Analisis Sentimen Review Pengguna Layanan Telemedicine Halodoc Menggunakan Algoritma Naive Bayes Classifier dan Support Vector Machine

Cikania, Reynalda Nabila (2021) Analisis Sentimen Review Pengguna Layanan Telemedicine Halodoc Menggunakan Algoritma Naive Bayes Classifier dan Support Vector Machine. Undergraduate thesis, Instistut Teknologi Sepuluh Nopember.

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Abstract

Halodoc merupakan aplikasi layanan kesehatan berbasis
telemedicine yang menghubungkan pasien dengan praktisi kesehatan seperti dokter, apotek, dan laboratorium. Ada beberapa komentar masyarakat pengguna aplikasi Halodoc, baik komentar yang bersifat positif maupun berisfat negatif. Hal ini menunjukkan adanya perhatian masyarakat terhadap aplikasi Halodoc sehingga perlu dilakukan analisis sentimen atau komentar yang muncul pada layanan aplikasi Halodoc terutama di masa pandemi COVID-19 agar layanan aplikasi Halodoc menjadi lebih baik. Algoritma Naïve Bayes Classifier (NBC) dan Support Vector Machine (SVM) digunakan untuk menganalisis sentimen masyarakat pengguna aplikasi layanan telemedicine Halodoc. Hasil
klasifikasi sentimen kategori negatif sebesar 12,33%, sedangkan sentimen kategori positif adalah 87,67% dari 5.687 review yang artinya sentimen review positif lebih banyak daripada sentimen review negatif. Performa ketepatan klasifikasi Algoritma Naive Bayes Classifier menghasilkan tingkat akurasi sebesar 87,77% dengan nilai AUC sebesar 57,11% dan G�Mean sebesar 40,08%, sedangkan Algoritma SVM dengan Kernel RBF memiliki nilai akurasi sebesar 86,1% dengan nilai AUC sebesar 60,149% dan nilai G-Mean sebesar 49,311%. Berdasarkan nilai akurasi model dapat diketahui model SVM Kernel RBF lebih baik daripada NBC pada pengklasifikasian review sentimen pengguna layanan telemedicine Halodoc.
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Halodoc is a telemedicine-based healthcare application that connects patients with health practitioners such as doctors, pharmacies, and laboratories. There are some comments from halodoc users, both positive and negative comments. This indicates the public's concern for the Halodoc application so it is necessary to analyze the sentiment or comments that appear on the Halodoc application service, especially during the COVID-19 pandemic in order for Halodoc application services to be better. The Naïve Bayes Classifier (NBC) and Support Vector Machine (SVM) algorithms are used to analyze the public sentiment of Halodoc's telemedicine service application users. The negative category sentiment classification result was 12.33%, while the positive category sentiment was 87.67% from 5,687 reviews which means that the positive review sentiment is more than the negative review sentiment. The accuracy performance of the Naive Bayes Classifier Algorithm resulted in an accuracy rate of 87.77% with an AUC value of 57.11% and a G-Mean of 40.08%, while svm algorithm with KERNEL RBF had an accuracy value of 86.1% with an AUC value of 60.149% and a G-Mean value of 49.311%. Based on the accuracy value of the model can be known SVM Kernel RBF model better than NBC on classifying the review of user sentiment of halodoc telemedicine service.

Item Type:	Thesis (Undergraduate)
Uncontrolled Keywords:	COVID – 19, Halodoc, Naïve Bayes Classifier, Support Vector Machine
Subjects:	H Social Sciences > HD Industries. Land use. Labor > HD108 Classification (Theory. Method. Relation to other subjects )
Divisions:	Faculty of Vocational > 49501-Business Statistics
Depositing User:	REYNALDA NABILA CIKANIA
Date Deposited:	19 Aug 2021 02:21
Last Modified:	19 Aug 2021 02:21
URI:	http://repository.its.ac.id/id/eprint/87562

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