Perancangan Sistem Informasi ERP dengan Pendekatan User Acceptance Testing (UAT)
Main Article Content
Abstract
Perkembangan teknologi informasi mendorong organisasi untuk mengintegrasikan seluruh proses bisnis melalui sistem Enterprise Resource Planning (ERP). Namun, keberhasilan penerapan sistem ERP tidak hanya ditentukan oleh aspek teknis, tetapi juga oleh tingkat penerimaan pengguna terhadap sistem yang dikembangkan. Penelitian ini bertujuan untuk merancang Sistem Informasi ERP dengan pendekatan User Acceptance Testing (UAT) guna memastikan sistem yang dibangun sesuai dengan kebutuhan dan ekspektasi pengguna. Metode penelitian yang digunakan meliputi analisis kebutuhan sistem, perancangan sistem menggunakan pemodelan UML, serta pengujian penerimaan pengguna melalui UAT yang melibatkan pengguna akhir sebagai responden. Pengujian UAT dilakukan untuk mengevaluasi aspek fungsionalitas, kemudahan penggunaan, dan kesesuaian sistem terhadap proses bisnis organisasi. Hasil penelitian menunjukkan bahwa sistem ERP yang dirancang mampu memenuhi kebutuhan pengguna dan memperoleh tingkat penerimaan yang baik berdasarkan hasil UAT. Dengan demikian, pendekatan UAT terbukti efektif dalam mendukung perancangan sistem ERP yang berorientasi pada pengguna serta meningkatkan potensi keberhasilan implementasi sistem dalam organisasi
Article Details
Section
This work is licensed under a Creative Commons Attribution 4.0 International License.
How to Cite
References
Chevalier, J. A., & Mayzlin, D. (2006). The effect of word of mouth on sales: Online book reviews. Journal of Marketing Research, 43(3), 345–354. https://doi.org/10.1509/jmkr.43.3.345
Dennis, A., Wixom, B. H., & Roth, R. M. (2015). Systems Analysis and Design (6th ed.). Wiley.
Han, J., Kamber, M., & Pei, J. (2012). Data Mining: Concepts and Techniques (3rd ed.). Morgan Kaufmann.
ISO/IEC. (2011). ISO/IEC 25010: Systems and Software Quality Requirements and Evaluation (SQuaRE). International Organization for Standardization.
Jianqiang, Z., & Xiaolin, G. (2017). Comparison research on text preprocessing methods on Twitter sentiment analysis. IEEE Access, 5, 2870–2879. https://doi.org/10.1109/ACCESS.2017.2672677
Kohavi, R. (1995). A study of cross-validation and bootstrap for accuracy estimation and model selection. IJCAI Proceedings, 14(2), 1137–1145.
Laudon & laudon 2020, The Role of Information Systems in BusinessFirms Competitiveness: Integrated Review Paper from Business Perspective
Laudon, K. C., & Traver, C. G. (2021). E-Commerce 2021: Business, Technology, Society. Pearson Education.
Monk, E., & Wagner, B. (2013). Concepts in Enterprise Resource Planning (4th ed.). Cengage Learning.Cambria, E., Schuller, B., Liu, B., Wang, H., &
O’Brien, J. A., & Marakas, G. M. (2011). Management Information Systems (10th ed.). McGraw-Hill.
Pressman, R. S., & Maxim, B. R. (2020). Software Engineering: A Practitioner’s Approach (9th ed.). McGraw-Hill Education.
Satzinger, J. W., Jackson, R. B., & Burd, S. D. (2016). Systems Analysis and Design in a Changing World (7th ed.). Cengage Learning.
Somers, T. M., & Nelson, K. (2004). A taxonomy of players and activities across the ERP project life cycle. Information & Management, 41(3), 257–278.
Sommerville, I. (2016). Software Engineering (10th ed.). Pearson Education.
Taboada, M., Brooke, J., Tofiloski, M., Voll, K., & Stede, M. (2011). Lexicon-based methods for sentiment analysis. Computational Linguistics, 37(2), 267–307. https://doi.org/10.1162/COLI_a_00049
Turney, P. D. (2002). Thumbs up or thumbs down? Semantic orientation applied to unsupervised classification of reviews. Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics (ACL), 417–424. https://doi.org/10.3115/1073083.1073153
Vinodhini, G., & Chandrasekaran, R. M. (2012). Sentiment analysis and opinion mining: A survey. International Journal of Advanced Research in Computer Science and Software Engineering, 2(6), 282–292.
Zhang, L., Wang, S., & Liu, B. (2018). Deep learning for sentiment analysis: A survey. Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery, 8(4), e1253. https://doi.org/10.1002/widm.1253
Zhang, Y., & Wallace, B. (2017). A sensitivity analysis of (and practitioners’ guide to) convolutional neural networks for sentence classification. arXiv preprint arXiv:1510.03820. https://arxiv.org/abs/1510.03820