakulita, S. L. Lumintang, D. T. Salakia, S. R. Sentinuwo, A. M. Sambul, and N. Islam, “Human Facial Expressions Identification using Convolutional Neural Network with VGG16 Architecture,” Knowledge Engineering and Data Science, vol. 5, no. 1, p. 78, Jun. 2022, doi: 10.17977/um018v5i12022p78-86.

A. Y. Saleh and L. K. Xian, “Stress Classification using Deep Learning with 1D Convolutional Neural Networks,” Knowledge Engineering and Data Science, vol. 4, no. 2, p. 145, Dec. 2021.

F. F. Rahani and P. A. Rosyady, “Quadrotor Altitude Control using Recurrent Neural Network PID,” Buletin Ilmiah Sarjana Teknik Elektro, vol. 5, no. 2, pp. 279–290, 2023.

M. Abumohsen, A. Y. Owda, and M. Owda, “Electrical Load Forecasting Using LSTM, GRU, and RNN Algorithms,” Energies (Basel), vol. 16, no. 5, p. 2283, Feb. 2023, doi: 10.3390/en16052283.

M. R. Raza, W. Hussain, and J. M. Merigo, “Cloud Sentiment Accuracy Comparison using RNN, LSTM and GRU,” in 2021 Innovations in Intelligent Systems and Applications Conference (ASYU), pp. 1–5, 2021, doi: 10.1109/ASYU52992.2021.9599044.

A. Pranolo, X. Zhou, Y. Mao, and B. Widi, “Exploring LSTM-based Attention Mechanisms with PSO and Grid Search under Different Normalization Techniques for Energy demands Time Series Forecasting,” Knowledge Engineering and Data Science, vol. 7, no. 1, pp. 1–12, 2024.

H. L. Nisa and A. Ahdika, “Hybrid Method for User Review Sentiment Categorization in ChatGPT Application Using N-Gram and Word2Vec Features,” Knowledge Engineering and Data Science, vol. 7, no. 1, p. 13, Apr. 2024, doi: 10.17977/um018v7i12024p13-26.

Y. Qu, P. Liu, W. Song, L. Liu, and M. Cheng, “A Text Generation and Prediction System: Pre-training on New Corpora Using BERT and GPT-2,” in 2020 IEEE 10th International Conference on Electronics Information and Emergency Communication (ICEIEC), pp. 323–326, 2020, doi: 10.1109/ICEIEC49280.2020.9152352.

B. Yang, X. Luo, K. Sun, and M. Y. Luo, “Recent Progress on Text Summarisation Based on BERT and GPT,” International Conference on Knowledge Science, Engineering and Management, pp. 225–241, 2023, doi: 10.1007/978-3-031-40292-0_19.

M. Dolinsky, “Trends in the Development of Basic Computer Education at Universities,” Buletin Ilmiah Sarjana Teknik Elektro, vol. 5, no. 4, pp. 584–591, 2024.

Md. T. Hossain, R. Afrin, and Mohd. A.-A. Biswas, “A Review on Attacks against Artificial Intelligence (AI) and Their Defence Image Recognition and Generation Machine Learning, Artificial Intelligence,” Control Systems and Optimization Letters, vol. 2, no. 1, pp. 52–59, 2024.

D. Velev and P. Zlateva, “Issues of Artificial Intelligence Application in Digital Marketing,” in Frontiers in Artificial Intelligence and Applications, 2023, doi: 10.3233/FAIA230716.

Y. Hu et al., “Artificial Intelligence Approaches,” Geographic Information Science & Technology Body of Knowledge, vol. 2019, Jul. 2019, doi: 10.22224/gistbok/2019.3.4.

I. H. Sarker, “AI-Based Modeling: Techniques, Applications and Research Issues Towards Automation, Intelligent and Smart Systems,” SN Comput Sci, vol. 3, no. 2, p. 158, Mar. 2022, doi: 10.1007/s42979-022-01043-x.

Y.-J. Cao et al., “Recent Advances of Generative Adversarial Networks in Computer Vision,” IEEE Access, vol. 7, pp. 14985–15006, 2019, doi: 10.1109/ACCESS.2018.2886814.

