Analisis Sentimen Terhadap Aplikasi Pospay Menggunakan Algoritma Support Vector Machine dan Naive Bayes

  • Ardini Yuanita Lubis Universitas Logistik dan Bisnis Internasional
  • Muhammad Yusril Helmi Setyawan Teknik Informatika, Universitas Logistik & Bisnis Internasional
Keywords: Sentiment, Pospay, Support Vector Machine, Naive Bayes, PlayStore

Abstract

In the era of business transformation, companies are intensively striving to adapt to the developments in digital technology to enhance operational efficiency and productivity. Pospay application is one form of digital transformation by PT Pos Indonesia, where its presence will impact the improvement of the company's productivity. This research focuses on sentiment analysis of the Pospay application on the PlayStore platform. By applying machine learning methods such as Support Vector Machine (SVM) and Naive Bayes, this study aims to provide solutions to the challenges of data complexity and heterogeneity. The results show a high percentage of accuracy (88% for Naive Bayes and 87% for SVM) in classifying user sentiments with a more dominant negative tendency. These findings provide valuable insights for companies to enhance the quality of their digital services.

Keywords: Sentiment, Pospay, Support Vector Machine, Naive Bayes, PlayStore

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References

M. Danuri, “Development and transformation of digital technology,” Infokam, vol. XV, no. II, pp. 116–123, 2019.

U. Saidata Aesyi and P. W. Cahyo, “Peningkatan Penjualan Produk Berdasarkan Analisis Komentar Pelanggan di Marketplace: Shopee,” J. Sains dan Inform., vol. 9, no. November 2022, pp. 1–8, 2023, doi: 10.34128/jsi.v9i1.539.

E. Laia and M. Yamin, “Penerapan Algoritma Naïve Bayes dalam Menganalisis Sentimen pada Review Pengguna E-Commerce,” Media Online), vol. 4, no. 1, pp. 305–316, 2023, doi: 10.30865/klik.v4i1.1186.

R. R. Salam, M. F. Jamil, Y. Ibrahim, R. Rahmaddeni, S. Soni, and H. Herianto, “Analisis Sentimen Terhadap Bantuan Langsung Tunai (BLT) Bahan Bakar Minyak (BBM) Menggunakan Support Vector Machine,” MALCOM Indones. J. Mach. Learn. Comput. Sci., vol. 3, no. 1, pp. 27–35, 2023, doi: 10.57152/malcom.v3i1.590.

D. Alita and R. A. Shodiqin, “Sentimen Analisis Vaksin Covid-19 Menggunakan Naive Bayes Dan Support Vector Machine,” J. Artif. Intell. Technol. Inf., vol. 1, no. 1, pp. 1–12, 2023, doi: 10.58602/jaiti.v1i1.20.

H. Rachmi, S. Suparni, and A. Al Kaafi, “Analisis Sentimen Sistem Ganjil Genap Kota Bogor,” J. ELTIKOM, vol. 5, no. 2, pp. 92–99, 2021, doi: 10.31961/eltikom.v5i2.429.

V. No, J. Hal, A. K. Neighbor, A. Azis, A. Turmudi, and A. S. Sunge, “Prediksi Penjualan Obat Dan Alat Kesehatan Terlaris Menggunakan,” vol. 6, no. 1, pp. 117–124, 2024.

F. Nelli, Python data analytics: With Pandas, NumPy, and Matplotlib: Second edition. 2018. doi: 10.1007/978-1-4842-3913-1.

A. Triayudi and I. Fitri, “Comparison Of The Feature Selection Algorithm In Educational Data Mining,” Telkomnika (Telecommunication Comput. Electron. Control., vol. 19, no. 6, pp. 1865–1871, 2021, doi: 10.12928/TELKOMNIKA.v19i6.21594.

J. K. Alwan, D. S. Jaafar, and I. R. Ali, “Diabetes diagnosis system using modified Naive Bayes classifier,” Indones. J. Electr. Eng. Comput. Sci., vol. 28, no. 3, pp. 1766–1774, 2022, doi: 10.11591/ijeecs.v28.i3.pp1766-1774.

I. Cholissodin and A. A. Soebroto, “AI , MACHINE LEARNING & DEEP LEARNING ( Teori & Implementasi ),” no. July 2019, 2021.

