Comparison of SVM, RF and SGD Methods for Determination of Programmer's Performance Classification Model in Social Media Activities

Perbandingan Metode SVM, RF dan SGD untuk Penentuan Model Klasifikasi Kinerja Programmer pada Aktivitas Media Sosial

  • Rusydi Umar Universitas Ahmad Dahlan
  • Imam Riadi Universitas Ahmad Dahlan
  • Purwono Universitas Ahmad Dahlan
Keywords: classification, support vector machine, random forest, stochastic gradient descent, programmer

Abstract

The failure of most startups in Indonesia is caused by team performance that is not solid and competent. Programmers are an integral profession in a startup team. The development of social media can be used as a strategic tool for recruiting the best programmer candidates in a company. This strategic tool is in the form of an automatic classification system of social media posting from prospective programmers. The classification results are expected to be able to predict the performance patterns of each candidate with a predicate of good or bad performance. The classification method with the best accuracy needs to be chosen in order to get an effective strategic tool so that a comparison of several methods is needed. This study compares classification methods including the Support Vector Machines (SVM) algorithm, Random Forest (RF) and Stochastic Gradient Descent (SGD). The classification results show the percentage of accuracy with k = 10 cross validation for the SVM algorithm reaches 81.3%, RF at 74.4%, and SGD at 80.1% so that the SVM method is chosen as a model of programmer performance classification on social media activities.

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Published
2020-04-20
How to Cite
Rusydi Umar, Imam Riadi, & Purwono. (2020). Comparison of SVM, RF and SGD Methods for Determination of Programmer’s Performance Classification Model in Social Media Activities. Jurnal RESTI (Rekayasa Sistem Dan Teknologi Informasi), 4(2), 329 - 335. https://doi.org/10.29207/resti.v4i2.1770
Section
Artikel Teknologi Informasi