A. Dash, J. Ye, and G. Wang, “A Review of Generative Adversarial Networks (GANs) and Its Applications in a Wide Variety of Disciplines: From Medical to Remote Sensing,” IEEE Access, vol. 12, pp. 18330–18357, 2024, doi: 10.1109/ACCESS.2023.3346273.

A. K. S. Lenson and G. Airlangga, “Comparative Analysis of MLP, CNN, and RNN Models in Automatic Speech Recognition: Dissecting Performance Metric,” Buletin Ilmiah Sarjana Teknik Elektro, vol. 5, no. 4, pp. 576–583, 2024.

D. A. A. Pertiwi, P. R. Setyorini, M. A. Muslim, and E. Sugiharti, “Implementation of Discretisation and Correlation-based Feature Selection to Optimize Support Vector Machine in Diagnosis of Chronic Kidney Disease,” Buletin Ilmiah Sarjana Teknik Elektro, vol. 5, no. 2, pp. 201–209, 2023.

M. A. Yulianto and A. Fadlil, “Wood Type Identification System using Naive Bayes Classification,” Control Systems and Optimization Letters, vol. 1, no. 3, pp. 139–143, Sep. 2023, doi: 10.59247/csol.v1i3.52.

N. A. Daulay, S. R. Putri, A. W. Wijayanto, and I. Y. Wulansari, “Optimizing Malaria Control: Granular and Cost-Effective Mosquito Habitat Index in Endemic Areas Through Satellite Imagery,” Knowledge Engineering and Data Science, vol. 7, no. 1, p. 40, May 2024, doi: 10.17977/um018v7i12024p40-57.

H. L. Fadhila, V. A. Permadi, and S. P. Tahalea, “Optimising the Fashion E-Commerce Journey: A Data-Driven Approach to Customer Retention,” Knowledge Engineering and Data Science, vol. 7, no. 1, pp. 58–70, 2024.

M. Á. G. Pérez, A. G. González, F. J. C. Rodríguez, I. M. M. Leon, and F. A. L. Abrisqueta, “Precision Agriculture 4.0: Implementation of IoT, AI, and Sensor Networks for Tomato Crop Prediction,” Buletin Ilmiah Sarjana Teknik Elektro, vol. 6, no. 2, pp. 172–181, 2024, doi: 10.12928/biste.v6i2.10954.

A. Srihita, A. S. Konda, A. Bellurkar, B. Sumithra, and B. Mishra, “Private Artificial Intelligence (AI) in Social Media,” in Sustainable Development Using Private AI, pp. 240-260, 2025.

I. Gemiharto and D. Masrina, “User Privacy Preservation in AI-Powered Digital Communication Systems,” Jurnal Communio: Jurnal Ilmu Komunikasi, vol. 13, no. 2, pp. 349–359, 2024.

C. Meurisch, B. Bayrak, and M. Mühlhäuser, “Privacy-preserving AI Services Through Data Decentralization,” in WWW ’20: Proceedings of The Web Conference 2020, pp. 190–200, 2020, doi: 10.1145/3366423.338010.

S. A. Khowaja, K. Dev, N. M. F. Qureshi, P. Khuwaja, and L. Foschini, “Toward Industrial Private AI: A Two-Tier Framework for Data and Model Security,” IEEE Wirel Commun, vol. 29, no. 2, pp. 76–83, Apr. 2022, doi: 10.1109/MWC.001.2100479.

A. Ziller et al., “Reconciling privacy and accuracy in AI for medical imaging,” Nat Mach Intell, vol. 6, no. 7, pp. 764–774, Jun. 2024, doi: 10.1038/s42256-024-00858-y.




DOI: https://doi.org/10.18196/jrc.v5i6.24090

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P-ISSN: 2715-5056 || E-ISSN: 2715-5072
Organized by Peneliti Teknologi Teknik Indonesia
Published by Universitas Muhammadiyah Yogyakarta in collaboration with Peneliti Teknologi Teknik Indonesia, Indonesia and the Department of Electrical Engineering
Website: http://journal.umy.ac.id/index.php/jrc
Email: jrcofumy@gmail.com


Kuliah Teknik Elektro Terbaik