ANALISIS SENTIMEN PENGGUNA MEDIA SOSIAL TERHADAP APLIKASI M-HEALTH PEDULI LINDUNGI DENGAN METODE LEXICON BASED DAN NAÏVE BAYES

Authors

  • Riky Iskandar Syah Universitas Islam Madura
  • Hoiriyah Hoiriyah Universitas Islam Madura
  • Miftahul Walid Universitas Islam Madura

DOI:

https://doi.org/10.21927/ijubi.v6i1.3275

Keywords:

pedulilindungi, satusehat, lexicon based, naïve bayes

Abstract

Pedulilindungi atau satusehat merupakan aplikasi yang dirilis secara resmi guna menangani penyebaran virus Covid-19 dan vaksinasi. Namun, dikarenakan suatu insiden besarnya kebocoran data pribadi, terutama identitas pribadi, kepercayaan masyarakat terhadap aplikasi tersebut sangat rendah. Untuk mengetahui pendapat masyarakat saat ini maka dilakukanlah penelitian dengan mengkombinasikan metode Lexicon Based dan Naïve Bayes. Hasil klasifikasi sentiment memperoleh nilai yaitu 62% negative, 32% netral, 6% positif pada Tiktok. 56% negative, 37% netral, 7% positif pada Youtube. 100% positif pada Twitter, dengan jumlah keseluruhan 118 skor negative, 69 skor netral, 113 skor positif, maka dapat disumpulkan masyarakat memiliki opini negative pada aplikasi peduli lindungi. Hasil evaluasi kinerja model memperoleh akurasi 91%, presisi 94%, recall 82%, f1_scores 86% pada Tiktok, pada Youtube akurasi sebesar 90%, presisi 93%, recall 81%, f1_scores 84%. Pada Twitter akurasi 70%, presisi 23%, recall 33%, f1-scores 28%. Pengkombinasian metode Lexicon Based dan Naïve Bayes ini memiliki akurasi yang sangat tinggi pada media sosial Tiktok dan Youtube, sehingga untuk penelitian selanjutnya pada media sosial Twitter perbanyak data yang diambil. Juga penelitian ini diharapkan dapat membantu membangun kembali aplikasi supaya lebih optimal.

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Published

2023-06-30

Issue

Vol. 6 No. 1 (2023): Indonesian Journal of Business Intelligence (IJUBI)

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