Analisa Sentimen Pemilu 2019 Pada Judul Berita Online Menggunakan Metode Logistic Regression

Alifiyah Rohmatul Hidayati(1*), Arief Senja Fitrani(2), Mochamad Alfan Rosid(3),

(1) Universitas Muhammadiyah Sidoarjo, Sidoarjo, Indonesia
(2) Universitas Muhammadiyah Sidoarjo, Sidoarjo, Indonesia
(3) Universitas Muhammadiyah Sidoarjo, Sidoarjo, Indonesia
(*) Corresponding Author

Abstract


Online news is a report that discusses an event packaged by the media as a means of publication in the form of news that can be accessed online. The 2019 election was one of the topics that was very much discussed at that time. In its implementation, the 2019 election reaped many critical notes related to the implementation and the issue of the integrity of the election itself. In this study, researchers took titles from various online news portals related to the 2019 election for sentiment analysis. The classification process is divided into three classes, namely positive, neutral and negative. The data used in this study amounted to 395 records. The stages carried out in this study are preprocessing which includes casefolding, remove punctuation, handling whitespace, stopword removal, stemming and tekonization. Next is handling imbalanced data to balance the number of classes. After going through the preprocessing stage, the next is the processing stage using the logistic regression method and randomized search cross validations. This is used to find the best parameters where the results of these parameters are then carried out by fitting the model. The results of the combined logistic regression method and randomized search cross validation show an accuracy score of 86%.

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References


L. D. Mahbubah and E. Zuliarso, “Analisa Sentimen Twitter Pada Pilpres 2019 Menggunakan Algoritma Naive Bayes,” Sintak, 2019.

M. K. Anam, B. N. Pikir, and M. B. Firdaus, “Penerapan Na ̈ıve Bayes Classifier, K-Nearest Neighbor (KNN) dan Decision Tree untuk Menganalisis Sentimen pada Interaksi Netizen danPemeritah,” MATRIK : Jurnal Manajemen, Teknik Informatika dan Rekayasa Komputer, vol. 21, no. 1, 2021, doi: 10.30812/matrik.v21i1.1092.

vincent michael, “Machine Learning: Mengenal Logistic Regression,” https://vincentmichael089.medium.com/machine-learning-2-logistic-regression-96b3d4e7b603, May 09, 2019.

J. Nasional, S. Informasi, H. Hakim, and S. Agustian, “Pebandingan Metode Decision Tree dan XGBoost untuk Klasifikasi Sentimen Vaksin Covid-19 di Twitter,” vol. 03, pp. 107–114, 2022.

Y. S. Nugroho and N. Emiliyawati, “Sistem klasifikasi variabel tingkat penerimaan konsumen terhadap mobil menggunakan metode random forest,” Jurnal Teknik Elektro, vol. 9, no. 1, pp. 24–29, 2017.

E. Fitri, “Analisis Sentimen Terhadap Aplikasi Ruangguru Menggunakan Algoritma Naive Bayes, Random Forest Dan Support Vector Machine,” Jurnal Transformatika, vol. 18, no. 1, p. 71, 2020, doi: 10.26623/transformatika.v18i1.2317.

A. Guterres, Gunawan, and J. Santoso, “Stemming Bahasa Tetun Menggunakan Pendekatan Rule Based,” Teknika, vol. 8, no. 2, 2019, doi: 10.34148/teknika.v8i2.224.

N. N. Pandika Pinata, I. M. Sukarsa, and N. K. Dwi Rusjayanthi, “Prediksi Kecelakaan Lalu Lintas di Bali dengan XGBoost pada Python,” Jurnal Ilmiah Merpati (Menara Penelitian Akademika Teknologi Informasi), vol. 8, no. 3, p. 188, 2020, doi: 10.24843/jim.2020.v08.i03.p04.

U. Erdiansyah, A. Irmansyah Lubis, and K. Erwansyah, “Komparasi Metode K-Nearest Neighbor dan Random Forest Dalam Prediksi Akurasi Klasifikasi Pengobatan Penyakit Kutil,” Jurnal Media Informatika Budidarma, vol. 6, no. 1, p. 208, 2022, doi: 10.30865/mib.v6i1.3373.

M. Rizky Mubarok, Muliadi, and R. Herteno, “Hyper-Parameter Tuning pada XGBoost Untuk Prediksi Keberlangsungan Hidup Pasien Gagal Jantung,” Kumpulan Jurnal Ilmu Komputer (KLIK), vol. 9, no. 2, pp. 391–401, 2022.

B. P. Pratiwi, A. S. Handayani, and S. Sarjana, “Pengukuran Kinerja Sistem Kualitas Udara Dengan Teknologi Wsn Menggunakan Confusion Matrix,” Jurnal Informatika Upgris, vol. 6, no. 2, 2021, doi: 10.26877/jiu.v6i2.6552.

S. Keputusan Dirjen Penguatan Riset dan Pengembangan Ristek Dikti, A. Nikmatul Kasanah, U. Pujianto, T. Elektro, F. Teknik, and U. Negeri Malang, “Terakreditasi SINTA Peringkat 2 Penerapan Teknik SMOTE untuk Mengatasi Imbalance Class dalam Klasifikasi Objektivitas Berita Online Menggunakan Algoritma KNN,” masa berlaku mulai, vol. 1, no. 3, pp. 196–201, 2017.

E. Agustin, A. Eviyanti, and N. Lutvi Azizah, “Deteksi Penyakit Epilepsi Melalui Sinyal EEG Menggunakan Metode DWT dan Extreme Gradient Boosting,” vol. 7, no. 1, pp. 117–127, 2023, doi: 10.30865/mib.v7i1.5412.




DOI: https://doi.org/10.30645/kesatria.v4i2.164

DOI (PDF): https://doi.org/10.30645/kesatria.v4i2.164.g163

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