SAHRUL, MUHAMMAD (2025) SENTIMEN ANALISIS PADA ULASAN APLIKASI INDODANA DI GOOGLE PLAY STORE MENGGUNAKAN ALGORITMA LOGISTIC REGRESSION, NAIVE BAYES DAN SUPPORT VECTOR MACHINE (SVM). S1 thesis, Universitas Mercu Buana Jakarta.
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Abstract
This study aims to evaluate user sentiment based on reviews of the Indodana application on the Google Play Store, utilizing Logistic Regression, Naive Bayes, and Support Vector Machine (SVM) algorithms. The primary goal of this research is to identify and classify reviews as positive or negative, as well as to assess the effectiveness of sentiment analysis on Paylater and Online Loan products to determine user interest in the services offered. Additionally, this study seeks to compare the accuracy and performance of each algorithm to determine the most effective method for sentiment analysis. After implementing the Naive Bayes algorithm, the results yielded an accuracy of 80.91%, precision of 80.69%, recall of 80.36%, and an F1-score of 80.54%. Meanwhile, the Logistic Regression algorithm achieved an accuracy of 92.01%, precision of 92.12%, recall of 91.87%, and an F1-score of 91.96%. On the other hand, the SVM algorithm recorded an accuracy of 92.63%, precision of 92.17%, recall of 91.98%, and an F1-score of 92.06%. These results indicate that, in this study, the SVM algorithm outperformed both Logistic Regression and Naive Bayes. It is expected that the findings of this study can provide deeper insights and contribute to the development of the Indodana application as well as similar applications, by helping companies better understand user feedback. Thus, this research may assist in formulating more targeted marketing strategies and improving product and service quality. Furthermore, this study is anticipated to serve as a reference for future research in the field of sentiment analysis on Paylater and Online Loan services, thereby adding value to companies operating in the digital financial sector. Keywords: Logistic Regression, NaÔve Bayes, Support Vector Machine, Online Loan, Sentiment Analysis Penelitian ini bertujuan mengevaluasi sentimen pengguna berdasarkan ulasan aplikasi Indodana di Play Store, menggunakan algoritma Logistic Regression, Naive Bayes, dan Support Vector Machine (SVM). Tujuan utama studi ini adalah mengidentifikasi dan mengklasifikasikan ulasan sentimen positif atau negatif, serta mengevaluasi efektivitas sentimen analisis pada produk Paylater dan Pinjaman Online (Pinjol) untuk melihat potensi ketertarikan pengguna terhadap layanan yang ditawarkan. Selain itu, penelitian ini bertujuan untuk membandingkan keakuratan dan performa masing-masing algoritma, guna menentukan algoritma yang paling efektif dalam menganalisis sentimen. Setelah menerapkan algoritma Logistic Regression menghasilkan akurasi 92,01%, presisi 92,12%, recall 91,87%, dan f1- score 91,96%. Sementara itu, algoritma Naive Bayes, diperoleh hasil dengan hasil akurasi 80,91% , presisi 80,69%, recall 80,36% , dan f1-score 80,54%.Di sisi lain, algoritma SVM mencatatkan nilai akurasi 92,63%, presisi 92,17%, recall 91,98%, dan f1-score 92,06%. Pernyataan ini menunjukkan bahwa dalam penelitian ini, algoritma SVM memiliki performa yang lebih baik dibandingkan Logistic Regression, dan Naive Bayes. Diharapkan hasil penelitian ini dapat memberikan pemahaman yang lebih mendalam dan berguna untuk pengembangan aplikasi Indodana serta aplikasi-aplikasi serupa, dengan membantu perusahaan memahami umpan balik pengguna secara lebih mendetail. Dengan demikian, penelitian ini dapat berkontribusi pada perumusan strategi pemasaran yang lebih tepat sasaran serta peningkatan kualitas produk dan layanan yang lebih baik. Penelitian ini juga diharapkan dapat menjadi referensi untuk penelitian selanjutnya di bidang sentimen analisis pada Paylater dan Pinjaman Online, sehingga dapat memberikan nilai tambah bagi perusahaan-perusahaan yang bergerak di sektor keuangan digital. Kata kunci: Logistic Regression, NaÔve Bayes, Support Vector Machine, Pinjaman Online, Sentiment Analisis
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