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

Edi Widodo, Rastri Prathivi, Soiful Hadi

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.

Keywords


Programming Language;MABAC;Tiobe Index;Ranking

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).




DOI: http://dx.doi.org/10.26623/transformatika.v21i2.7001

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Jurnal Transformatika : Journal Information Technology  by  Department of Information Technology, Faculty of Information Technology and Communication, Semarang University  is licensed under a  Creative Commons Attribution 4.0 International License.