Penerapan Algoritma Naïve Bayes dalam Menganalisis Sentimen pada Review Pengguna E-Commerce
DOI:
https://doi.org/10.30865/klik.v4i1.1186Keywords:
Sentiment Analysis; Reviews; Shopee; Naïve Bayes; SalesAbstract
Customer satisfaction is an important thing that is the company's goal. Whether you realize it or not, customer opinions written on social media, a little or a lot, will have an impact on potential customers. In its development, sentiment is increasingly easy to find in various online media, one of which is shopee. Product reviews are a very important source of information related to product quality and are very influential, both for consumers and producers. With a large amount of data for each product in the Shopee store, analyzing and concluding product review information will definitely take a lot of time if done manually. To overcome this, a sentiment analysis system is needed that can automatically extract important information that can objectively determine product quality and handle large textual information. The sentiment analysis system consists of several stages, namely crawling, pre-processing, word weighting, and sentiment classification. By applying the Naïve Bayes algorithm through the selection of range and frequency features, accuracy, accuracy and recall results will be obtained using the Confusion Matrix test. The API is used to collect data and perform simulations using data reviews on the Shopee website to generate positive and negative sentiments. The test will display the results of the percentile values for accuracy and retrieval. From the test results obtained results of accuracy of 99.5%, precision of 99.49%, recall of 100%. Thus it can be concluded that the classification method of the Naïve Bayes algorithm is quite relevant even though the accuracy is not yet 100%. The results of this study can provide solutions for Shopee stores to improve the quality of their sales.
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References
Agustina C.A, N et al, “Implementasi Algoritma Naive Bayes untuk Analisis Sentimen Ulasan Shopee pada Google Play Store.” MALCOM, 2022.
Amrullah, A. Z., Sofyan Anas, A., and Hidayat, M. A. J., "Analisis Sentimen Movie Review Menggunakan Naive Bayes Classifier Dengan Seleksi Fitur Chi Square," BITe, 2020.
Asri Amaliza Fathia Matusea, I. A. S., "RANCANG BANGUN APLIKASI PENDAFTARAN PASIEN ONLINE DAN PEMERIKSAAN DOKTER DI KLINIK PENGOBATAN BERBASIS WEB," Rekayasa Informasi, 2021.
A.R. Isnain, A. Sihabuddin, and Y. Suyanto, “Bidirectional Long Short Term Memory Method and Word2vec Extraction Approach for Hate Speech Detection,” IJCCS, 2020.
A. Surahman, A. F. O. P. Pasaribu, and D. Darwis, “Ekstraksi Data Produk e-Marketplace Sebagai Strategi Pengolahan Segmentasi Pasar Menggunakan Web Crawler,” Sist. J. Sist. Inf., 2020.
A. Surahman, “PENGEMBANGAN MARKET SEGMENTASI UNTUK MENCAPAI KEUNGGULAN BERSAING PADA E-MARKETPLACE,” J. Komput. dan Inform., 2020.
A. Rahman, E. Utami, and S. Sudarmawan, “Sentimen Analisis Terhadap Aplikasi pada Google Playstore Menggunakan Algoritma Naïve Bayes dan Algoritma Genetika,” J. Komtika (Komputasi dan Inform., vol. 5, no. 1, pp. 60–71, 2021, doi: 10.31603/komtika.v5i1.5188.
Azzahra, Sitti Aliyah, & Arief Wibowo. “Analisis Sentimen Multi-Aspek Berbasis Konversi Ikon Emosi Dengan Algoritme Naïve Bayes Untuk Ulasan Wisata Kuliner Pada Web Tripadvisor.” Jurnal Teknologi Informasi dan Ilmu Komputer 7(4): 737. 2020.
Darwis, D., Siskawati, N., & Abidin, Z., "Penerapan Algoritma Naive Bayes Untuk Analisis Sentimen Review Data Twitter Bmkg Nasional," J. Tekno Kompak, 2021.
D. Alita, Y. Fernando, and H. Sulistiani, “Implementasi Algoritma Multiclass SVM pada Opini Publik Berbahasa Indonesia di Twitter,” J. Tekno Kompak, 2020.
D. Musfiroh et al., “Analisis Sentimen terhadap Perkuliahan Daring di Indonesia dari Twitter Dataset Menggunakan InSet Lexicon,” MALCOM Indones. J. Mach. Learn. Comput. Sci., vol. 1, no. 1, pp. 24–33, 2021.
D. Pratmanto, R. Rousyati, F. F. Wati, A. E. Widodo, S. Suleman, and R. Wijianto, “App Review Sentiment Analysis Shopee Application in Google Play Store Using Naive Bayes Algorithm,” J. Phys. Conf. Ser., vol. 1641, no. 1, 2020, doi: 10.1088/1742-6596/1641/1/012043.
Giovani, A. P., Ardiansyah, Haryanti, T., Kurniawati, L., & Gata, W. (2020). “Analisis Sentimen Aplikasi Ruang Guru Di Twitter Menggunakan Algoritma Klasifikasi.” Teknoinfo, 2020.
Gunawan Sudarsono et al, “Analisis Data Mining Data Netflix Menggunakan Aplikasi Rapid Miner.” Journal of Business and Audit Information Systems, 2021.
Kosasih, R., & Alberto, A. “Analisis Sentimen Produk Permainan Menggunakan Metode TF-IDF Dan Algoritma K-Nearest Neighbor.” InfoTekJar, 2021.
Masripah, Siti, & Lila Dini Utami. “Algoritma Klasifikasi Naïve Bayes Untuk Analisa Sentimen Aplikasi Shopee.” Swabumi 8(2): 114–17, 2020.
Muktafin, Elik Hari, Kusrini Kusrini, & Emha Taufiq Luthfi. “Analisis Sentimen Pada Ulasan Pembelian Produk Di Marketplace Shopee Menggunakan Pendekatan Natural Language Processing.” Jurnal Eksplora Informatika 10(1): 32–42, 2020.
N. Herlinawati, Y. Yuliani, S. Faizah, W. Gata, and S. Samudi, “Analisis Sentimen Zoom Cloud Meetings di Play Store Menggunakan Naïve Bayes dan Support Vector Machine,” CESS (Journal Comput. Eng. Syst. Sci., vol. 5, no. 2, p. 293, 2020, doi: 10.24114/cess.v5i2.18186.
Sari, R, “Analisis Sentimen Pada Review Objek Wisata Dunia Fantasi menggunakan Algoritma K-Nearest Neighbor (K-NN).” Evolusi, 2020.
S. T. Rizaldi and M. Mustakim, “Perbandingan Teknik Pembagian Data untuk Klasifikasi Sarana Akses Air pada Algoritma K-Nearest Neighbor dan Naïve Bayes Classifier,” Semin. Nas. Teknol. Informasi, Komun. dan Ind. 12, pp. 130–137, 2020.
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