Evaluating the Popularity of Programming Languages in Indonesia using the MABAC Method

Authors

  • Edi Widodo Universitas Semarang
  • Rastri Prathivi Universitas Semarang
  • Soiful Hadi Universitas Semarang

DOI:

https://doi.org/10.26623/transformatika.v21i2.7001

Keywords:

Programming Language, MABAC, Tiobe Index, Ranking

Abstract

In today's fast-paced digital era, the selection of a programming language plays a crucial role in the success of software development projects. This research aims to create an index of popularity for programming languages using the multi-attributive border approximation area comparison (MABAC) method. The study considers four data sources, including Jobstreet.Com, LinkedIn.Com, Google Trends, and Tiobe.com, to obtain the necessary information for evaluating the popularity of programming languages in Indonesia. The data range for this study is from May 1, 2020, until April 31, 2021. The results of the study indicate that the top ten programming languages in terms of popularity in Indonesia are Java, SQL, php, JavaScript, C, C++, python, C#, Visual Basic, and Assembly. The index can serve as a useful guide for strategic decision-making regarding the selection of programming languages for addressing the needs of the information technology market in Indonesia. The study's findings can be useful for software developers, IT professionals, and decision-makers in organizations who need to select a programming language for their software projects in Indonesia. The MABAC method used in this study can also be applied to other contexts for evaluating the popularity of programming languages.

References

Bissyande TF, Thung F, Lo D, Jiang L, Reveillere L. Popularity, interoperability, and impact of programming languages in 100,000 open-source projects. Proceedings - International Computer Software and Applications Conference, IEEE Computer Society; 2013, p. 303–12. https://doi.org/10.1109/COMPSAC.2013.55.

Barua A, Thomas SW, Hassan AE. What are developers talking about? An analysis of topics and trends in Stack Overflow. Empirical Software Engineering 2014; 19:619–54. https://doi.org/10.1007/s10664-012-9231-y.

Swaroop. How accurate are the language ratings published in the TIOBE index. Quora 2021. https://qr.ae/pGBw16 (accessed November 18, 2021).

Alinezhad A, Javad K. New Methods and Applications in Multiple Attribute Decision Making (MADM). MABAC Method. In: New Methods and Applications in Multiple Attribute Decision Making (MADM). International Series in Operations Research & Management Science, vol 277, Springer, Cam.; 2019, p. 193–8. https://doi.org/10.1007/978-3-030-15009-9_25.

Stević Ž, Pamučar D, Vasiljević M, Stojić G, Korica S. Novel integrated multi-criteria model for supplier selection: Case study construction company. Symmetry (Basel) 2017;9. https://doi.org/10.3390/sym9110279.

Wang J, Wei G, Wei C, Wei Y. MABAC method for multiple attribute group decision making under q-rung orthopair fuzzy environment. Defence Technology 2020; 16:208–16. https://doi.org/10.1016/j.dt.2019.06.019.

Pamučar D, Stević Ž, Zavadskas EK. Integration of interval rough AHP and interval rough MABAC methods for evaluating university web pages. Applied Soft Computing Journal 2018; 67:141–63. https://doi.org/10.1016/j.asoc.2018.02.057.

Xue-Guo X, Shi H, Li-Jun Z, Hu-Chen L. Green supplier evaluation and selection with an extended MABAC method under the heterogeneous information environment. Sustainability (Switzerland) 2019;11. https://doi.org/10.3390/su11236616.

Widodo E, Hadi S. Analisis Popularitas Bahasa Pemrograman Menggunakan Metode Simpe Multi Attribure Rating Technique (SMART) Untuk Menentukan Pilihan Dalam Mempelajari Bahasa Pemrograman. E-PROSIDING SEMINAR NASIONAL HASIL PENELITIAN LPPM UNIVERSITAS SEMARANG 2021:301–5.

Wdzięczna D. SQL Pattern Matching 2017. https://learnsql.com/blog/using-like-match-patterns-sql/ (accessed June 7, 2021).

Downloads

Published

2023-08-10

Issue

Vol. 21 No. 1 (2023): July 2023

Section

Artikel

License

Authors who publish with this journal agree to the following terms:

  1. Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under a Creative Commons Attribution License that allows others to share the work with an acknowledgement of the work's authorship and initial publication in this journal.
  2. Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgement of its initial publication in this journal.
  3. Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work.

Creative Commons License
Transformatika is licensed under a  Creative Commons Attribution 4.0 International License.