Perbandingan Algoritma SVM, KNN dan NBC Terhadap Analisis Sentimen Aplikasi Loan Service
Abstract
According to data on the number of credit card users in Indonesia, it has decreased from late 2019 to 2021 oneThe reason is because of the Covid-19 pandemic that hit. Because of this condition, many people are starting to switch to digital credit because they are considered to minimize transmission of viruses and the process is felt to be more efficient than having to use a credit card. This study aims to compare the level of accuracy between the three algorithms, namely the naïve Bayes classifier, k-nearest neighbor and support vector machine for digital credit applications or often called loan services, namely Kredivo. Akulaku, and Indodana in Indonesia by classifying it into two classes namely positive and negative by using the help of the Python programming language to analyze a sentiment by going through text preprocessing and weighting processes said TF-IDF. The results for the accuracy of the Kredivo application using K-NN get a score of 84%, Naïve Bayes 88%, and SVM get 89%. For the application of the K-NN method, it gets 79%, Naïve Bayes 86%, and SVM 87%. As for the indodana application, the K-NN method gets 81%, Naïve Bayes 88%, and SVM 88%. From the results of this accuracy it can be concluded that the Support Vector Machine method has better accuracy results compared to the K-Nearest Neighbor and Naïve Bayes methods.
Keywords
Full Text:
PDFReferences
E. K. Panginan et al., “FENOMENA APLIKASI KREDIT PINJAMAN ONLINE KREDIVO DI INDONESIA,†J. Sisfokom (Sistem Inf. dan Komputer), vol. 9, no. 4, pp. 247–253, 2022, doi: 10.55123/insologi.v1i4.770.
S. Surohman, S. Aji, R. Rousyati, and F. F. Wati, “Analisa Sentimen Terhadap Review Fintech Dengan Metode Naive Bayes Classifier Dan K- Nearest Neighbor,†EVOLUSI J. Sains dan Manaj., vol. 8, no. 1, pp. 93–105, 2020, doi: 10.31294/evolusi.v8i1.7535.
A. Muhammadin and I. A. Sobari, “Analisis Sentimen Pada Ulasan Aplikasi Kredivo Dengan Algoritma Svm Dan Nbc,†Reputasi J. Rekayasa Perangkat Lunak, vol. 2, no. 2, pp. 85–91, 2021, doi: 10.31294/reputasi.v2i2.785.
M. D. Hendriyanto, A. A. Ridha, and U. Enri, “Analisis Sentimen Ulasan Aplikasi Mola Pada Google Play Store Menggunakan Algoritma Support Vector Machine,†INTECOMS J. Inf. Technol. Comput. Sci., vol. 5, no. 1, pp. 1–7, 2022, doi: 10.31539/intecoms.v5i1.3708.
Pristiyono et al., “Sentiment analysis of COVID-19 vaccine in Indonesia using Naïve Bayes Algorithm Sentiment analysis of COVID-19 vaccine in Indonesia using Naïve Bayes Algorithm,†IOP Conf. Ser. Mater. Sci. Eng., 2021, doi: 10.1088/1757-899X/1088/1/012045.
Junadhi, Agustin, M. Rifqi, and M. K. Anam, “Sentiment Analysis of Online Lectures using K-Nearest Neighbors based on Feature Selection,†J. Nas. Pendidik. Tek. Inform., vol. 11, no. 3, pp. 216–225, 2022, doi: 10.23887/janapati.v11i3.51531.
A. M. Rahat, A. Kahir, A. Kaisar, and M. Masum, “Comparison of Naive Bayes and SVM Algorithm based on Sentiment Analysis Using Review Dataset,†2019.
F. M. Delta Maharani, A. Lia Hananto, S. Shofia Hilabi, F. Nur Apriani, A. Hananto, and B. Huda, “Perbandingan Metode Klasifikasi Sentimen Analisis Penggunaan E-Wallet Menggunakan Algoritma Naïve Bayes dan K-Nearest Neighbor,†Metik J., vol. 6, no. 2, pp. 97–103, 2022, doi: 10.47002/metik.v6i2.372.
R. T. Aldisa and P. Maulana, “Analisis Sentimen Opini Masyarakat Terhadap Vaksinasi Booster COVID-19 Dengan Perbandingan Metode Naive Bayes, Decision Tree dan SVM,†Build. Informatics, Technol. Sci., vol. 4, no. 1, pp. 106–109, 2022, doi: 10.47065/bits.v4i1.1581.
