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
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.
Downloads
References
M. A. Jaya, R. Ferdiana, and S. Fauziati, “Analisis Faktor keberhasilan Startup Digital di Yogyakarta,” in Prosiding SNATIF, 2017, vol. 4, no. 1, pp. 167–173.
M. D. K. Perdani, Widyawan, and P. I. Santoso, “Faktor-faktor yang mempengaruhi pertumbuhan startup di yogyakarta,” in Seminar Nasional Teknologi Informasi dan Komunikasi 2018, 2018, vol. 2018, no. Sentika, pp. 23–24.
T. Koch, C. Gerber, and J. J. De Klerk, “The impact of social media on recruitment: Are you Linkedin?,” SA J. Hum. Resour. Manag., vol. 16, pp. 1–14, 2018.
C. Tu, Z. Liu, H. Luan, and M. Sun, “PRISM: Profession identification in social media,” ACM Trans. Intell. Syst. Technol., vol. 8, no. 6, 2017.
Purwono, R. Umar, and I. Riadi, “Perancangan Indikator Kinerja Programmer pada Aktivitas Media Sosial,” J. Fasilkom UMB, 2020.
H. Aliady et al., “Implementasi Support Vector Machine ( Svm ) Dan Random Forest pada Diagnosis Kanker Payudara,” Semin. Nas. Teknol. Inf. dan Komun. 2018 (SENTIKA 2018), vol. 2018, no. Sentika, pp. 278–285, 2018.
D. Maulina and R. Sagara, “Klasifikasi Artikel Hoax Menggunakan Support Vector Machine Linear Dengan Pembobotan Term Frequency – Inverse Document Frequency,” Mantik Penusa, vol. 2, no. 1, pp. 35–40, 2018.
I. Oktanisa and A. A. Supianto, “Perbandingan Teknik Klasifikasi Dalam Data Mining Untuk Bank a Comparison of Classification Techniques in Data Mining for,” Teknol. Inf. dan Ilmu Komput., vol. 5, no. 5, pp. 567–576, 2018.
D. Ariadi and K. Fithriasari, “Klasifikasi Berita Indonesia Menggunakan Metode Naive Bayesian Classification dan Support Vector Machine dengan Confix Stripping Stemmer,” J. SAINS DAN SENI ITS Vol. 4, No.2, vol. 4, no. 2, pp. 248–253, 2015.
A. Handayanto, K. Latifa, N. D. Saputro, and R. R. Waliyansyah, “Analisis dan Penerapan Algoritma Support Vector Machine ( SVM ) dalam Data Mining untuk Menunjang Strategi Promosi ( Analysis and Application of Algorithm Support Vector Machine ( SVM ) in Data Mining to Support Promotional Strategies ),” JUITA J. Inform., vol. 7, no. November, pp. 71–79, 2019.
A. T. J. H, “Preprocessing Text untuk Meminimalisir Kata yang Tidak Berarti dalam Proses Text Mining,” Inform. UPGRIS, vol. 1, pp. 1–9, 2015.
A. Alajmi and E. mostafa Saad, “Toward an ARABIC Stop-Words List Generation Toward an ARABIC Stop-Words List Generation,” no. January 2012, 2018.
J. Shodiq and L. A. Muharom, “Kategorisasi Dokumen Text Menggunakan Metode K-Nearest Neighbor pada Dokumen Tugas Akhir Universitas Muhammadiyah Jember,” Universitas Muhammadiyah Jember, 2017.
N. K. Widyasanti, I. K. G. D. Putra, and N. K. D. Rusjayanthi, “Seleksi Fitur Bobot Kata dengan Metode TF-IDF untuk Ringkasan Bahasa Indonesia,” Merpati, vol. 6, no. 2, pp. 119–126, 2018.
Suyanto, Machine Learning Tingkat Dasar dan Lanjut. Bandung: Informatika, 2018.
A. S. Ritonga and E. S. Purwaningsih, “Penerapan Metode Support Vector Machine ( SVM ) Dalam Klasifikasi Kualitas Pengelasan Smaw ( Shield Metal Arc Welding ),” Ilm. Edutic, vol. 5, no. 1, pp. 17–25, 2018.
I. Riadi, R. Umar, and F. D. Aini, “Analisis Perbandingan Detection Traffic Anomaly Dengan Metode Naive Bayes Dan Support Vector Machine (Svm),” Ilk. J. Ilm., vol. 11, no. 1, p. 17, 2019.
N. I. Widiastuti, E. Rainarli, and K. E. Dewi, “Peringkasan dan Support Vector Machine pada Klasifikasi Dokumen,” J. Infotel, vol. 9, no. 4, p. 416, 2017.
Suyanto, Machine Learning Tingkat Dasar dan Lanjut. Bandung: Informatika, 2018.
T. H. Apandi, C. A. Sugianto, and C. R. Service, “Algoritma Naive Bayes untuk Prediksi Kepuasan Pelayanan Perekaman e-KTP ( Naive Bayes Algorithm for Satisfaction Prediction of e-ID,” JUITA J. Inform., vol. 7, no. November, pp. 125–128, 2019.
S. Asiyah and K. Fithriasari, “Klasifikasi Berita Online Menggunakan Metode Support Vector Machine Dan K-Nearest Neighbor,” J. Sains dan Seni ITS, vol. 5, no. 2, 2016.
G. Varoquaux, L. Buitinck, G. Louppe, O. Grisel, F. Pedregosa, and A. Mueller, “Scikit-learn: Machine Learning in Python Fabian,” J. Mach. Learn. Res., vol. 19, no. 1, pp. 2825–2830, 2011.
Copyright (c) 2020 Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi)
This work is licensed under a Creative Commons Attribution 4.0 International License.
Copyright in each article belongs to the author
- The author acknowledges that the RESTI Journal (System Engineering and Information Technology) is the first publisher to publish with a license Creative Commons Attribution 4.0 International License.
- Authors can enter writing separately, arrange the non-exclusive distribution of manuscripts that have been published in this journal into other versions (eg sent to the author's institutional repository, publication in a book, etc.), by acknowledging that the manuscript has been published for the first time in the RESTI (Rekayasa Sistem dan Teknologi Informasi) journal ;
