Pengaruh Penggunaan Media Sosial Instagram Bagi Mahasiswa STMIK Borneo Internasional Balikpapan Menggunakan Technology Acceptance Model (TAM)

Authors

  • Ego Ismawan STMIK Borneo Internasional, Balikpapan http://orcid.org/0000-0002-3258-3090
  • Dila Seltika Canta STMIK Borneo Internasional, Balikpapan
  • Elvin Leander Hadisaputro STMIK Borneo Internasional, Balikpapan

DOI:

https://doi.org/10.30865/jurikom.v9i3.4239

Keywords:

Instagram, TAM (Technology Acceptance Model), Sosial Media, Perceived Usefulness, Perceived Ease of

Abstract

Instagram is one of the most popular social media applications in Indonesia and in the world and almost all people access it. The word Instagram is a combination of Insta (instant) and telegram. Insta refers to a polaroid camera that can produce photos instantly. Through Instagram we can upload photos and videos, and publish them. The purpose of this study was to analyze the factors of the technology acceptance model related to the use of Instagram at STMIK Borneo International Balikpapan. The problems that exist in this study are the ease of using the system, the use of the system, and the attitude it causes. This type of research is a case study. The subjects in this study were active students at STMIK Borneo Internasional class 2018-2020 who had been actively using Instagram for the last 5 months. The technique used for data collection in this research is a survey method, and by using a questionnaire. The software used by researchers to analyze the research data is IBM SPSS 25. From the research results, users feel that it is easy to use Instagram social media, and users feel the benefits of Instagram social media. From the convenience and also the benefits felt by users, the interest in using Instagram social media also produces a very good response. Based on data analysis, Perceived usefulness (POU) has a positive and significant effect on behavior intention (BEU) using Instagram. The t-statistic value is 5.040 > 2.032 with a stg value of 0.000 < 0.05. Perceived ease of use (PEU) has a positive and significant effect on behavior intention (BEU) using Instagram. The t-statistic value is 3.903 > 2.032 with a stg value of 0.000 < 0.05. In other words, the ease and benefits obtained are one of the factors in increasing interest in using Instagram social media.

References

A. Abadi, T. Rajabioun, and P. A. Ioannou, “Traffic Flow Prediction for Road Transportation Networks With Limited Traffic Data,†IEEE Trans. Intell. Transp. Syst., vol. 16, no. 2, pp. 653–662, 2015, doi: 10.1109/TITS.2014.2337238.

K. Y. Chan and T. S. Dillon, “Traffic flow prediction using orthogonal arrays and Takagi-Sugeno neural fuzzy models,†in 2014 International Joint Conference on Neural Networks (IJCNN), 2014, pp. 35–41, doi: 10.1109/IJCNN.2014.6889374.

H. Dong, L. Jia, X. Sun, C. Li, and Y. Qin, “Road traffic flow prediction with a time-oriented ARIMA model,†in NCM 2009 - 5th International Joint Conference on INC, IMS, and IDC, 2009, no. 1, pp. 1649–1652, doi: 10.1109/NCM.2009.224.

C. Hu, K. Xie, G. Song, and T. Wu, “Hybrid Process Neural Network based on Spatio-Temporal Similarities for Short-Term Traffic Flow Prediction,†in Proceedings of the 11th International IEEE Conference on Intelligent Transportation Systems Beijing, China, October 12-15, 2008, 2008, pp. 253–258.

K. Kumar, M. Parida, and V. K. Katiyar, “Short Term Traffic Flow Prediction for a Non Urban Highway Using Artificial Neural Network,†in Procedia - Social and Behavioral Sciences, 2013, vol. 104, pp. 755–764, doi: 10.1016/j.sbspro.2013.11.170.

B. Priambodo and Y. Jumaryadi, “Time Series Traffic Speed Prediction Using k-Nearest Neighbour Based on Similar Traffic Data,†MATEC Web Conf., vol. 218, p. 03021, 2018, doi: 10.1051/matecconf/201821803021.

B. Priambodo and A. Ahmad, “Traffic flow prediction model based on neighbouring roads using neural network and multiple regression,†J. Inf. Commun. Technol., vol. 17, no. 4, pp. 513–535, 2018.

B. Priambodo and A. Ahmad, “Predicting Traffic Flow Based on Average Speed of Neighbouring Road Using Multiple Regression.â€

I. Laña, J. Del Ser, and I. Olabarrieta, “Understanding Daily Mobility Patterns in Urban Road Networks using Traffic Flow Analytics,†in International Workshop on Urban Mobility & Intelligent Transportation Systems (UMITS), 2016, pp. 1157–1162, doi: 10.1109/NOMS.2016.7502980.

H. Dai and Z. Yang, “Real-Time Traffic Volume Estimation with Fuzzy Linear Regression,†in 2006 6th World Congress on Intelligent Control and Automation, 2006, pp. 3164–3167, doi: 10.1109/WCICA.2006.1712950.

