ANALISIS SENTIMEN PADA LAYANAN APLIKASI GRAB DI PLAY STORE MENGGUNAKAN METODE NAIVE BAYES

KHAIRUNNISA, NPM 2108100031 (2025) ANALISIS SENTIMEN PADA LAYANAN APLIKASI GRAB DI PLAY STORE MENGGUNAKAN METODE NAIVE BAYES. Skripsi thesis, Universitas Labuhanbatu.

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Abstract

Perkembangan aplikasi transportasi online seperti Grab memunculkan beragam ulasan di Play Store yang mencerminkan persepsi pengguna terhadap kualitas layanan, sehingga ulasan ini dapat dijadikan sumber data penting untuk analisis sentimen guna memahami kecenderungan opini masyarakat. Analisis sentimen merupakan teknik text mining yang digunakan untuk mengklasifikasikan opini ke dalam kategori positif, negatif, atau netral, dan metode Naive Bayes dipilih karena sederhana namun efektif dalam memproses teks serta menghasilkan prediksi klasifikasi yang akurat. Penelitian ini menggunakan pendekatan kuantitatif dengan data berupa 2.000 ulasan pengguna Grab dari Play Store yang kemudian diklasifikasikan menggunakan algoritma Naive Bayes melalui tahapan pengumpulan data, preprocessing, klasifikasi, dan evaluasi. Dari hasil klasifikasi diperoleh 1.513 ulasan positif, 151 ulasan netral, dan 336 ulasan negatif yang menunjukkan mayoritas pengguna memberikan tanggapan positif terhadap layanan Grab. Penelitian ini membuktikan bahwa metode Naive Bayes efektif dalam mengklasifikasikan sentimen ulasan pengguna Grab di Play Store, dan temuan ini bermanfaat bagi pengembang aplikasi untuk meningkatkan kualitas layanan berdasarkan data opini masyarakat. Kata kunci: Analisis Sentimen, Naive Bayes, Text Mining, Grab, Ulasan Pengguna ==================================================================================================== The development of online transportation applications such as Grab has given rise to various reviews on the Play Store that reflect user perceptions of service quality. Therefore, these reviews can be used as an important data source for sentiment analysis to understand public opinion trends. Sentiment analysis is a text mining technique used to classify opinions into positive, negative, or neutral categories. The Naive Bayes method was chosen because it is simple yet effective in processing text and producing accurate classification predictions. This study uses a quantitative approach with data in the form of 2,000 Grab user reviews from the Play Store which are then classified using the Naive Bayes algorithm through the stages of data collection, preprocessing, classification, and evaluation. The classification results obtained 1,513 positive reviews, 151 neutral reviews, and 336 negative reviews, indicating that the majority of users gave a positive response to Grab services. This study proves that the Naive Bayes method is effective in classifying the sentiment of Grab user reviews on the Play Store, and these findings are useful for application developers to improve service quality based on public opinion data. Keywords: Sentiment Analysis, Naive Bayes, Text Mining, Grab, User Reviews

Item Type:	Thesis (Skripsi)
Uncontrolled Keywords:	Analisis Sentimen, Naive Bayes, Text Mining, Grab, Ulasan Pengguna=============Sentiment Analysis, Naive Bayes, Text Mining, Grab, User Reviews
Subjects:	Q Science > QA Mathematics > QA75 Electronic computers. Computer science T Technology > T Technology (General) Z Bibliography. Library Science. Information Resources > ZA Information resources > ZA4050 Electronic information resources Z Bibliography. Library Science. Information Resources > ZA Information resources > ZA4450 Databases
Divisions:	Fakultas Sains Dan Teknologi > Teknologi Informasi
Depositing User:	Unnamed user with email repository@ulb.ac.id
Date Deposited:	04 Nov 2025 03:05
Last Modified:	04 Nov 2025 03:05
URI:	http://repository.ulb.ac.id/id/eprint/1917

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