A. Tangkelayuk and E. Mailoa, “Klasifikasi Kualitas Air Menggunakan Metode KNN , Naïve Bayes Dan Decision Tree,†vol. 9, no. 2, pp. 1109–1119, 2022.
F. E. Kavabilla, T. Widiharih, and B. Warsito, “Analisis Sentimen Pada Ulasan Aplikasi Investasi Online Ajaib Pada Google Play Menggunakan Metode Support Vector Machine Dan Maximum Entropy,†J. Gaussian, vol. 11, no. 4, pp. 542–553, 2023, doi: 10.14710/j.gauss.11.4.542-553.
Imamah and F. H. Rachman, “Twitter sentiment analysis of Covid-19 using term weighting TF-IDF and logistic regresion,†Proceeding - 6th Inf. Technol. Int. Semin. ITIS 2020, pp. 238–242, 2020, doi: 10.1109/ITIS50118.2020.9320958.
D. Alita and A. Rahman, “Pendeteksian Sarkasme pada Proses Analisis Sentimen Menggunakan Random Forest Classifier,†J. Komputasi, vol. 8, no. 2, pp. 50–58, 2020, doi: 10.23960/komputasi.v8i2.2615.
A. B. Putra and S. Nita, “Perancangan dan Pembangunan Sistem Informasi E-Learning Berbasis Web ( Studi Kasus Pada Madrasah Aliyah Kare Madiun ),†Semin. Nas. Teknol. Inf. dan Komun. 2019, vol. 1, no. 1, pp. 81–85, 2019.
I. Susianti, S. S. Ningsih, M. Al Haris, and T. W. Utami, “Analisis Sentimen Pada Twitter Terkait New Normal Dengan Metode Naïve Bayes Classifier,†Pros. Semin. Edusainstech FMIPA UNIMUS, pp. 354–363, 2020, [Online]. Available: https://prosiding.unimus.ac.id/index.php/edusaintek/article/view/576/578
P. R. Agatha and A. M. Polina, “Analisis Sentimen Terhadap Penggunaan Marketplace di Indonesia Menggunakan Metode Support Vector Machine dengan Seleksi Fitur Chi Square,†SINARINT (Seminar Nas. Ris. dan Inov. Teknol., vol. 1, pp. 314–323, 2022.
M. Ikhsan, N. Adhiatma, and M. Sauri, “APLIKASI PELAPORAN DATA GAINT AND LOSSES SPBU 23.361.03 KOTA JAMBI BERBASIS WEB,†LP2M STMIK NURDIN HAMZAH JAMB, pp. 39–42, 2019.
Ivan Dwi Nugraha and Y. Azhar, “Deteksi Depresi Pengguna Twitter Indonesia Menggunakan LSTM-RNN,†J. Nas. Pendidik. Tek. Inform., vol. 11, no. 3, pp. 320–329, 2022, doi: 10.23887/janapati.v11i3.50674.
A. Mustopa, Hermanto, Anna, E. B. Pratama, A. Hendini, and D. Risdiansyah, “Analysis of user reviews for the pedulilindungi application on google play using the support vector machine and naive bayes algorithm based on particle swarm optimization,†2020 5th Int. Conf. Informatics Comput. ICIC 2020, vol. 2, 2020, doi: 10.1109/ICIC50835.2020.9288655.
A. Sofiyan and A. Azkiya, “PENERAPAN METODE ROUGH SET MENGANALISIS PENYAKIT YANG SERING DIKELUHKAN PASIEN ( STUDI KASUS PUSKESMAS JAYA MUKTI DUMAI ),†Inform. (Jurnal Inform. Manaj. dan Komput., vol. 14, no. 1, pp. 31–40, 2022.
DOI: https://doi.org/10.30865/mib.v7i3.6427
Refbacks
Copyright (c) 2023 JURNAL MEDIA INFORMATIKA BUDIDARMA
This work is licensed under a Creative Commons Attribution 4.0 International License.
JURNAL MEDIA INFORMATIKA BUDIDARMA
Universitas Budi Darma
Secretariat: Sisingamangaraja No. 338 Telp 061-7875998
Email: mib.stmikbd@gmail.com
This work is licensed under a Creative Commons Attribution 4.0 International License.