Analisis Sentimen Sistem Ganjil Genap di Tol Bekasi Menggunakan Algoritma Support Vector Machine

Heru Sukma Utama; Didi Rosiyadi; Bobby Suryo Prakoso; Dedi Ariadarma

doi:10.29207/resti.v3i2.1050

Heru Sukma Utama PASCA SARJANA STMIK NUSA MANDIRI
Didi Rosiyadi STMIK Nusa Mandiri Jakarta
Bobby Suryo Prakoso STMIK Nusa Mandiri Jakarta
Dedi Ariadarma LIPI

DOI: https://doi.org/10.29207/resti.v3i2.1050

Keywords: analysis, odd, even, SVM, Text Mining, analisis, ganjil, genap, SVM, Text Mining

Abstract

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.

Downloads

Download data is not yet available.

References

[1] L. Oktasari, Y. H. Chrisnanto, and R. Yuniarti, “Text Mining Dalam Analisis Sentimen Asuransi Menggunakan Metode Niave Bayes Classifier,” Pros. SNST, 2016.
[2] E. Indrayuni, “Analisa Sentimen Review Hotel Menggunakan Algoritma Support Vector Machine Berbasis Particle Swarm Optimization,” J. Evolusi Vol. 4 Nomor 2 - 2016, 2016.
[3] J. Ipmawati, Kusrini, and E. Taufiq Luthfi, Komparasi Teknik Klasifikasi Teks Mining Pada Analisis Sentimen, vol. 6, no. 1. 2017.
[4] I. F. Rozi, S. Hadi, and E. Achmad, “Implementasi Opinion Mining ( Analisis Sentimen ) untuk Ekstraksi Data Opini Publik pada Perguruan Tinggi,” vol. 6, no. 1, pp. 37–43, 2012.
[5] Rafi Saumi Rustian, “Apa itu Sosial Media,” 01 maret 2012, 2012.
[6] “Klasifikasi Posting Twitter Kemacetan Lalu Lintas Kota Bandung Menggunakan Naive Bayesian Classification,” IJCCS (Indonesian J. Comput. Cybern. Syst., 2013.
[7] N. Winarsih and J. Kusumaningrum, “Analisis Kapasitas Gerbang Tol Karawang Barat,” Psikologi, Ekon. Sastra, Arsit. Tek. Sipil, 2013.
[8] N. Y. A. Faradhillah, R. P. Kusumawardani, and I. Hafidz, “Eksperimen Sistem Klasifikasi Analisa Sentimen Twitter pada Akun Resmi Pemerintah Kota Surabaya Berbasis Pembelajaran Mesin,” Pros. Semin. Nas. Sist. Inf. Indones. 2016, 2016.
[9] P. Pascasarjana and U. Udayana, “Text mining dengan metode naïve bayes classifier dan support vector machines untuk sentiment analysis,” Univ. Stuttgart, 2011.
[10] A. Fathan Hidayatullah, M. Rifqi Ma, and arif Program Studi Manajemen Informatika STMIK Jenderal Achmad Yani Yogyakarta Jl Ringroad Barat, “Penerapan Text Mining dalam Klasifikasi Judul Skripsi,” Semin. Nas. Apl. Teknol. Inf. Agustus, 2016.
[11] S. Budi, “Text Mining Untuk Analisis Sentimen Review Film Menggunakan Algoritma K-Means,” Techno.Com, 2017.
[12] U. Pauziah and D. Michael Sonny, “Kajian Algoritma Naive Bayes Dalam pemilihan Penerimaan Beasiswa Tingkat SMA,” Semin. Nas. Teknol. Inf. dan Multimed., 2014.
[13] D. Dwinavinta, C. Nugraha, M. Fahmi, Z. Naimah, and N. Setiani, “Klasterisasi Judul Buku dengan Menggunakan Metode K-Means,” Semin. Nas. Apl. Teknol. Inf. Yogyakarta, 2014.
[14] 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, 2015.
[15] andi nurul Hidayat, “Analisis Sentimen Terhadap Wacana Politik Pada Media Masa Online Menggunakan Algoritma Support Vector Machine Dan Naive Bayes,” J. Elektron. Sistim Inf. Dan Komput., 2015.
[16] A. S. Nugroho, “Pengantar Support Vector Machine ∗,” J. Data Mining, Jakarta, 2007.
[17] M. Abdi, D. Herumurti, and I. Kuswardayan, “Analisis Perbandingan Kecerdasan Buatan pada Computer Player dalam Mengambil Keputusan pada Game Battle RPG,” JUTI J. Ilm. Teknol. Inf., 2017.
[18] A. S. Nugroho, A. B. Witarto, and D. Handoko, “Support Vector Machine – Teori dan Aplikasinya dalam Bioinformatika,” Kuliah Umum IlmuKomputer.Com, 2003.
[19] I. Mathilda Yulietha and S. Al Faraby, “Klasifikasi Sentimen Review Film Menggunakan Algoritma Support Vector Machine,” e-Proceeding Eng., vol. 4, no. 3, pp. 4740–4750, 2017.
[20] S. Chormunge and S. Jena, “Efficient feature subset selection algorithm for high dimensional data,” Int. J. Electr. Comput. Eng., vol. 6, no. 4, pp. 1880–1888, 2016.
[21] L. Dini Utami and R. S. Wahono, “Integrasi Metode Information Gain Untuk Seleksi Fitur dan Adaboost Untuk Mengurangi Bias Pada Analisis Sentimen Review Restoran Menggunakan Algoritma Naïve Bayes,” J. Intell. Syst., vol. 1, no. 2, pp. 120–126, 2015.
[22] N. Krisandi, B. Prihandono, and Helmi, “Algoritma K - Nearest Neighbor Dalam Klasifikasi Data Hasil Produksi Kelapa Sawit Pada PT. Minamas Kecamatan Parindu,” Bul. Ilm. Math.Stat.dan Ter., 2013.
[23] N. Hermaduanti and S. Kusumadewi, “Sistem Pendukung Keputusan Berbasis Sms Untuk Menentukan Status Gizi Dengan Metode K- Nearest Neighbor,” Semin. Nas. Apl. Teknol. Inf. ISSN 1907-5022, 2008.
[24] C. Darujati, “Perbandingan Klasifikasi Dokumen Teks Menggunakan Metode Naïve Bayes Dengan K-Nearest Neighbor Abstrak,” Univ. Stuttgart, 2010.