EVALUASI SENTIMEN PENGGUNA TERHADAP APLIKASI BANK SAQU DENGAN METODE ALGORITMA NAÏVE BAYES
Article Sidebar
Bank Saqu,
Algoritma Naïve Bayes,
Ulasan Pengguna,
Pengalaman Pengguna,
Pra-pemrosesan Teks,
Sentiment Analysis,
Saqu Bank,
Naïve Bayes Algorithm,
User Reviews,
User Experience,
Text Pre-processing.
Main Article Content
Abstract
Studi ini bermaksud untuk mengevaluasi sentimen pengguna mengenai aplikasi Bank Saqu dengan memakai pendekatan komparasi algoritma Naïve Bayes. Evaluasi sentimen merupakan langkah-langkah guna mengenali dan mengklasifikasikan pendapat dan perasaan pengguna ke dalam kategori positif, negatif, atau netral berdasarkan ulasan yang diberikan. Algoritma Naïve Bayes dipilih karena efisiensinya dalam memproses data teks serta kemampuannya memberikan hasil klasifikasi yang kompetitif. Kumpulan data yang dimanfaatkan dalam kajian ini berasal dari tinjauan pemakai aplikasi Bank Saqu yang diperoleh melalui platform digital. Proses penelitian melibatkan tahap praproses data, seperti pembersihan teks, stemming, dan tokenisasi, untuk memastikan kualitas data yang dianalisis. Selanjutnya, dilakukan perbandingan kinerja beberapa bentuk algoritma Naïve Bayes, seperti Multinomial, Bernoulli, dan Gaussian, berdasarkan metrik evaluasi seperti akurasi, presisi, recall, dan F1-score. Temuan riset mengindikasikan bahwa algoritma Naïve Bayes sanggup mengelompokkan opini pemakai dengan tingkat keakuratan yang signifikan. Varian algoritma tertentu menunjukkan kinerja yang lebih unggul dalam menangani dataset yang tidak seimbang. Temuan ini diperkirakan mampu mendukung pembuat perangkat lunak Bank Saqu dalam mengerti pandangan konsumen dan meningkatkan layanan berdasarkan hasil analisis sentimen.
This study aims to evaluate user sentiment regarding the Bank Saqu application using a comparative approach of the Naïve Bayes algorithm. Sentiment evaluation involves steps to recognize and classify users' opinions and feelings into categories such as positive, negative, or neutral based on the reviews provided. The Naïve Bayes algorithm was chosen due to its efficiency in processing text data and its ability to deliver competitive classification results. The data used in this study is sourced from reviews of the Bank Saqu application obtained through digital platforms. The research process includes data preprocessing stages, such as text cleaning, stemming, and tokenization, to ensure the quality of the data being analyzed. A comparison of the performance of several Naïve Bayes algorithm variants, such as Multinomial, Bernoulli, and Gaussian, is then conducted based on evaluation metrics like accuracy, precision, recall, and F1-score. The findings indicate that the Naïve Bayes algorithm can classify user opinions with a significant level of accuracy. Certain algorithm variants show superior performance in handling imbalanced datasets. These findings are expected to assist the developers of the Bank Saqu application in understanding customer views and improving services based on sentiment analysis results.
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This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
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