Penerapan KNN, DT, dan NB untuk Memprediksi Task Success Developer Berbasis AI-Metrics
DOI:
https://doi.org/10.36050/rsvfdr22Keywords:
Decision Tree, K-Nearest Neighbor, Klasifikasi Keberhasilan Tugas, Metrik Kecerdasan Buatan, Naïve BayesAbstract
This study is motivated by the limited utilization of AI-based metrics to predict task success among
developers in software development projects. The main issue addressed is the absence of a systematic
comparative approach to classification algorithms in identifying the most effective model in this context.
Therefore, this research compares the performance of three classification algorithms—K-Nearest Neighbors
(KNN), Decision Tree (DT), and Naïve Bayes (NB)—in predicting task success using AI-metrics data. The
evaluation metrics include precision, recall, F1-score, and accuracy, presented through classification
reports and confusion matrices. The results show that DT achieved an accuracy of 91%, KNN 92%, and NB
86%. The confusion matrix analysis indicates that DT demonstrates high precision, KNN shows minor
imbalance, and NB struggles to identify minority classes. Additionally, clustering was performed using the
K-Means algorithm and visualized in two dimensions through Principal Component Analysis (PCA), revealing clear segmentation among developer groups. The ultimate benefit of this study is to provide a
foundation for decision-making in selecting the most appropriate algorithm to enhance developer team
effectiveness and personalize managerial strategies. The novelty of this research lies in the combined
application of classification and clustering approaches using AI-metrics to more accurately and data
drivenly identify developer task success.
References
[1] Y. K. Dwivedi et al., “Artificial Intelligence (AI): Multidisciplinary perspectives on emerging challenges, opportunities, and agenda for research, practice and policy,” Int J Inf Manage, vol. 57, Apr. 2021, doi: 10.1016/j.ijinfomgt.2019.08.002.
[2] M. Falahat, S. C. Chong, and C. Liew, “Navigating new product development: Uncovering factors and overcoming challenges for success,” Heliyon, vol. 10, no. 1, Jan. 2024, doi: 10.1016/j.heliyon.2023.e23763.
[3] B. Y. Kassa and E. K. Worku, “The impact of artificial intelligence on organizational performance: The mediating role of employee productivity,” Journal of Open Innovation: Technology, Market, and Complexity, vol. 11, no. 1, Mar. 2025, doi: 10.1016/j.joitmc.2025.100474.
[4] W. Wysocki, I. Miciuła, and M. Mastalerz, “Classification of Task Types in Software Development Projects,” Electronics (Switzerland), vol. 11, no. 22, Nov. 2022, doi: 10.3390/electronics11223827.
[5] D. Al-Fraihat, Y. Sharrab, A. R. Al-Ghuwairi, H. Alzabut, M. Beshara, and A. Algarni, “Utilizing machine learning algorithms for task allocation in distributed agile software development,” Heliyon, vol. 10, no. 21, Nov. 2024, doi: 10.1016/j.heliyon.2024.e39926.
[6] M. Ángeles López-Cabarcos, P. Vázquez-Rodríguez, and L. M. Quiñoá-Piñeiro, “An approach to employees’ job performance through work environmental variables and leadership behaviours,” J Bus Res, vol. 140, pp. 361–369, Feb. 2022, doi: 10.1016/j.jbusres.2021.11.006.
[7] H. Urbancová, P. Vrabcová, M. Hudáková, and G. J. Petrů, “Effective training evaluation: The role of factors influencing the evaluation of effectiveness of employee training and development,” Sustainability (Switzerland), vol. 13, no. 5, pp. 1–14, Mar. 2021, doi: 10.3390/su13052721.
[8] A. Almalawi, B. Soh, A. Li, and H. Samra, “Predictive Models for Educational Purposes: A Systematic Review,” Dec. 01, 2024, Multidisciplinary Digital Publishing Institute (MDPI). doi: 10.3390/bdcc8120187.
[9] M. Gunawardena, P. Bishop, and K. Aviruppola, “Personalized learning: The simple, the complicated, the complex and the chaotic,” Teach Teach Educ, vol. 139, Mar. 2024, doi: 10.1016/j.tate.2023.104429.
[10] P. Bansal, “AI Pattern Recognition and its Features,” Int J Eng Adv Technol, vol. 14, no. 3, pp. 18–25, Feb. 2025, doi: 10.35940/ijeat.C4562.14030225.
[11] C. Daniel, “Advancements and Challenges in Machine Learning and Artificial Intelligence: Shaping the Future of Technology,” 2024. [Online]. Available: https://www.researchgate.net/publication/377150546
[12] H. Allam, L. Makubvure, B. Gyamfi, K. N. Graham, and K. Akinwolere, “Text Classification: How Machine Learning Is Revolutionizing Text Categorization,” Information (Switzerland), vol. 16, no. 2, Feb. 2025, doi: 10.3390/info16020130.
[13] I. Essamlali, H. Nhaila, and M. El Khaili, “Advances in machine learning and IoT for water quality monitoring: A comprehensive review,” Mar. 30, 2024, Elsevier Ltd. doi: 10.1016/j.heliyon.2024.e27920.
[14] F. Filippucci et al., “The impact of Artificial Intelligence on productivity, distribution and growth: Key mechanisms, initial evidence and policy challenges,” 2024. [Online]. Available: www.oecd.org/termsandconditions
[15] A. Irwin, I. R. Tone, P. Sobocinska, J. Liggins, and S. Johansson, “Thinking five or six actions ahead: Investigating the non-technical skills used within UK forestry chainsaw operations,” Saf Sci, vol. 163, Jul. 2023, doi: 10.1016/j.ssci.2023.106112.
[16] F. Ouyang, M. Wu, L. Zheng, L. Zhang, and P. Jiao, “Integration of artificial intelligence performance prediction and learning analytics to improve student learning in online engineering course,” International Journal of Educational Technology in Higher Education, vol. 20, no. 1, Dec. 2023, doi: 10.1186/s41239-022-00372-4.
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Copyright (c) 2025 Iski Mediansyah, Muhammad Bitrayoga, Arief Zikry, Firza Septian (Author)
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