(KOMPARASI SUPPORT VECTOR MACHINE DAN NAÏVE BAYES PADA ANALISIS SENTIMEN APLIKASI PINJAMAN ONLINE)

SIMANJUNTAK, YOLANDA PRETTY (2023) (KOMPARASI SUPPORT VECTOR MACHINE DAN NAÏVE BAYES PADA ANALISIS SENTIMEN APLIKASI PINJAMAN ONLINE). S1 thesis, Universitas Mercu Buana - Menteng.

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Abstract

Aplikasi Pinjaman Online adalah platform digital untuk mengajukan pinjaman menggunakan perangkat mobile atau komputer dari mana saja, tanpa perlu datang ke bank atau lembaga keuangan secara fisik. Penelitian ini bertujuan untuk membandingkan performa SVM dan Naïve Bayes dalam analisis sentimen pada aplikasi pinjaman online dengan data sebanyak 2500 ulasan. Hasil penelitian menunjukkan bahwa tingkat akurasi keduanya sama pada evaluasi kinerja metode confusion matrix yaitu 0.73, tetapi jika dilihat dari nilai AUC algoritma Naïve Bayes lebih baik daripada SVM. Artinya, Naïve Bayes lebih baik memprediksi sentimen dibanding SVM karena nilai akurasi pada Naïve Bayes yaitu 0.72 lebih tinggi daripada SVM dengan 0.69. Online Loan Applications are digital platforms that let you apply for loans using a mobile device or computer from anywhere, without having to visit a bank or financial institution in person. This study aims to compare the performance of SVM and Naïve Bayes in sentiment analysis on online loan apps using 2,500 reviews. The results show that both algorithms achieved the same accuracy of 0.73 based on the confusion matrix evaluation. However, in terms of AUC scores, Naïve Bayes outperformed SVM. This means Naïve Bayes is better at predicting sentiment, with an AUC score of 0.72 compared to SVM’s 0.69

Item Type: Thesis (S1)
NIM/NIDN Creators: 41821010109
Uncontrolled Keywords: Analisis Sentimen, Pinjaman Online, SVM, Naïve Bayes. Sentiment Analysis, Online Loan Applications, SVM, Naïve Bayes
Subjects: 000 Computer Science, Information and General Works/Ilmu Komputer, Informasi, dan Karya Umum > 000. Computer Science, Information and General Works/Ilmu Komputer, Informasi, dan Karya Umum > 003 Systems/Sistem-sistem
Divisions: Fakultas Ilmu Komputer > Sistem Informasi
Depositing User: SUTRA DEWANGGA
Date Deposited: 12 Feb 2025 02:44
Last Modified: 12 Feb 2025 02:44
URI: http://repository.mercubuana.ac.id/id/eprint/94119

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