Analisis Sentimen Sistem Ganjil Genap di Tol Bekasi Menggunakan Algoritma Support Vector Machine
Analysis of the odd even-numbered sentiment systems in Bekasi toll using the Support Vector Machine Algorithm, is a process of understanding, extracting, and processing textual data automatically from social media. The purpose of this study was to determine the level of accuracy, recall and precisio...
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| Format: | Article |
| Language: | English |
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Ikatan Ahli Informatika Indonesia
2019-08-01
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| Series: | Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) |
| Subjects: | |
| Online Access: | http://iaii.or.id/jurnal/index.php/RESTI/article/view/1050 |
| _version_ | 1827363492696424448 |
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| author | Heru Sukma Utama Didi Rosiyadi Bobby Suryo Prakoso Dedi Ariadarma |
| author_facet | Heru Sukma Utama Didi Rosiyadi Bobby Suryo Prakoso Dedi Ariadarma |
| author_sort | Heru Sukma Utama |
| collection | DOAJ |
| description | Analysis of the odd even-numbered sentiment systems in Bekasi toll using the Support Vector Machine Algorithm, is a process of understanding, extracting, and processing textual data automatically from social media. The purpose of this study was to determine the level of accuracy, recall and precision of opinion mining generated using the Support Vector Machine algorithm to provide information community sentiment towards the effectiveness of the odd system of Bekasi tiolls on social media. The research method used in this study was to do text mining in comments-comments regarding posts regarding even odd oddities on Bekasi toll on Twitter, Instagram, Youtube and Facebook. The steps taken are starting from preprocessing, transformation, datamining and evaluation, followed by information gaon feature selection, select by weight and applying SVM Algorithm model. The results obtained from the study using the SVM model are obtained Confusion Matrix result, namely accuracyof 78.18%, Precision of 74.03%, and Sensitivity or Recall of 86.82%. Thus this study concludes that the use of Support Vector Machine Algorithms can analyze even odd sentiments on the Bekasi toll road. |
| first_indexed | 2024-03-08T07:50:53Z |
| format | Article |
| id | doaj.art-73aa6f3fcbc1485c9c0ad303652af528 |
| institution | Directory Open Access Journal |
| issn | 2580-0760 |
| language | English |
| last_indexed | 2024-03-08T07:50:53Z |
| publishDate | 2019-08-01 |
| publisher | Ikatan Ahli Informatika Indonesia |
| record_format | Article |
| series | Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) |
| spelling | doaj.art-73aa6f3fcbc1485c9c0ad303652af5282024-02-02T14:50:50ZengIkatan Ahli Informatika IndonesiaJurnal RESTI (Rekayasa Sistem dan Teknologi Informasi)2580-07602019-08-013224325010.29207/resti.v3i2.10501050Analisis Sentimen Sistem Ganjil Genap di Tol Bekasi Menggunakan Algoritma Support Vector MachineHeru Sukma Utama0Didi Rosiyadi1Bobby Suryo Prakoso2Dedi Ariadarma3PASCA SARJANA STMIK NUSA MANDIRISTMIK Nusa Mandiri JakartaSTMIK Nusa Mandiri JakartaLIPIAnalysis of the odd even-numbered sentiment systems in Bekasi toll using the Support Vector Machine Algorithm, is a process of understanding, extracting, and processing textual data automatically from social media. The purpose of this study was to determine the level of accuracy, recall and precision of opinion mining generated using the Support Vector Machine algorithm to provide information community sentiment towards the effectiveness of the odd system of Bekasi tiolls on social media. The research method used in this study was to do text mining in comments-comments regarding posts regarding even odd oddities on Bekasi toll on Twitter, Instagram, Youtube and Facebook. The steps taken are starting from preprocessing, transformation, datamining and evaluation, followed by information gaon feature selection, select by weight and applying SVM Algorithm model. The results obtained from the study using the SVM model are obtained Confusion Matrix result, namely accuracyof 78.18%, Precision of 74.03%, and Sensitivity or Recall of 86.82%. Thus this study concludes that the use of Support Vector Machine Algorithms can analyze even odd sentiments on the Bekasi toll road.http://iaii.or.id/jurnal/index.php/RESTI/article/view/1050analysis, odd, even, SVM, Text Mininganalisis, ganjil, genap, SVM, Text Mining |
| spellingShingle | Heru Sukma Utama Didi Rosiyadi Bobby Suryo Prakoso Dedi Ariadarma Analisis Sentimen Sistem Ganjil Genap di Tol Bekasi Menggunakan Algoritma Support Vector Machine Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) analysis, odd, even, SVM, Text Mining analisis, ganjil, genap, SVM, Text Mining |
| title | Analisis Sentimen Sistem Ganjil Genap di Tol Bekasi Menggunakan Algoritma Support Vector Machine |
| title_full | Analisis Sentimen Sistem Ganjil Genap di Tol Bekasi Menggunakan Algoritma Support Vector Machine |
| title_fullStr | Analisis Sentimen Sistem Ganjil Genap di Tol Bekasi Menggunakan Algoritma Support Vector Machine |
| title_full_unstemmed | Analisis Sentimen Sistem Ganjil Genap di Tol Bekasi Menggunakan Algoritma Support Vector Machine |
| title_short | Analisis Sentimen Sistem Ganjil Genap di Tol Bekasi Menggunakan Algoritma Support Vector Machine |
| title_sort | analisis sentimen sistem ganjil genap di tol bekasi menggunakan algoritma support vector machine |
| topic | analysis, odd, even, SVM, Text Mining analisis, ganjil, genap, SVM, Text Mining |
| url | http://iaii.or.id/jurnal/index.php/RESTI/article/view/1050 |
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