D. Zhang and M. R. Kabuka, “Combining Weather Condition Data to Predict Traffic Flow: A GRU Based Deep Learning Approach,†Proc. - 2017 IEEE 15th Int. Conf. Dependable, Auton. Secur. Comput. 2017 IEEE 15th Int. Conf. Pervasive Intell. Comput. 2017 IEEE 3rd Int. Conf. Big Data Intell. Compu, vol. 2018-Janua, pp. 1216–1219, 2018, doi: 10.1109/DASC-PICom-DataCom-CyberSciTec.2017.194.

J. Lee, B. Hong, K. Lee, and Y.-J. Jang, “A Prediction Model of Traffic Congestion Using Weather Data,†in 2015 IEEE International Conference on Data Science and Data Intensive Systems, 2015, pp. 81–88, doi: 10.1109/DSDIS.2015.96.

R. Zhang, Y. Shu, Z. Yang, P. Cheng, and J. Chen, “Hybrid Traffic Speed Modeling and Prediction Using Real-World Data,†Proc. - 2015 IEEE Int. Congr. Big Data, BigData Congr. 2015, pp. 230–237, 2015, doi: 10.1109/BigDataCongress.2015.40.

W. Hu, L. Yan, and H. Wang, “Traffic Jams Prediction Method based on Two-dimension Cellular Automata Model *,†2014.

K. Lee, B. Hong, D. Jeong, and J. Lee, “Congestion pattern model for predicting short-term traffic decongestion times,†in 2014 17th IEEE International Conference on Intelligent Transportation Systems, ITSC 2014, 2014, pp. 2828–2833, doi: 10.1109/ITSC.2014.6958143.

A. Anwar, T. Nagel, and C. Ratti, “Traffic origins: A simple visualization technique to support traffic incident analysis,†IEEE Pacific Vis. Symp., pp. 316–319, 2014, doi: 10.1109/PacificVis.2014.35.

J. Ahn, E. Ko, and E. Y. Kim, “Highway traffic flow prediction using support vector regression and Bayesian classifier,†2016 Int. Conf. Big Data Smart Comput. BigComp 2016, pp. 239–244, 2016, doi: 10.1109/BIGCOMP.2016.7425919.

W. Xiong, Z. Yu, L. Eeckhout, Z. Bei, F. Zhang, and C. Xu, “SZTS: A novel big data transportation system benchmark suite,†Proc. Int. Conf. Parallel Process., vol. 2015-Decem, pp. 819–828, 2015, doi: 10.1109/ICPP.2015.91.

W. Hu, L. Yan, and H. Wang, “Traffic jams prediction method based on two-dimension cellular automata model,†in 17th International IEEE Conference on Intelligent Transportation Systems (ITSC), 2014, pp. 2023–2028, doi: 10.1109/ITSC.2014.6958001.

Z. Wang, M. Lu, X. Yuan, J. Zhang, and H. Van De Wetering, “Visual traffic jam analysis based on trajectory data,†IEEE Trans. Vis. Comput. Graph., vol. 19, no. 12, pp. 2159–2168, 2013, doi: 10.1109/TVCG.2013.228.

S. Bischof, C.-S. Karapantelakis, Athanasios Nechifor, A. Sheth, A. Mileo, and P. Barnaghi, “Real Time IoT Stream Processing and Large-scale Data Analytics for Smart City Applications,†2014.

S. Kolozali, M. Bermudez-Edo, D. Puschmann, F. Ganz, and P. Barnaghi, “A knowledge-based approach for real-time IoT data stream annotation and processing,†in Proceedings - 2014 IEEE International Conference on Internet of Things, iThings 2014, 2014 IEEE International Conference on Green Computing and Communications, GreenCom 2014 and 2014 IEEE International Conference on Cyber-Physical-Social Computing, CPS 20, 2014, no. iThings, pp. 215–222, doi: 10.1109/iThings.2014.39.

S. Bischof, A. Karapantelakis, A. Sheth, and A. Mileo, “Semantic Modelling of Smart City Data Description of Smart City Data,†in W3C Workshop on the Web of Things Enablers and services for an open Web of Devices, 2014, pp. 1–5, [Online]. Available: http://www.w3.org/2014/02/wot/papers/karapantelakis.pdf.

Additional Files

Published

2022-06-30

How to Cite

Ismawan, E., Canta, D. S., & Hadisaputro, E. L. (2022). Pengaruh Penggunaan Media Sosial Instagram Bagi Mahasiswa STMIK Borneo Internasional Balikpapan Menggunakan Technology Acceptance Model (TAM). JURNAL RISET KOMPUTER (JURIKOM), 9(3), 673–679. https://doi.org/10.30865/jurikom.v9i3.4239

Issue

Section

Articles