M. G. Hussain, B. Sultana, M. Rahman, and M. R. Hasan, “Comparison analysis of Bangla news articles classification using support vector machine and logistic regression,” Telkomnika (Telecommunication Comput. Electron. Control., vol. 21, no. 3, pp. 584–591, 2023, doi: 10.12928/TELKOMNIKA.v21i3.23416.

A. Nugroho and Y. Religia, “Analisis Optimasi Algoritma Klasifikasi Naive Bayes menggunakan Genetic Algorithm dan Bagging,” J. RESTI (Rekayasa Sist. dan Teknol. Informasi), vol. 5, no. 3, pp. 504–510, 2021, doi: 10.29207/resti.v5i3.3067.

N. Khalid, S. Abdul-Rahman, W. Wibowo, N. A. S. Abdullah, and S. Mutalib, “Leveraging social media data using latent dirichlet allocation and naïve bayes for mental health sentiment analytics on Covid-19 pandemic,” Int. J. Adv. Intell. Informatics, vol. 9, no. 3, pp. 457–471, 2023, doi: 10.26555/ijain.v9i3.1367.

M. A. Saddam, E. Kurniawan D, and I. Indra, “Analisis Sentimen Fenomena PHK Massal Menggunakan Naive Bayes dan Support Vector Machine,” J. Inform. J. Pengemb. IT, vol. 8, no. 3, pp. 226–233, 2023, doi: 10.30591/jpit.v8i3.4884.

J. Florensius Sianipar, Y. R. Ramadhan, and I. Jaelani, “Analisis Sentimen Pembangunan Kereta Cepat Jakarta-Bandung di Media Sosial Twitter Menggunakan Metode Naive Bayes,” Media Online), vol. 4, no. 1, pp. 360–367, 2023, doi: 10.30865/klik.v4i1.1033.

Rina Noviana and Isram Rasal, “Penerapan Algoritma Naive Bayes Dan Svm Untuk Analisis Sentimen Boy Band Bts Pada Media Sosial Twitter,” J. Tek. dan Sci., vol. 2, no. 2, pp. 51–60, 2023, doi: 10.56127/jts.v2i2.791.

N. C. Gosari and R. Rismayani, “Penerapan Data Mining Dalam Mengelompokkan Kunjungan Wisatawan Mancanegara Di Prov. Sulawesi Selatan Dengan K-Means Dan SVM,” J. Inform. J. Pengemb. IT, vol. 8, no. 3, pp. 174–180, 2023, doi: 10.30591/jpit.v8i3.4554.

M. P. Pulungan, A. Purnomo, and A. Kurniasih, “Penerapan SMOTE untuk Mengatasi Imbalance Class dalam Klasifikasi Kepribadian MBTI Menggunakan Naive Bayes Classifier,” J. Teknol. Inf. dan Ilmu Komput., vol. 10, no. 7, pp. 1493–1502, 2023, doi: 10.25126/jtiik.1077989.

N. V. Chawla, K. W. Bowyer, L. O. Hall, and W. P. Kegelmeyer, “SMOTE: Synthetic minority over-sampling technique,” J. Artif. Intell. Res., vol. 16, no. June 2002, pp. 321–357, 2002, doi: 10.1613/jair.953.

M. Y. Pusadan, A. Ghifari, and Y. Anshori, “Implementasi Data Mining untuk Prediksi Status Proses Persalinan pada Ibu Hamil Menggunakan Algoritma Naive Bayes,” Technomedia J., vol. 8, no. 1 Juni, pp. 137–153, 2023, doi: 10.33050/tmj.v8i1.1980.

A. Adriyendi and Y. Melia, “Klasifikasi Menggunakan Naïve Bayes Dan K-Nearest Neighbor Pada Manajemen Layanan Teknologi Informasi,” J. Teknol. Dan Sist. Inf. Bisnis, vol. 2, no. 2, pp. 99–107, 2020, doi: 10.47233/jteksis.v2i2.121.

Published
2024-07-08
How to Cite
Lubis, A., & Setyawan, M. Y. H. (2024). Analisis Sentimen Terhadap Aplikasi Pospay Menggunakan Algoritma Support Vector Machine dan Naive Bayes. Jurnal Teknologi Dan Sistem Informasi Bisnis, 6(3), 514-521. https://doi.org/10.47233/jteksis.v6i3.1310
Section
Articles