Metode Naive Bayes untuk analisis sentimen terhadap tempat wisata

Rahmawati, Aprilia (2022) Metode Naive Bayes untuk analisis sentimen terhadap tempat wisata. Sarjana thesis, UIN Sunan Gunung Djati Bandung.

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Abstract

Text mining adalah awal dari analisis sentiment. Dimana text mining adalah proses mengambil informasi dari sebuah text. Sedangkan analisis sentiment sendiri yaitu proses menganalisis data berupa teks untuk mengetahui suatu kalimat mengandung sentiment positif, atau negatif. Pada penelitian ini, dibuat suatu sistem yang dapat mengklasifikasi tweet yang berisi opini terkait tempat wisata di Kabupaten Bandung Barat dimana opini tersebut akan berisi opini positif dan negatif. Proses klasifikasi dilakukan dengan menggunakan algoritma Naïve Bayes dengan menerapkan metode Cross Industry Standard Process for Data Mining (CRISP-DM). Dengan begitu akan didapatkan bagaimana pandangan orang-orang terhadap tempat wisata di Kabupaten Bandung Barat. Berdasarkan penghitungan hasil klasifikasi dari 250 data yang terdiri atas data sentiment positif dan negatif, dibuat menjadi enam skenario. Dari hasil skenario-skenario tersebut skenario 6 memiliki nilai f1-score tertinggi, yaitu 0.828 atau 82.8%. Hasil ini menjelaskan algoritma naïve bayes bekerja cukup baik dalam proses analisis sentiment. Text mining is the beginning of sentiment analysis. Where text mining is the process of retrieving information from a text. Meanwhile, sentiment analysis is the process of analyzing data in the form of text to find out if a sentence contains positive or negative sentiments. In this study, a system was created that can classify tweets containing opinions related to tourist attractions in West Bandung Regency where these opinions will contain positive and negative opinions. The classification process is carried out using the Naïve Bayes algorithm by applying the Cross Industry Standard Process for Data Mining (CRISP-DM) method. That way you will get how people view tourist attractions in West Bandung Regency. Based on the calculation of the classification results from 250 data consisting of positive and negative sentiment data, six scenarios were made. From the results of these scenarios, scenario 6 has the highest f1-score, which is 0.828 or 82.8%. These results explain that the nave Bayes algorithm works quite well in the sentiment analysis process.

Item Type: Thesis (Sarjana)
Uncontrolled Keywords: text mining; analisis sentiment; naïve bayes; twitter; wisata;
Subjects: Data Processing, Computer Science
Operations, Archieves, Information Centers > Classification of Specific Subject
Divisions: Fakultas Sains dan Teknologi > Program Studi Teknik Informatika
Depositing User: Aprilia Rahmawati
Date Deposited: 22 Sep 2022 07:22
Last Modified: 22 Sep 2022 07:22
URI: https://digilib.uinsgd.ac.id/id/eprint/57761

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