SENTIMENT ANALYSIS WITH A CASE STUDY OF PRACTICE CARD ON TWITTER SOCIAL MEDIA USING NAIVE BAYES METHOD
Abstract
-In early March 2022, Indonesia experienced a coronavirus pandemic which caused COVID-19 to enter for the first time. Since then, all sectors have been affected by the COVID-19 pandemic, not only health, the economic sector has also been seriously affected by this pandemic. In overcoming employment problems, the government makes a policy of the Pre-Employment Card program. The Pre-Employment Card Program is one of the government's efforts to expand job opportunities and to increase competitiveness which later became one of the social assistance for the community to overcome the Covid-19 pandemic. The implementation of the Pre-Employment Card program received pros and cons from the community, one of which was on Twitter social media. The results of the sentiment analysis of the pre-employment card program are mostly positive. The test results show that the Naïve Bayes Classifier method is successful in classifying sentiment with the highest accuracy value of 96%, the highest precision value of 98%, and the highest recall value of 96%, and AUC of 96%.
References
Ananda, F. D., & Pristyanto, Y. (2021). Analisis Sentimen Pengguna Twitter Terhadap Layanan Internet Provider Menggunakan Algoritma Support Vector Machine. MATRIK : Jurnal Manajemen, Teknik Informatika Dan Rekayasa Komputer. https://doi.org/10.30812/matrik.v20i2.1130
Artha, E. U., Dahlan, A., Informatika, J. T., Teknik, F., Magelang, U. M., Informasi, J. S., & Catur, C. (2018). Klasifikasi Model Percakapan Twitter Mengenai Ujian Nasional. JPIT, 03(01), 121–125.
Buntoro, G. A. (2017). Analisis Sentimen Calon Gubernur DKI Jakarta 2017 Di Twitter. Integer Journal.
Fikri, M. I., Sabrila, T. S., & Azhar, Y. (2020). Perbandingan Metode Naïve Bayes dan Support Vector Machine pada Analisis Sentimen Twitter. SMATIKA JURNAL. https://doi.org/10.32664/smatika.v10i02.455
Khalimi, A. M. (2020). Perhitungan Confusion Matrix Multi-Class Clasification 3x3.
Laksono, R. A., Sungkono, K. R., Sarno, R., & Wahyuni, C. S. (2019). Sentiment Analysis of Restaurant Customer Reviews on TripAdvisor using Naive Bayes. 2019 12th International Conference on Information & Communication Technology and System (ICTS), 49–54.
Muis, A., & Muhammad, F. (2023). Pelatihan Text Mining Menggunakan Bahasa Pemrograman Python. Abdimas Langkanae, 3(1), 36–46.
Pandhu, A., & Diki, W. (2020). Analisa sentimen dan Klasifikasi Komentar Positif Pada Twitter dengan Naïve Bayes Classification. BRITech (Jurnal Imiah Komputer, Sains Dan Teknologi Terapan.
Perekonomian, K. (2020). Kumpulan Peraturan Kredit Usaha Rakyat (KUR). Siaran Pers No. HM.4.6/11/SET.M.EKON.2.3/01/2020.
Priyanto, A., & Ma'arif, M. R. (2018). Implementasi Web Scrapping dan Text Mining untuk Akuisisi dan Kategorisasi Informasi dari Internet (Studi Kasus: Tutorial Hidroponik). Indonesian Journal of Information Systems, 1(1), 25-33.
Rosdiana, R., Eddy, T., Zawiyah, S., & Muhammad, N. Y. U. (2019). Analisis Sentimen pada Twitter terhadap Pelayanan Pemerintah Kota Makassar. Proceeding SNTEI.
Ruhyana, N. (2019). Analisis Sentimen terhadap Penerapan Sistem Plat Nomor Ganjil/Genap pada Twitter dengan Metode Klasifikasi Naive Bayes. Jurnal IKRA-ITH Informatika.
Salim, S. S., & Mayary, J. (2020). ANALISIS SENTIMEN PENGGUNA TWITTER TERHADAP DOMPET ELEKTRONIK DENGAN METODE LEXICON BASED DAN K – NEAREST NEIGHBOR. Jurnal Ilmiah Informatika Komputer. https://doi.org/10.35760/ik.2020.v25i1.2411
Sanusi, R., Astuti, F. D., & Buryadi, I. Y. (2021). Analisis sentimen pada twitter terhadap program kartu pra kerja dengan recurrent neural network. JIKO, 5(2), 89–99.
Watrianthos, R., Suryadi, S., Irmayani, D., Nasution, M., & Simanjorang, E. F. S. (2019). Sentiment Analysis Of Traveloka App Using Naïve Bayes Classifier Method, 8(07), 786–788.
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